Measuring and Comparing Volume Flexibility of Small and Large Firms
A dissertation submitted to the
Division of Research and Advanced Studies Of the University of Cincinnati
in partial fulfillment of the requirements for the degree of
DOCTORATE OF PHILOSOPHY (Ph.D.)
In the Department of Quantitative Analysis and Operations Management
2000
by
Eric P. Jack
B.S. Georgia Institute of Technology, 1981
M.B.A., Wright State University, 1990
Committee Chair: Amitabh S. Raturi, Ph.D.
Measuring and Comparing Volume Flexibility of Small and Large Firms
Eric P. Jack
(ABSTRACT)
This study defines Volume Flexibility as: “the ability to profitably increase or decrease aggregate production (output) in response to changes in customer demand.” We use a triangulated approach to measure and relate volume flexibility to firm performance. Part 1 uses secondary data to measure volume flexibility. Other researchers use variability in sales to measure volume flexibility and conclude that small firms are more volume flexible than large firms are. But, variability in sales essentially measures diversity in the environment, and therefore, it may not be a valid measure of volume flexibility. Our measures consider the combined impact of the firm’s technology and environmental diversity by incorporating process properties such as inventory levels and costs incurred in meeting sales variation. Using 20 years (1979-1998) of Compustat data on 550 firms in the capital goods industries (SICs 3510-3590), we identify key sources of volume flexibility that give competitive advantages to small firms. But, when we simultaneously account for environmental uncertainty, production technology, and performance, we find that large firms are more volume flexible than small firms are. We also revalidate these findings with a second data set representing 20 years (1979-1998) of data on 2,100 firms in 93 industries. In part 2, we conduct case studies of three small firms in the capital goods industries. We document and assess the drivers and sources of volume flexibility. Our key findings identify drivers of volume flexibility in two categories: external market forces and internal strategic choices. We also identify key sources of volume flexibility and categorize them into a taxonomy of short-term and long-term sources as well as internal and external sources of volume flexibility. Finally, in Part 3, we conduct a field survey of 750 APICS managers to understand the leverage that volume flexibility provides across small and large firms. Our results validate that the short-term and long-term sources have a positive impact on a firm’s volume flexibility. In addition, the results show that volume flexibility has a positive impact on delivery performance and financial performance.
Key words: Operations strategy, manufacturing flexibility, volume flexibility, empirical research
DEDICATION
This dissertation is dedicated in memory of Mr. Paul Walker who was one of my grade
school teachers at the Saint Thomas Boys Elementary School in Port-of-Spain, Trinidad,
West Indies. Mr. Walker was a disciplined and dedicated teacher who took a keen interest in my early education. When I think of being a university Professor, I am motivated by the caring attitude and the high teaching standards that he exemplified. His enthusiasm for life and learning has been a great source of inspiration for me for all these years. I am sure that if he were alive today, he would be very proud of my accomplishments.
ACKNOWLEDGEMENTS
I would like to thank three groups of people who helped and inspired me
throughout this dissertation process.
First, I wish to thank my wife (Ave) and my immediate family for their patience and support during this long and challenging process. Surely, I could not have done this
without their love and encouragement. Thanks.
Second, I want to thank my committee chair, Dr. Amitabh Raturi, who has
provided support, encouragement, and direction throughout the entire Ph.D. process.
Amit is a very special professor and his guidance and insight proved invaluable in
helping me to navigate this Ph.D. process. I also want to thank my committee members:
Dr. Norman Bruvold, Dr. James Evans, Dr. Martin Levy, and Dr. Karen Machleit. I am
grateful for the time and effort they invested in reviewing this research in progress and
making suggestions to improve the final product. I am also deeply grateful to Gary
Raines for the numerous hours he spent helping me to extract and manipulate the
enormous amount of data from the Standard and Poors’ Compustat business database.
Third, I want to thank several companies who assisted us during the case studies
and field survey. I especially want to thank the C.E.O. of United Air Specialist (Mr.
Peter Nangle) and several senior managers including [Mr. John carter (Operations
Manager); Mr. Keith Horton (V.P. of Sales); Mr. Richard Spence (Human Resources
Manager); Ms Lynn Laard (V.P. of Marketing), Mr. Dave Rasfeld (Comptroller); and Mr.
Kevin Lefur (V.P. of Engineering)]. I also extend a special thanks to one of the owner- managers of Rotex, Inc. (Mr. Gary Armstrong) and two of his senior managers [Mr. Nash
McCauley (V.P. of Marketing) and Mr. Thomas Brewer (Quality/Technical Service
Manager). From the Cables-to-Go company I thank the V.P. of Operations (Mr. Paul
Roy). Finally, I am extremely grateful for the assistance that the greater Cincinnati
Chapter of APICS gave us during the field survey. For APICS’s special support, I thank three individuals: Mr. Arthur Davies (President); Mr Bill Petty (V.P. of Education) and
Mr. Carl Stein (Honorary Member).
Chapter 1. Introduction and Overview...... 23
1.1. The Research Question ...... 27
1.2. Objectives...... 27
1.3. Triangulation ...... 28
1.4. Key Contributions of this Research...... 31
Chapter 2. Literature Review...... 34
2.1. Dimensions of Manufacturing Flexibility...... 36
2.1.1. Volume Flexibility ...... 38
2.1.2. Product-mix Flexibility ...... 39
2.1.3. New Product Flexibility ...... 40
2.2. Measuring Manufacturing Flexibility...... 40
2.2.1. Conceptual Measures ...... 40
2.2.2. Models ...... 41
2.2.3. Empirical Measures...... 43
2.3. Flexibility Tradeoffs ...... 46
2.4. Valuation of Flexibility...... 48
2.5. Drivers and Sources of Volume Flexibility...... 49
2.6. Relevant Arguments from Organizational Theory Literature ...... 51
2.7. Distinguishing between Small and Large Firms ...... 54
2.8. Identifying the Gaps in the Literature...... 56
Chapter 3. Empirical Validation using Secondary Data...... 58
3.1. Objectives...... 58
viii 3.2. The Fiegenbaum and Karnani (1991) Study ...... 59
3.3. Data Set to Revalidate F&K’s Hypotheses ...... 62
3.4. Data Manipulation ...... 67
3.5. Revalidating F&K’s Hypotheses...... 68
3.5.1. Hypothesis, H1:...... 68
3.5.2. Revalidating H2, H3, and H4: ...... 71
3.5.3. Testing H5, H6, and H7...... 75
3.5.4. Summary of the Revalidation ...... 77
3.5.5. Output Fluctuations using Mills and Schumann (1985) Procedure ...... 80
3.6. Process Measures of Volume Flexibility ...... 83
3.6.1. Surrogate measures of Volume Flexibility...... 84
3.6.2. Flexibility Ratios...... 85
3.6.3. The Data Set...... 85
3.6.4. Volume Flexibility Measures...... 86
3.6.4.1. Output Fluctuations and Inventory Buffers ...... 88
3.6.4.2. Output Fluctuations and Cost-Of-Goods-Sold...... 91
3.6.4.3. Output Fluctuations, Inventory buffers, and Cost-of-Goods Sold ....95
3.6.4.4. Output Fluctuations, Profitability, Inventory Buffers, and CGS ...... 98
3.6.4.5. Summary of Results on Process-Based Measures...... 102
3.6.5. Evaluating Process-based Measures with a Larger Sample Size...... 103
3.6.5.1. Data Analysis ...... 105
3.6.5.2. Summary of Results using 90 SICs and 2100 Firms...... 105
Chapter 4. Case Studies...... 110
ix 4.1. Measuring Volume Flexibility within the Firm...... 111
4.2. Case Study Methodology ...... 113
4.3. The Case Study Questionnaire ...... 118
4.4. Company Profiles ...... 119
4.5. Drivers of Volume Flexibility ...... 122
4.5.1. Forecasting Inaccuracy...... 124
4.5.2. Customer Market Segments...... 127
4.5.3. Variability in Order Volume...... 129
4.5.4. Delivery Lead-Time ...... 132
4.5.5. Delivery Reliability (Responsiveness) ...... 134
4.5.6. Product Customization ...... 136
4.5.7. Core Competency...... 138
4.6. Sources of Volume Flexibility...... 141
4.6.1. Manufacturing Capabilities ...... 144
4.6.2. Labor Flexibility ...... 146
4.6.3. Networks and Strategic Alliances...... 149
4.6.4. Short-Term Sources of Volume Flexibility...... 153
4.6.4.1. Inventory and Capacity Buffers ...... 153
4.6.4.2. Labor Flexibility...... 154
4.6.5. Long-Term Sources...... 155
4.6.5.1. Network of plants ...... 155
4.6.5.2. Outsourcing...... 156
4.6.5.3. Increasing Plant Capacity ...... 157
x 4.6.5.4. Workforce Levels ...... 158
4.7. Summary ...... 159
Chapter 5. Field Survey...... 161
5.1. Developing the Constructs ...... 162
5.1.1. Drivers of Volume Flexibility...... 162
5.1.2. Importance of Volume Flexibility...... 164
5.1.3. Volume Flexibility Capability ...... 164
5.1.4. Short-term Sources of Volume Flexibility ...... 165
5.1.5. Long-term Sources of Volume Flexibility...... 166
5.1.6. Performance...... 166
5.1.7. The Field Survey Questionnaire ...... 167
5.2. Framework and Hypotheses ...... 170
5.2.1. Volume Flexibility and Performance ...... 170
5.2.2. Hypotheses on Importance of Volume Flexibility...... 171
5.2.3. Hypotheses on Sources of Volume Flexibility ...... 172
5.2.4. The Hypothesized Model...... 173
5.3. The Pre-Test ...... 174
5.4. The Sample...... 175
5.5. Data Analysis...... 176
5.5.1. Preliminary Data Analysis...... 176
5.5.2. Assessing Unidimensional Measurement...... 179
5.5.2.1. Item Analysis ...... 180
5.5.2.2. Exploratory Factor Analysis (EFA)...... 187
xi 5.5.3. Canonical Correlation Analysis ...... 189
5.5.4. Regression Analysis ...... 192
5.5.5. Analysis of Variance (ANOVA)...... 198
5.5.6. Structural Equation Modeling...... 200
5.5.6.1. The Hypothesized Model...... 202
5.5.6.2. The Measurement Model ...... 204
Confirmatory Factor Analysis ...... 204
Convergent Validity and Item Reliability...... 205
t-Values ...... 206
R2 Values...... 207
Fit Statistics for the Measurement Model ...... 208
Standardized Residuals ...... 211
Measurement Model Q-Plots...... 213
Modification Indices ...... 215
Discriminant Validity...... 216
5.5.6.3. The Structural Model...... 217
Chapter 6. Results and Conclusions...... 222
6.1. Triangulated Objectives ...... 222
6.2. Triangulated Data Sources and Analytical Methods ...... 223
6.3. Results...... 225
6.3.1. Results from the Secondary Data Analysis ...... 225
6.3.2. Results from the Case Studies ...... 230
6.3.3. Results from the Field Survey ...... 232
xii 6.3.4. Triangulated Results...... 234
6.3.4.1. Sources of Volume Flexibility ...... 235
6.3.4.2. Drivers of Volume Flexibility...... 236
6.3.4.3. Volume Flexibility and Performance ...... 237
6.4. Conclusions ...... 240
6.4.1. Output Fluctuations and Volume Flexibility ...... 240
6.4.2. Short-Term Sources of Volume Flexibility...... 242
6.4.3. Long-Term Sources of Volume Flexibility ...... 244
6.4.4. Perceived Value of Volume Flexibility...... 246
6.4.5. Contributions to Operations Management Theory...... 247
6.4.6. Shortcomings and Directions for Future Research ...... 250
Bibliography 252
Appendices 262
Appendix A. Supporting Information for Chapter 3 ...... 262
A1. Regression Diagnostics and Remedial Procedures (Sample #1) ...... 262
A2. List of 2100 Firms and Years of Data in Sample #2...... 268
A3. Regression Results for OF2 using Sample #2...... 291
A4. Regression Results for VF1 using Sample #2...... 293
A5. Regression Results for VF2 using Sample #2...... 295
A6. Regression Results for VF3 using Sample #2...... 297
A7. Regression Results for VF4 using Sample #2...... 299
Appendix B. Supporting Information for Chapter 4 ...... 302
B.1. Case Study Questionnaire...... 302
xiii B.1.1 Demographic Data ...... 302
B.1.2 Assessing your Competitive Environment ...... 302
B.1.3 Forecasting your demand...... 303
B.1.4 Measuring your delivery performance ...... 303
B.1.5 Measuring your overall performance ...... 304
B.1.6 Marketing Strategy...... 304
B.1.7 Business strategy decision-making ...... 305
B.1.8 Manufacturing Strategy...... 306
B.1.9 Manufacturing Planning and Control...... 307
B.1.10 Workforce development and usage...... 308
B.1.11 Equipment and technology ...... 308
B.1.12 Networks and strategic alliances...... 309
B.1.13 Short-term Sources of Volume Flexibility ...... 310
B.1.14 Mid-term Sources of Volume Flexibility ...... 311
B.1.15 Long-term Sources of Volume Flexibility...... 312
B.2. Case Study Narrative: Company A...... 313
B.2.1 Interview Data:...... 313
B.2.2 Summary of Findings ...... 313
B.2.3 Company Overview...... 313
B.2.4 Business Environment...... 314
B.2.5 Demand Forecasting...... 315
B.2.6 Delivery Performance...... 315
B.2.7 Overall Performance ...... 316
xiv B.2.8 Business Strategy Decision-making...... 316
B.2.9 Manufacturing Strategy...... 317
B.2.10 Production Control...... 317
B.2.11 Supplier/Vendor Networks ...... 318
B.2.12 Labor ...... 319
B.2.13 Conclusions...... 320
B.3. Case Narrative: Company B...... 321
B.3.1 Interview Data:...... 321
B.3.2 Summary of Our First Meeting...... 321
B.3.3 The Plant...... 322
B.3.4 Assessing the Competitive Environment...... 323
B.3.5 Demand Forecasting...... 325
B.3.6 Delivery Performance...... 325
B.3.7 Overall Performance ...... 326
B.3.8 Marketing Strategy...... 327
B.3.9 Business Strategy and Decision-Making...... 328
B.3.10 Manufacturing Strategy...... 328
B.3.11 Manufacturing Planning and Control...... 330
B.3.12 Workforce...... 331
B.3.13 Networks and Strategic Alliances...... 332
B.3.14 Short-term Sources of Volume Flexibility ...... 333
B.3.15 Mid-term Sources of Volume Flexibility ...... 334
B.3.16 Long-term Sources of Volume Flexibility...... 335
xv B.4. Case Narrative: Company C...... 337
B.4.1 Interview Data:...... 337
B.4.2 The Plant...... 337
B.4.3 Volume Flexibility Benchmark Data ...... 338
B.4.4 Assessing the Competitive Environment...... 339
B.4.5 Demand Forecasting...... 340
B.4.6 Delivery Performance...... 342
B.4.7 Overall Performance ...... 343
B.4.8 Marketing Strategy...... 344
B.4.9 Business Strategy and Decision-Making...... 344
B.4.10 Manufacturing Strategy...... 345
B.4.11 Manufacturing Planning and Control...... 347
B.4.12 Workforce...... 348
B.4.13 Networks and Strategic Alliances...... 349
B.4.14 Short-term Sources of Volume Flexibility ...... 350
B.4.15 Mid-term Sources of Volume Flexibility ...... 351
B.4.16 Long-term Sources of Volume Flexibility...... 351
C. Supporting Information for Chapter 5...... 352
C.1. Correlation Matrix...... 352
C.2. Factor Analysis Results...... 353
C.3. ANOVA Results ...... 355
C.4. Lisrel (version 8.3) Output for SEM Analysis in Section 5.5.6...... 364
C.4.1 Standardized Solution and T-values for Alternative Model #2 ...... 382
xvi C.4.2 Fit Statistics for Alternative Structural Equation Model #2...... 383
xvii List of Figures
Figure 1.1 Total Costs vs. Volume Levels ...... 24
Figure 1.2 Triangulation Methodology ...... 29
Figure 1.3 Research Plan ...... 30
Figure 2.1 Framework on Manufacturing Flexibility...... 35
Figure 2.2 Flexibility Tradeoffs...... 47
Figure 3.1 F&K’s Hypotheses ...... 60
Figure 3.2 Summary of Our Revalidation of F&K’s Hypotheses...... 78
Figure 3.3 Summary of Process-Based Measures using 550 Firms in 28 SICs...... 103
Figure 3.4 Summary of Process-based measures using 2100 Firms in 90 SICs...... 107
Figure 4.1 Case Study Research Design...... 117
Figure 5.1 Perceived Value of Volume Flexibility ...... 173
Figure 5.2 The Hypothesized Model...... 202
Figure 5.3 Measurement Model showing Standardized Solution...... 205
Figure 5.4 Measurement Model showing t-Values ...... 206
Figure 5.5 Q-Plot of Standardized Residuals...... 214
Figure 5.6 The Structural Model...... 217
Figure 5.7 Structural Model showing the Standardized Solution ...... 219
Figure 5.8 Structural Model showing t-Values...... 219
Figure 6.1 Triangulated Objectives...... 223
Figure 6.2 Triangulation of Data Sources and Analytical Methods...... 225
Figure 6.3 SEM Results showing the Standardized Solution ...... 232
Figure 6.4 Structural Model showing t-Values...... 232
xviii Figure 6.5 Triangulated hypotheses and Propositions...... 234
Figure 6.6 Sources of Volume Flexibility ...... 236
Figure 6.7 Drivers of Volume Flexibility...... 237
Figure 6.8 Volume Flexibility and Performance ...... 239
Figure 6.9 Output Fluctuations, Volume Flexibility and Firm Size ...... 242
Figure 6.10 Short-Term Sources v.s. Firm Size...... 244
Figure 6.11 Long-Term Sources v.s. Firm Size...... 245
Figure 6.12 Volume Flexibility Linkages...... 249
xix List of Tables
Table 2.1 Sample of Papers on Manufacturing Flexibility...... 36
Table 3.1 List of F&K's Hypotheses ...... 61
Table 3.2 SIC Breakdown, (Table 1 of 3) ...... 63
Table 3.3 SIC Breakdown (Table 2 of 3) ...... 64
Table 3.4 SIC Breakdown (Table 3 of 3) ...... 65
Table 3.5 Distribution of Firms by Avg. Annual Sales ($M)...... 66
Table 3.6 Distribution of Firms by Number of Employees...... 66
Table 3.7 Compustat Data Manipulation...... 67
Table 3.8 Revalidation of H1...... 69
Table 3.9 Wilcoxon Signed-Rank Test on bj ...... 70
Table 3.10 Revalidating H2, H3, and H4 (using ROS)...... 72
Table 3.11 Wilcoxon Signed-Rank Test for H2, H3 and H4...... 73
Table 3.12 Revalidating H5, H6, and H7 ...... 76
Table 3.13 Revalidation of F&K's Hypotheses...... 77
Table 3.14 Regression Analysis Results for Output Fluctuation...... 82
Table 3.15 Measuring Volume Flexibility with one Small and one Large Firm...... 87
Table 3.16 Regression Results for VF1 ...... 90
Table 3.17 Regression Results for VF2 ...... 94
Table 3.18 Regression Results for VF3 ...... 97
Table 3.19 Regression Results for VF4 ...... 101
Table 3.20 Distribution of Firms by Avg. Annual Sales ($M) ...... 104
xx Table 3.21 Distribution of Firms by Number of Employees...... 104
Table 4.1 Defining Range, Cost, and Time for Volume Flexibility ...... 112
Table 4.2 Case Study Methodology ...... 115
Table 4.3 Company Profiles ...... 119
Table 4.4 Drivers of Volume Flexibility ...... 124
Table 4.5 Sources of Volume Flexibility (1 of 2) ...... 142
Table 4.6 Sources of Volume Flexibility (2 of 2) ...... 143
Table 4.7 Drivers of Volume Flexibility ...... 159
Table 4.8 Sources of Volume Flexibility...... 160
Table 5.1 Drivers of Volume Flexibility ...... 163
Table 5.2 Field Survey Questionnaire...... 169
Table 5.3 Breakdown of the Survey Data...... 177
Table 5.4 Industry Breakdown (Table 1 of 2)...... 178
Table 5.5 Industry Breakdown (Table 2 of 2)...... 179
Table 5.6 Item Analysis of Performance...... 181
Table 5.7 Item Analysis of Importance of Volume Flexibility...... 182
Table 5.8 Item Analysis of Volume Flexibility Capability ...... 183
Table 5.9 Item Analysis of Short-Term Sources...... 184
Table 5.10 Item Analysis of Long-Term Sources...... 185
Table 5.11 Item Analysis of Drivers of Volume Flexibility...... 186
Table 5.12 Factor Analysis Summary ...... 188
Table 5.13 Summary of Canonical Correlation Analysis...... 190
Table 5.14 Regression Analysis Results for H2 ...... 193
xxi Table 5.15 Regression Analysis Results for H4 and H5 ...... 195
Table 5.16 Regression Analysis Results for H6 and H9 ...... 197
Table 5.17 ANOVA Results ...... 199
Table 5.18 List of SEM variables and parameters ...... 201
Table 5.19 List of Variables in the Lisrel Model...... 203
Table 5.20 Fit Statistics for the Measurement Model ...... 210
Table 5.21 Standardized Residuals ...... 212
Table 5.22 Modification Indices ...... 215
Table 5.23 The Phi Matrix...... 216
Table 5.24 Discriminant Validity Calculations ...... 216
Table 5.25 Fit Statistics for Structural Model...... 220
Table 6.1 Revalidation of F&K's Hypotheses ...... 226
Table 6.2 Measures of Volume Flexibility Using Sample N1 ...... 227
Table 6.3 Measures of Volume Flexibility Using Sample N2 ...... 229
Table 6.4 Drivers of Volume Flexibility ...... 230
Table 6.5 Sources of Volume Flexibility...... 231
Table 6.6 Field Survey Results ...... 233
xxii Chapter 1. Introduction and Overview
Four guiding principles drive the development of this dissertation. First, flexibility is a major source of competitive advantage for many firms. Second, measuring and evaluating the advantage derived from volume flexibility is elusive. Third, it is often argued that size has important consequences for flexibility--small firms are generally regarded as more flexible. Finally, empirical research in operations management is scant.
Aggarwal (1997) defines manufacturing flexibility as “the ability to meet the needs of the market without too much cost, time, effort, performance, or organizational disruption.” Manufacturing flexibility has emerged as a key source of competitive advantage for firms in an era of global competition that is characterized by rapidly changing technology and shorter product life cycles. The literature suggests that the seventies was the decade of “Productivity,” the eighties focused on “Total Quality
Management,” and the nineties and subsequent belong to “Flexibility” (Evans, 1996).
The competitive potential of flexibility has also been documented in a study of manufacturing firms in Japan, North America and Europe (DeMeyer et al, 1989). In a more recent study, other researchers have shown that flexibility is a key dimension of the performance of a company’s supply chain (Vickery et al, 1999). Companies are motivated to be flexible because the global marketplace is more competitive and customers are demanding product variety along with, quality, competitive prices, and faster delivery.
Sethi and Sethi, (1990) show that manufacturing flexibility consists of 11 different dimensions. However, in this research, we focus exclusively on only one dimension: volume flexibility. Sethi and Sethi (1990) defines volume flexibility of a
23 manufacturing system as “its ability to be operated profitably at different output levels.”
Some of the strategies deployed for increasing volume flexibility include using overtime and temporary workers, cross training workers, developing complementary product portfolios, creating and maintaining slack resources, improving forecasting and planning systems as well as leveraging the firm's ability to negotiate on volume with suppliers and customers. These sources of volume flexibility are essentially options that small and large firms use to gain competitive advantage in the market place. By definition, volume flexibility allows a firm to respond to varying volume needs of the customer with the lowest average cost. Thus, one measure of volume flexibility is the change in cost when volume is changed. One firm is more flexible than if this change is small. For example, consider the simple scenario in Figure 1.1. Firm B experiences a smaller variation in total costs than Firm A does over the volume domain. Therefore, for the same level of environmental uncertainty, Firm B, demonstrates more volume flexibility.
Total Costs at Varying Volumes Levels
270.0
250.0
230.0
Total Costs 210.0
190.0 50 75 100 125 150 Firm A 250.0 208.3 190.0 200.0 216.7 Firm B 230.0 209.2 205.0 207.5 213.3 Volume Levels
Figure 1.1 Total Costs vs. Volume Levels
24
Notwithstanding this simple example, in this research, we argue that existing empirical studies of volume flexibility have not adequately captured how large and small firms use their resources to respond to environmental uncertainty. For example, when we survey the operations management literature in Chapter 2 of this research, our analysis suggests that there are relatively few empirical studies on manufacturing flexibility, in general, and that this relative shortage is even more pronounced when we consider the specific dimension of volume flexibility. Therefore, we begin this research by focusing on two basic questions: (1) what is the strategic value of volume flexibility? and (2) does firm size play a dominant mediating role in the deployment of a volume flexibility strategy?
The strategic value of volume flexibility to firms is well documented [(Slack,
1983), (Hayes and Wheelwright, 1984), (Sethi and Sethi, 1990), (Gupta and Somers,
1992), (Gerwin, 1993), (Upton, 1994), (Ettlie and Penner-Hahn, 1994), (Koste and
Manoj, 1999)]. Volume flexibility enables a firm to effectively increase or decrease aggregate production level in response to customer demand (Hayes and Wheelwright,
1984). Volume flexibility also enables a firm to maintain a high level of delivery reliability by preventing out -of-stock conditions for products that are suddenly in high demand. Conversely, in periods of slow demand, a volume flexible firm is not saddled with excess inventories and\or surplus capacity. Firms may selectively choose from a variety of resource options (internal buffers or external outsourcing arrangements) to become volume flexible. However, at the core of this volume flexibility capability is the need for a firm to improve coordination at each echelon of its supply chain especially in the face of increasing demand (Vickery et al, 1999). Furthermore, volume flexibility has
25 been shown as positively related to a firm’s performance. For example, Vickery et al
(1999) study the furniture industry and they show that volume flexibility is a source of competitive advantage for firms operating in highly cyclical and/or seasonal markets.
Despite the competitive advantages that volume flexibility inherently brings to a firm, there are still many questions about whether or not firm size is an important determinant of volume flexibility. Many studies suggest that small firms play a very important role in the US economy [Birch (1988), Keats and Bracker (1988), and Dean et al (1998)]. Birch (1988) suggests that small firms, with fewer than 500 employees, employ almost half of the US workforce. Keats and Bracker (1988) show that small businesses comprise 97% of the enterprises in the US economy and employ 58% of the workforce. Small firms are more pronounced in some sectors of the economy. For example, the 1992 census data suggests that in the machine tool industries (SICs 3541-
3549) approximately 75 percent of these firms employ less than 100 employees. Clearly, the large numbers of small manufacturing firms suggests that this would be a fertile area for research. In this regard, many researchers have investigated the relative competitiveness of small and large firms. Research in economics has generally found a positive and significant association between firm size and profitability (Shepherd, 1972).
This research shows that large firms can have the significant advantages of economies of scale and to some extent economies of scope. On the other hand, empirical research in the field of organizational behavior suggests that small firms may derive competitive advantage from greater interaction of their internal departments, which increases the firm’s overall synergism and responsiveness (Neilson,1974). Dean et al (1998) show that small businesses will be more likely to enter industry environments in which speed,
26
flexibility, and niche targeting are rewarded. Therefore, given that there are different
competitive advantages for large and small firms, there are still many pertinent questions
about how do small and large firms use volume flexibility to sustain their competitive
advantage in this global marketplace.
1.1. The Research Question
This research focuses on the relationships between volume flexibility, firm size,
and the financial performance of the firm. Our specific research question is:
“Are small firms more volume flexible than large firms are; and if so, is volume flexibility a major source of competitive advantage for small firms?”
1.2. Objectives
This research has three main objectives:
1. First, we want to replicate the Fiegenbaum and Karnani (1991) study (henceforth
referred to as F&K and discussed in detail in chapter 3) using a subset of product
industries. In doing so, we have two main goals: (a) we want to see if these
hypotheses can be validated more strongly without the influence of product and
service mixes that may underlie F&K’s evaluation of 3,000 firms and 83 industries;
and (b) we want to extend F&K’s work by arguing that more effective empirical
measures of volume flexibility should also consider the cost structure and the
technology of the firm.
27
2. Second, we want to understand the drivers and sources of volume flexibility and how
they impact a firm’s competitiveness.
3. Third, we want to understand the leverage that volume flexibility provides across
small and large firms.
1.3. Triangulation
While other studies have also attempted to measure volume flexibility from different perspectives [e.g. F&K, Suarez et al (1995 and 1996), and Vickery et al (1999)], we find that measuring and evaluating the competitive advantage derived from volume flexibility remains elusive. Our goal is to make a legitimate value-added contribution to the theory development of volume flexibility. Whetten (1989) argues that while it is difficult to judge such contributions, he suggests “proposed improvements addressing only a single element of an existing theory are seldom judged to be sufficient. Therefore, a general rule of thumb is that critiques should focus on multiple elements of a theory.”
Therefore, in this research, we have elected to use a triangulated research methodology in order to compensate for the shortcomings of any single measurement method.
Consequently, we use three approaches to triangulate our understanding of volume flexibility. First, we use regression analysis as the main statistical technique for conducting the empirical validation of the relationship between volume flexibility, firm size and performance. In this effort, we use a secondary data source (Standard and Poors
Compustat Business Database) by extracting 20 years of financial performance data that the publicly-traded firms report on their SEC 10-K reports. Second, we conduct the case studies using structured interviews with key decision makers in three firms in order to
28 determine the drivers and sources of volume flexibility. We document much of this qualitative data in a systematic format and derive several propositions from it. Third, we use a field survey to test and validate the relative importance of volume flexibility in small and large firms. Our hypotheses are based on the propositions developed from the case studies as well as the regression analyses using secondary data.
Triangulation (shows the relationship between the three research methods)
Field Survey
Theory Building: What?
Empirical Case Study Validation Theory Testing: Who? Where? When? Theory Building: How? Why?
Figure 1.2 Triangulation Methodology
Using this triangulated approach (Figure 1.2), our research makes several valuable contributions to the operations management literature in both theory building and theory testing. Whetten (1989) suggests that in theory building, we attempt to find answers to three questions: What? How? and Why? In this effort, the field survey addresses the
(What?) question and assesses the manager’s view of volume flexibility in response to demand uncertainty. For example, in the field survey a typical question that we focus on 29
is: What is the relationship between volume flexibility and competitiveness? Next, the
case studies were designed to address the questions (How? and Why?) by focusing on
the drivers and sources of volume flexibility within the firms. For example, a typical
question that we focus on is: Why are firms driven to adopt a volume flexibility strategy
and how do they achieve it? Finally, the empirical validation study is designed to provide
answers to the theory testing question (Who?, Where? and When?). For example, a basic
question that we investigate is: are small firms more volume flexible than large firms are? And if so, When? and Where?
Research Plan Chapter 6 Chapter 1 Results and Introduction Chapter 3 Chapter 4 Conclusions Empirical Case Studies Validation
Chapter 2 Chapter 5 Literature Field Survey Review
Research Methods Empirical validation (regression) Case Study (qualitative) Field Survey (multiple)
Figure 1.3 Research Plan
This research is organized in the following manner. In Chapter 2, we present a
framework for measuring volume flexibility and summarize the broad stream of operations management literature on manufacturing flexibility. In Chapter 3, we
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revalidate existing hypotheses by using regression analysis to relate volume flexibility to
firm size and performance by building on the work of Fiegenbaum and Karnani (1991).
We then extend this research stream by developing and testing hypotheses on four new
process-based measures of volume flexibility. In Chapter 4, we use the case study
methodology to develop a deeper understanding of the context and background of the
drivers and sources of volume flexibility. In chapter 5, we use the field survey
methodology and employ multiple analytical techniques to test several hypotheses
generated from the empirical measures and the case study. Finally, in chapter 6 we state
our results and conclusions as well as directions for future research.
1.4. Key Contributions of this Research
From an operations management perspective, there are many ways to address the
relative uses of volume flexibility between small and large firms. Generally, research in
operations management follows three basic streams: conceptual, empirical and modeling.
In this research, we have elected to address this question from an empirical perspective.
Our study adds to the growing but under-represented body of purely empirical research in the operations management literature. For example, Scudder and Hill (1998) show that empirical research in operations management represents only 11 percent of the research published in scholarly operations management journals. This finding represents a small increase from four percent in 1986 to 11 percent in 1995. In addition, a number of recent papers and editorials have highlighted the relative paucity of case and field research in operations management (Meredith,1998). For example, in analyzing the research agenda on manufacturing flexibility, Gerwin (1993) suggests that “research on flexibility needs
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to have both a theoretical and an applied orientation. Special attention must be paid to
the latter type of work as it is less developed than the former. . . theoretical and applied
investigations will continue to be severely hampered until the problems of developing
valid and reliable measures of flexibility and its value are resolved. . . Operationalizing
flexibility is therefore the single most important research priority.”
Consequently, this research focuses specifically on empirical measures of volume
flexibility. This study adds to the under-represented body of empirical studies in the
operations management’s literature stream. Perhaps more importantly, this study helps
us understand how to effectively measure and use volume flexibility as a competitive
weapon. The two new contributions of this work include:
· Refining the theoretical rationale for measuring volume flexibility across
large and small firms.
· Presenting critical findings that give a more accurate interpretation of the
drivers and sources of volume flexibility in small and large firms.
For example, other researchers use variability in sales to measure volume flexibility and conclude that small firms are more volume flexible than large firms are.
But, variability in sales essentially measures diversity in the environment, and therefore, it may not be a valid measure of volume flexibility. In chapter 3, our four new process-
based measures consider the combined impact of the firm’s technology and
environmental diversity by incorporating process properties such as inventory levels and
costs incurred in meeting sales variation. Using 20 years of Compustat data on 550 firms
in 29 capital goods industries, we find that small firms gain competitive advantage by
more judicious use of inventory and better management of costs in response to similar
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levels of environmental uncertainty. However, when we incorporate financial performance directly into our new measures, we find that large firms are more volume
flexible. We conclude that while small firms are more efficient in responding to
environmental uncertainty, large firms have "deeper pockets" to cushion performance
when sales levels vary.
We focus on the drivers and sources of volume flexibility in chapters 4 and 5.
For example, in chapter 4, we use the case study methodology to document the rich
context and background of the drivers and sources of volume flexibility. Our key
findings identify drivers and sources of volume flexibility in two categories: external
market forces and internal strategic choices. We also address the time dependence issues
involved in implementing a volume flexibility strategy and we categorize the sources of
volume flexibility into a taxonomy of short-term and long-term sources. Finally, in
chapter 5, we conduct a field survey of 750 APICS managers and we test several
hypotheses that address the leverage that volume flexibility provides across small and
large firms. Our results validate that the short-term and long-term sources have a positive impact on a firm’s volume flexibility. In addition, these results show that volume flexibility has a positive impact on a firm’s operational performance measures such as delivery performance and financial performance.
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Chapter 2. Literature Review
In this chapter, we analyze the broad stream of research on manufacturing flexibility and focus on the work that has been done to support the concept of output or volume flexibility. First, we present our own framework for analyzing manufacturing flexibility by describing the relationships between the content of the literature and the overall focus or type of research being accomplished. Second, we show how we map some of the key articles in this research stream into our framework and highlight some of the pertinent ideas of this literature stream. Third, we highlight some of the relevant arguments from the Organization Theory literature. Finally, we summarize the gaps in the literature and describe how our research addresses these gaps.
In the operations management arena, we can organize the content of the literature on manufacturing flexibility into four basic streams: dimensions of flexibility, measurement of flexibility, analysis of the flexibility tradeoffs, and the financial valuation of flexibility. Similarly, we can divide the type of research done on flexibility into three basic categories: conceptual, empirical, and modeling perspectives. Figure 2.1 summarizes the relationships between the content and the nature of research on manufacturing flexibility.
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Framework for Analyzing Literature on Manufacturing Flexibility
Dimensions of Flexibility Content Conceptual Measuring Flexibility Empirical Flexibility Tradeoffs Modeling Nature of Valuation of Research Flexibility
Figure 2.1 Framework on Manufacturing Flexibility
We analyzed several articles from the manufacturing flexibility literature and mapped them into our framework as shown in Table 2.1.
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Dimensions Measurement Tradeoffs Valuation
Astley, G. and Brahm, Bernardo, J. and Bhatnagar, R. and Azzone, G. and Bertele, U. R. (1989); De Toni, A. Mohamed, A. (1992); Chandra, P. (1993); (1989). Conceptual and Tonchia, S. Cox, Jr., T. (1989); Correa, H. and Slack, N. (1998); Gupta, Y. and Gerwin, D., (1993); Koste, (1996); Hyer, N. and Goyal, S. (1989); L. and Malhotra, M. Wemmerlov, U. (1984); Sethi, A.K. and Sethi, (1999); Slack, N. (1983); McCutcheon D., Raturi, S.P. (1990); A. and Meredith, J. Shewchuk, J. and (1994); Slack, N. and Moodie, C. (1997); Correa, H. (1992). Upton, D. (1994); Slack, N. (1987);
Abernethy, Margaret and Collins, R. et al, (1998); Bylinsky G. (1983); Empirical Lillis, Anne (1995); Mills, D. and Schumann, Jaikumar. R. (1986); Dixon, J. R. (1992); L. (1985); Swamidass, Ettlie, J.E. and Penner- P. and Suresh, K. (1998); Hahn, J.D. (1994); Fiegenbaum, A. and Karnani, A.(1991); Gupta Y.P. and Somers, T.M.(1992); Kekre, S. and Srinivasan, K.(1990); Noble, M. (1995); Upton, D. (1994, 1995, 1997); Vickery et al (1999) Model Choi, S. and Kim, J. deGroote, Xavier Cohen, M. (1996); Fine, C. (1998); deGroote, (1994a); Kumar, A.; and Freund R. (1990); Xavier (1994b); Boyer, Gurnani, H.; Kekre, S.; Huchzermeier, A. and Kenneth and Leong, and Lafond, N. (1990); Cohen, M. (1996); Kogut, Keong (1996); Jordan, Grubbstrom, R. and B. and Kulatilaka N. William C. and Graves Olhager, J. (1997); (1994); Lederer, P. and S.C. (1995); Brill, P.H. Khouja, M. (1995); Singhal, V. (1988); and Mandelbaum, M. Ramasesh, R. and (1987); Schneeweiss, C. Jayakumar, M. (1991); and Schneider, H. (1999). Tannous, G. (1996); Table 2.1 Sample of Papers on Manufacturing Flexibility
2.1. Dimensions of Manufacturing Flexibility
The conceptual research on manufacturing flexibility plays a key role in developing and advancing new theories to support this concept. In this regard, articles that focus on the dimensions of manufacturing flexibility have provided some key insights that help us understand this concept. The current literature on manufacturing flexibility contains several excellent papers that synthesize and organize previous literature dealing with flexibility dimensions. The terms and definitions of various authors are not always consistent; the same term often being used in different ways, and
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several different names being given to essentially the same type of flexibility. For
example, many of the articles describe manufacturing flexibility as “the ability to change
or react with little penalty in time, effort, cost or performance,” Upton (1994). Flexibility
has been widely recognized as a multi-dimensional concept within the manufacturing function (Gerwin,1993).
The domain of manufacturing flexibility is comprised of several different flexibility types or dimensions, with each dimension having its own constituent elements.
The literature contains many frameworks and taxonomies that attempt to capture the complexity of manufacturing flexibility. For example, Slack (1983) presents a general framework for measuring manufacturing flexibility. Gupta and Goyal (1989) define nine dimensions of manufacturing flexibility. To date, the most comprehensive typology is by
Sethi and Sethi (1990), who isolate eleven flexibility dimensions, and discuss their purposes, means of implementing them, and methods for measuring them. Sethi and
Sethi (1990) suggests that there are three hierarchies of manufacturing flexibility: 1) component or basic flexibility (machine, material handling, and operation); 2) system flexibility (process, routing, product, volume, and expansion); and 3) aggregate flexibility
(program, production, and market).
In many of these conceptual papers, the basic elements of flexibility emerge. For example, Slack (1983) suggest that flexibility must be defined by three basic elements
(range, cost, and time): range of possible states the system can adopt, cost of moving from one state to another, and the time required to move from one state to another.
Similarly, Upton (1994) suggests that there are three basic elements (range, mobility, and uniformity) of manufacturing flexibility: range (possible states), mobility (ease of
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movement between states), and uniformity (similarity of performance within a given range).
Firms typically use three dimensions of manufacturing flexibility to respond to demand fluctuations: volume flexibility, product mix flexibility, and new product flexibility. We also note that other researchers (e.g. Suarez et al, 1995 and 1996) have studied the relationship between these three dimensions and conclude that “some of the flexibility types move together, such as mix and new product flexibility, whereas volume flexibility responds to whole different dynamic.” Therefore, in this research, we focus exclusively on volume flexibility and to maintain scientific integrity, we carefully isolate and identify the drivers and sources of volume flexibility using our triangulated methodology in chapters 3, 4 and 5. For now, we define each of these concepts.
2.1.1. Volume Flexibility
Volume flexibility of a manufacturing system is "its ability to be operated profitably at different overall output levels” (Sethi and Sethi, 1990). This definition is consistent with Gerwin (1993), Suarez et al. (1996), Gupta and Goyal (1989) and Browne et al. (1984), and is similar to the demand flexibility of Son and Park (1987). The main strategic purpose of volume flexibility is to help cope with aggregate demand uncertainty.
Volume flexibility permits the firm to adjust production upwards and downwards within wide limits. In terms of range, mobility and uniformity, volume flexibility is "the extent of change and the degree of fluctuation in aggregate output level, which the system can accommodate without incurring high transition penalties or large changes in performance outcomes” (Upton, 1994). If a firm’s costs are modeled by only fixed and variable costs components, a system with relatively low fixed and variable costs is more
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volume-flexible than another system that chooses to have relatively higher fixed cost in order to have lower variable cost (Sethi and Sethi,1990). Average manufacturing cost of the former will be less sensitive to volume changes than that of the latter.
2.1.2. Product-mix Flexibility
Product-mix flexibility is closely related to volume flexibility. Product-mix
flexibility is defined as “the number and variety of products that can be produced without
incurring high transition penalties or large changes in performance outcomes,” (Upton,
1994). Using Slack’s (1983) framework, product-mix flexibility is measured by the size
of the product mix a system can manufacture and the time and cost of changing the
product mix. Other researchers have suggested that the level of product-mix flexibility in
a firm must be assessed within the current production system configuration without
considering major setups or facility modifications (Gupta and Somers, 1996). As is the
case with volume flexibility, product-mix flexibility also increases the ability of the firm
to respond rapidly to changes in market conditions (Suarez et al.,1996). Therefore, the
breadth of a firm’s product line can make it difficult to isolate and measure the true
impact of volume flexibility (Kekre and Srinivasan, 1990). While we recognize the
product mix issue as a potential confounding factor, we have elected to measure volume
flexibility using a triangulated approach in order to validate our findings more strongly.
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2.1.3. New Product Flexibility
New product flexibility is another dimension of manufacturing flexibility that is used to respond to rapidly changing demand. New product flexibility is “the number and
variety of new products that are introduced into production without incurring high
transition penalties or large changes in performance,” (Dixon, 1992). The number of new products provides insight into a firm’s strategic emphasis on product development, while the variety of products relates to the innovativeness of the new products. Dixon
(1992) measures mix flexibility by the number of different product characteristics made simultaneously, the number of different product characteristics produced during a one- month period and the average number of changeovers among these characteristics.
2.2. Measuring Manufacturing Flexibility
Table 2.1 also shows that there has been a broad-based effort to measure manufacturing flexibility. However, Slack (1983) warns that empirically measuring flexibility is very difficult, since flexibility represents the potential of a system, and does not need to be demonstrated to be real.
2.2.1. Conceptual Measures
At the conceptual level, Upton (1994) states that in measuring manufacturing flexibility, researchers should clearly define the dimension of flexibility, the time period under consideration, and the three basic elements (range, mobility, and uniformity) of that dimension of flexibility. Perhaps Gerwin (1993) best captures the challenges in measuring manufacturing flexibility by suggesting that flexibility is more than just an
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adaptive response to uncertainty and that flexibility measures should capture the
difference between required, potential and actual flexibilities. Required flexibility
represents management’s strategic determination of how much is needed of a particular
type of flexibility. Potential flexibility is determined by existing plant capabilities if
external conditions are appropriate. Actual flexibility stems from the utilization of plant
capabilities and is determined by experience.
Gerwin suggests that required flexibility can be gleaned from customer surveys
and other marketing feedback mechanisms. Evaluations of potential flexibility are likely to come from internal assessments within the firm. Actual flexibility is determined from performance data. Therefore, applying Gerwin’s concepts we can argue that required volume flexibility may be captured by variation in demand (not variations in sales).
Potential volume flexibility may be measured by variation in sales and inventory, or the ability to ramp up and ramp down production within certain cost limits. Actual volume flexibility may be measured by the actual performance of the firm.
2.2.2. Models
There have also been several models that suggest ways to measure manufacturing flexibility. Among these, we focus on two key contributions: deGroote (1994b) and
Jordan and Graves (1995).
First, deGroote (1994b) makes an important distinction between flexibility and diversity in the environment. deGroote defines diversity as the variability, variety, or complexity in the environment. As such, diversity can be related to the variability of market conditions as characterized by a stochastic or a seasonal demand or random input prices. deGroote defines flexibility as a hedge against the diversity of the environment.
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As such, a particular technology is said to be more flexible than another is, if an increase
in the diversity of the environment yields a more desirable change in performance than
the change that would be obtained with the other technology under the same conditions.
deGroote defines technology broadly to include any aspect of the firm’s production
resources, control procedures, and overall strategy. Therefore, if the focus of an analysis
is on the design of the firm’s overall strategy, one would include external factors
(variability in market conditions) into the specification of the environment while other
considerations which directly measure technology would be used to define flexibility.
The second key contribution from the stream literature on flexibility models that
we highlight is from the work of Jordan and Graves (1995) who show that limited
flexibility configured in the right way can provide most of the benefits of full flexibility.
Jordan and Graves (1995) examine process flexibility as a buffer against demand
uncertainty. Process flexibility comes from being able to use the same equipment for the
production of more than one product. The example Jordan and Graves (1995) use comes
from the auto industry, and it considers the problem of manufacturing more than one
model of automobile at a plant. This work makes an important contribution on a way to
value flexibility: by comparing the sales for a given product/plant configuration to sales
in a hypothetical fully flexible configuration, where each plant has the capability of
producing all products. They show that it is possible to achieve most of the benefit of a
fully flexible system in a much less flexible system, by assigning products to plants in a
certain way. The framework conceptualizes the set of plants and products as nodes in a
network, and assignments of products to plants as arcs. The key idea is to make assignments in a way that creates long product/plant chains, preferably with a cycle.
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Intuitively, such scheme creates a system that can do almost everything a fully flexible system can do in terms of switching products among plants. This creates a large amount of process flexibility at a cost much lower than a fully flexible system would cost.
In chapter 3, we exploit the arguments of deGroote (1996) and Jordan and Graves
(1995) in order to develop four new process-based measures of volume flexibility.
2.2.3. Empirical Measures
Despite the conceptual groundwork and the detailed modeling efforts, significant challenges still remain in developing and testing actual empirical measures of manufacturing flexibility. Many researchers have attempted to measure the multidimensional construct of manufacturing flexibility. In this research, we highlight the work of Kekre and Srinivasan (1990), Fiegenbaum and Karnani (1991), Gupta and
Somers (1992), Ettlie and Penner-Hahn (1994), and Suarez, Cusumano and Fine (1996).
Kekre and Srinivasan (1990) investigated the positive and negative effects of product line breadth on the firm’s performance. These authors validate an empirical model that captures the direct and indirect effects of product line breadth both on marketing performance and on manufacturing costs. They conclude that broader product lines lead to a higher market share and to increased profitability. These findings suggest that it may be stated that larger firms are more profitable than smaller ones are because of both economies of scale and product line breadth (economies of scope). Therefore, when measuring the impact of volume flexibility researchers should also consider that product mix flexibility might be a confounding factor. This potential problem is another reason why we have elected to a triangulated methodology to measure volume flexibility.
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Fiegenbaum and Karnani (1991) studied 3,000 firms in 83 industries and measured output flexibility as the standard deviation of sales over a 16-year time period
(1979-1987). These authors concluded that there is a direct relationship between firm size and output flexibility and that output flexibility is a source of competitive advantage for small firms. Since we use this work as a launching pad for our own empirical research, we will describe this study in more detail in chapter 3. For now, we simply state that we use the theoretical arguments from deGroote (1996) and Jordan and Graves
(1995) to argue that a better measure of volume flexibility should incorporate environmental diversity, technology, and performance considerations.
Gupta and Somers (1992) developed and validated several scales for measuring flexibility. In a very comprehensive, large-scale empirical study, these authors surveyed
269 firms, isolated 34 items for measuring flexibility, and developed a 21-item instrument using the Sethi and Sethi (1990) classification system. The items come from various sources in the flexibility literature and represent an attempt at developing a comprehensive flexibility measure. The factor analysis revealed nine important factors, corresponding to nine flexibility dimensions: expansion/market flexibility, material handling flexibility, routing flexibility, machine flexibility, market flexibility, product/production flexibility, process flexibility, programming flexibility, and volume flexibility. These nine dimensions are very close to the Sethi and Sethi dimensions, with the exception that product and production flexibilities are combined, and operation flexibility is not considered separately. In chapter 5, we also develop several scaled items to measure volume flexibility and we identify eight factors that document the drivers and sources of volume flexibility.
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Ettlie and Penner-Hahn (1994) propose empirically measuring actual flexibility achieved using ratios instead of traditional measures. Traditional measures of system flexibility include the number of unique parts scheduled for production, the number of part families (categories of similar part types) scheduled for production, and average changeover time between two parts scheduled. Ettlie and Penner-Hahn (1994) propose several measures to include: the ratio of the number of part types to the number of part families and its inverse, the ratio of the number of part families to the changeover time and its inverse, and the ratio of the number of parts types to changeover time, and its inverse. In chapter 3, we use similar ratios to develop four new process-based measure of volume flexibility.
Another study by Suarez, Cusumano and Fine (1996) develops a framework to help incorporate flexibility into the manufacturing strategic planning process. These authors studied 31 plants in the printed circuit board industry and they define three different types of flexibility, develop measures for these types of flexibility, and examine the relationships among them. Suarez et al (1996) identified six factors (sources) that impact the implementation of manufacturing flexibility: production technology; production management techniques; relationships with subcontractors and suppliers; human resource issues; product design; and accounting and information systems. They find that more automated plants are less flexible, even when the equipment is programmable. They also conclude that factors not directly connected with the production system play an important role in increasing flexibility. The factors Suarez et al. (1996) mention specifically are the direct involvement of workers in the problem- solving activities, close relationships with suppliers, and flexible wage schemes. The
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direct involvement of workers in the problem-solving activities is an aspect of the
"technology" because it affects how the employees act under various conditions. Directly involving the workers into the decision-making process makes them more committed to the final decision, and more willing to be flexible. The close relationships with supplies and flexible wage schemes are examples of uncertainty management strategies. Close relationships with supplies reduce the uncertainty about the quality of raw material, and the lead times, while building flexibility into employee compensation is likely to reduce the probability of a labor strike, for example. It also gives the manufacturer better control over the labor costs, and the ability to adjust faster to changing environmental conditions.
However, despite these efforts, the stream of research that focuses on specific measures of volume flexibility has not been well represented in the literature. Also, efforts to validate the impact of volume flexibility on firm size and performance have also been scarce (Suarez, et al, 1996). One recent study by Swamidass and Suresh (1998) focused on how effectively firms use their Automated Manufacturing Technology
(AMT). These authors study 86 firms in the machine tool industry and show that AMT use increased rapidly as firm size increases logarithmically. In chapter 3, we also test the relationship between volume flexibility and firm size.
2.3. Flexibility Tradeoffs
Other efforts have also focused on the tradeoffs involved in adopting strategies based on manufacturing flexibility. Table 2.1 shows much of the research on flexibility tradeoffs has been done from the conceptual and modeling perspectives. Consequently, there appears to be a gap in actual measures that empirically test and validate these tradeoffs. The task of identifying and quantifying these tradeoffs can be very
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challenging. The transition penalties involved in moving from one flexible state to another, include lost production time, the cost of lost production, and scrap or rework
(Gerwin, 1987). Some researchers have studied the tradeoffs involved in volume flexibility. For example, Mills and Schumann (1985) builds on the work of Stigler
(1939) by looking at 856 US manufacturing firms and arguing that there is a trade-off between static cost efficiency and volume flexibility.
Volume Flexibility Tradeoffs
(VF1 > VF2)
Cost ($) AC2
AC1
C1
C2
V V 1 2 Volume
Figure 2.2 Flexibility Tradeoffs
Stigler (1939) suggests that a firm is more volume flexible if it has a lower marginal cost associated with changes in output volume. Stigler (1939) makes the assumption that the total cost function of the firm is increasing and convex. Figure 2.2 describes two firms with different cost curves. Firm 1, which is the smaller firm, has a higher minimum efficient cost (C1) than that (C2) of the larger firm, 2. Also, the larger firm can produce most efficiently at a higher volume level (V2) than that of the smaller
47
firm (V1). Correspondingly, the average cost curve (AC1) is flatter for firm 1 than that
(AC2) of firm 2. Clearly, the smaller firm (Firm 1) incurs a smaller penalty in deviating
from its optimal output that does the larger firm (Firm 2). As such, volume flexibility of
the firm’s cost function is represented by how flat the bottom of the curve is. This trade-
off is based on the differences in production technologies used by large and small firms.
Therefore, in practice, volume flexibility may be measured as the second derivative of the
average cost curve. In this example, the smaller firm is more volume flexible than the
larger firm is (VF1 > VF2).
In their work, Fiegenbaum and Karnani (1991) also state that “… a direct
empirical test of this argument would be to measure the cost functions of many firms of different sizes in an industry.” However, they argue that it would be very difficult, if not impossible to get data for such an empirical test. Therefore, these authors elect to measure the variability in output over time in response to changing market conditions.
The main problem with this approach is that it may only measure variability in the environment. In our study, we argue that if one wants to measure the flexibility of small firms versus large firms, one must find surrogate variables that can reliably measure the impact of the differences in production technologies between large and small firms. In chapter 3, we use this key argument to develop a theoretical framework for four new process-based measures of volume flexibility.
2.4. Valuation of Flexibility
Valuation of manufacturing flexibility is the fourth area of content in this literature stream. As shown in table 2.1, much of the work done in this area has been supported by models that focus on flexibility as an option price or as a contingency plan
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[Fine, C. and Freund R. (1990); Ramasesh and Jayakumar (1991); Huchzermeier and
Cohen (1996); Kogut and Kulatilaka (1994)]. In this research stream, many of the models are based on financial valuations of discounted cash flows derived from estimated levels of uncertainty or risks in the environment. For example, Ramasesh and Jayakumar
(1991) measured the aggregate flexibility of a manufacturing system by investigating the joint effect of flexibilities on a variety of dimensions when measurement for a medium- term time horizon is appropriate. These authors propose a value-based approach in which flexibility is measured by the ability of a manufacturing system to generate revenues consistently across all conceivable states. They suggest an index, (the ratio of the mean to the standard deviation of the distribution of optimal revenues) as the aggregate measure of flexibility.
In this research we will also measure the value of volume flexibility. But, unlike this valuation research perspective that focus on flexibility as an option price or a contingency plan, we measure the perceived value of volume flexibility by relying on the perceptions of managers at the plant level. We present this analysis in chapter 5.
2.5. Drivers and Sources of Volume Flexibility
In our review of the OM literature, we found no research that focused specifically on the drivers and sources of volume flexibility. Much of the research in this arena describes flexibility simply as a response to uncertainty in the business environment
[Gupta and Somers (1992), Gerwin (1993), Sethi and Sethi (1990), Slack (1983), and
Upton (1995)]. Even the researchers who have measured volume flexibility [e.g. F&K,
Suarez et al (1996), and Vickery et al (1999)], they have not focused specifically
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identifying the drivers and sources of volume flexibility. Therefore, we have chosen to specifically address this gap in our research and we present a detailed analysis in chapters
4 and 5. For now, we will define each of these concepts and lay the foundation for our analysis in chapters 4 and 5.
Drivers of volume flexibility are defined as a key factors (internal or external to the organization) that cause a firm to adopt a volume flexibility strategy. The theoretical underpinnings of this notion could be traced back to the work of Stigler (1939) who argues that flexibility and adaptability are related in that “the greater the adaptability, the less the need for flexibility.” Therefore, Stigler suggests that the real need for flexibility clearly arises when there is partial adaptability. A firm’s adaptability is considered partial when it is unable to react efficiently and effectively to uncertainties in the competitive environment. Perhaps the uncertainties in the environment are best captured by d’Aveni (1995) who describe several hyper-competitive environmental forces such as short product cycles, new technologies, short product design cycles, and radical redefinition of market boundaries. In chapter 4, we identify some of the key drivers of volume flexibility and organize them into two categories: external market forces (e.g. variability in order volume, number of market segments, and delivery lead time) and internal strategic choices (e.g. forecasting challenges, delivery reliability, and core competencies).
The sources of volume flexibility are embedded in how a firm uses its resources to respond to uncertainties in its business environment. There is ample evidence in the
OM strategy literature for the notion that a firm can effectively use its resources as sources of volume flexibility. Skinner (1969, 1974, and 1985) has long argued for closer
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coordination between manufacturing operations and corporate strategy in order for a
business to gain and sustain a competitive advantage. Other researchers, for example,
Hayes and Wheelwright (1984) describe manufacturing strategy as “ a sequence of
decisions that, over time, enables a business to achieve a desired manufacturing structure
(i.e. capacity, facilities, technology, and vertical integration), infrastructure (i.e., workforce, quality, production planning/material control, and organization), and a set of specific capabilities (that enables it to pursue its chosen competitive strategy over the long term).” Therefore, in chapter 4 and 5, we build on this foundation and develop a taxonomy of internal and external sources and we also consider the time dependence of a volume flexibility strategy by identifying some of the short-term and long-term sources of volume flexibility.
2.6. Relevant Arguments from Organizational Theory Literature
Perhaps the organizational theory literature offers some clearer insights as to why small firms may be more adaptable or flexible than larger firms are. Scott (1992) explains the three schools of thought that view organizations as rational, natural, and open systems.
1. Rational systems theorists view organizations as highly formalized collectivities
oriented to the pursuit of specific goals.
2. Natural systems theorists view organizations as collectivities seeking to survive.
3. Open systems theorists view organizations as coalitions of interest groups highly
influenced by their environments
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The underlying link between each of these perspectives is the relative importance
that environmental factors play in determining the adaptability and survival of
organizations. Scott (1992) suggests that three basic arguments emerge on the role of
environmental elements and pressures in influencing organizational development:
contingency, population ecology, and resource dependency.
Contingency argument (Lawrence and Lorsch, 1967) suggests there is no one
organizational form but several, and their suitability is determined by the extent of the
match between the form of the organization and demands of the environment. In essence,
Lawrence and Lorsch argue that if an open system perspective is taken, then the rational
and the natural system perspectives may be seen to identify different organizational
types, which vary because they have adapted to different types of environments. The
rational and natural system perspectives are at variance because each focuses on a
different end of a single continuum representing the range of organizational forms.
The population ecology approach (or natural selection model) originated in
biology with the work of Darwin and stresses selection by attributing observed patterns in
the distribution of organizational forms to the action of environmental selection, both in
terms of what kinds of organizations are created and which survive. Carroll (1984) argues that this model differs from other approaches to organizations in that it applies primarily to populations of organizations rather than to individual units. It is central to the natural selection thesis that environments differentially select organizations for survival on the basis of fit between organizational forms and environmental characteristics. Three processes are emphasized in evolutionary analysis: the creation of variety, the selection of some forms over others, and the retention of those forms
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(Campbell, 1969). In the first stage, variety is created by some process, planned or unplanned. In the second, some forms of organization are differentially selected for survival. And in stage three, the selected forms are preserved in some fashion, by reproduction or duplication.
The resource dependence model, as developed by Pfeffer and Salancik (1978), emphasizes adaptation. Much more so than in the population ecology approach, the resource-dependent theorists view organizations as active, not passive, in determining their own fate. Organizational participants, particularly managers, scan the relevant environment, searching for opportunities and threats, attempting to strike favorable bar- gains and to avoid costly entanglements. All organizations are dependent on suppliers and consumers but which specific exchange partners are selected and what are the terms of exchange is partly determined by the organization itself. Astute managers acquire necessary resources but do so without creating crippling dependencies.
Therefore, using these arguments of organizational development theorists, we can provide more intuitive explanations as to why small firms may be more adaptable to their environment than larger firms are. The population ecologist argument can be used to argue at the industry level that small firms adapt more naturally than larger ones do to drastic changes in the environment, and thus smaller firms are more apt to survive. On the other hand, the resource dependency argument can be used to show the interconnection between internal and external dependencies of the firm. Thus, using the resource dependency perspective, one can argue that a large firm has greater resources
(e.g. more inventory and capital resources to increase or decrease capacity quickly) to withstand “shocks” in volume fluctuations. Therefore, in this research, we will use these
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arguments to distinguish between large and small firms’ use of technology and their operating environments.
2.7. Distinguishing between Small and Large Firms
There are many ways to define a small firm. For example, Robinson and Pearce
(1984) found that firms were typically defined as small on the basis of either annual sales or number of employees. Robinson and Pierce (1984) reviewed 50 studies that defined a small business based upon the number of employees (from one to 2000). Keats and
Bracker (1988) found that based on average annual sales, researchers place small firms in the range from $150K to $100M. The US Small Business Administration defines a small firm as “one that is independently owned and operated, and is not a dominant player in its field of operation.” Other researchers define a small firm as having fewer than 500 employees. However, this measure of using number of employees is deceiving because it depends on the industry. In many service industries, such as a bank or a restaurant, a firm with 500 employees would be considered large. On the other hand, most manufacturing firms that employ less than 500 employees would be considered small. Therefore, since our research will focus primarily on manufacturing industries, we define a small firm to be one that employs less than 500 employees or one with an average annual income of less than $100M.
What are the key differences in operating characteristics between small and large firms? Many researchers have addressed this question in the operations strategy arena [e.g. Penrose (1959), Skinner (1969), Porter (1980), Wernerfelt (1984), Prahalad and Hammel (1990), and Voss (1992)]. Also, in a recent study, Dean et al (1998)
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perform a comparative analysis of the impact of industry characteristics on the formation of large and small businesses in a large sample of US manufacturing firms. Their results suggest that small firms have certain attributes that allow them to overcome some of the barriers that create greater difficulties for their larger counterparts. These authors argue that small firms possess capabilities characterized as niche-filling or selective focus, flexibility, and streamlined operations that result in quicker response to the dynamics of industry environments. On the other hand large firms can capitalize on advantages typically associated with greater size. They can acquire market share based on broad product lines and reputation, exploit patents and scale economies in research and development, exert bargaining power over suppliers and customers and dominate through leadership pricing (Porter, 1980). Therefore, given these characteristic differences between large and small firms, Dean et al (1998) show that small businesses will be more likely to enter industry environments in which speed, flexibility, and niche targeting are rewarded. In contrast, large firms will be more likely to successfully enter industries in which deep pockets, economies of scale, and broad-based strategies are most advantageous.
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2.8. Identifying the Gaps in the Literature
In this detailed review of the operations management literature on manufacturing
flexibility we have identified three major gaps. First, there is a paucity of research particularly on empirical measures of volume flexibility. Second, even the few researchers, who have laid the foundations for empirical measures of volume flexibility, they have also identified several gaps in this research stream. Third, as we discussed in chapter 1, several researchers [e.g. (Scudder and Hill, 1998) and (Meredith, 1998)] have recently shown that empirical research is generally under-represented in the operations management literature.
Although our taxonomy of the literature on manufacturing flexibility (Table 2.1) shows a fair representation of empirical research efforts, a closer analysis suggests that few papers have actually focused on the subject of volume flexibility. Two papers that focus on empirical measures of volume flexibility are particularly relevant to our research: Fiegenbaum and Karnani (1991) and Suarez et al (1995 and 1996). We will discuss the F&K study in detail in chapter 3. The Suarez et al (1995 and 1996) papers are relevant because they use empirical research methods to measure volume, new product and product mix flexibilities. Their results show that volume flexibility “appears to be orthogonal to the other two types: mix and new product flexibilities.” Also, as we have discussed earlier, we have found no current research that focuses specifically on the drivers and sources of volume flexibility.
The devoted researchers in this area have also identified the gaps in empirical measures of flexibility. For example, Gerwin (1993) suggests that research on flexibility needs to have more of an applied focus to complement the existing theoretical work.
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Gerwin also advises that the main stumbling block to advances on both the theoretical and applied fronts is the lack of measures for flexibility and its economic value. deGroote (1994b) argues that an extension of his framework would be to begin with a particular characterization of flexibility and diversity and then to find a performance function that relates the two properties. Fiegenbaum and Karnani (1991) recommend that an extension of their work would be to investigate the relative importance of economic and organizational factors influencing output flexibility. Suarez et al (1996) suggest that additional studies should be conducted in other industries before we can determine whether or not their measures of flexibility apply to other manufacturing contexts. Upton
(1994) argues that “. . . in order for managers to be able to build manufacturing flexibility and exploit it for lasting competitive advantage, we need to develop a clear understanding of what is meant by the capability of flexibility and also identify the characteristics of operations that support that capability.” Finally, Dixon (1992) suggests that in measuring flexibility, we should focus not solely on the costs of making changes between different production states but more importantly on the resources consumed in the process.
Therefore, given these gaps in the operations management literature, we have chosen to do an empirical study to measure one construct, volume flexibility. Our research contributes to the operations management literature stream in both theory building and theory testing on the measures of volume flexibility. We contend that there are still many unanswered questions about volume flexibility: What is it? How do we measure it? Does it impact the bottom line? And if so, how? Consequently, because of the complex nature of this concept, we have elected to use multiple research methods
(triangulation) in order to adequately capture the essence of volume flexibility.
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Chapter 3. Empirical Validation using Secondary Data
Volume flexibility has been shown to be a source of competitive advantage for many firms [Fiegenbaum and Karnani (1991) and Vickery et al (1999)]. A firm that can vary volume and order sizes at minimal cost is by definition more volume flexible. To hedge against known demand variability, firms generally use safety stock, safety lead- time, overtime and other operational measures. However, if a firm carries excessive safety stock, it is hedging against environmental uncertainty at an unnecessarily high cost. Similarly, if a firm deploys overtime more than it needs to (with labor costs at
150% of regular wages) one can argue that it is spending its money ineffectively.
Therefore, the dilemma for many firms is in determining how to strike the correct balance in order to sustain a competitive advantage. Thus, the question that we address is how do we characterize the performance of a volume flexible firm by simultaneously accounting for the environmental (demand) uncertainty that it faces and how effectively it hedges against that uncertainty.
3.1. Objectives
In chapter 1 we laid out three major objectives of this research. In this chapter, we focus on the first objective from two perspectives. First, we want to replicate the
Fiegenbaum and Karnani (1991) study [henceforth referred to as F&K] to test if their results are reproducible in a subset of industries. F&K use the variability in sales as the primary measure for output flexibility of a firm and conclude that small firms are more volume flexible. But variability in sales, de Groote (1994b) argues, is a measure of
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diversity in the environment, and as such, it is not a valid measure of flexibility.
Flexibility can only be defined as a property of technology. Others [for example, Dixon
(1992)] have characterized flexibility as a property of the process that provides a hedge against diversity in the environment. Therefore, our second major focus is to develop and test new process-based measures of volume flexibility to see it they produce different results that those presented by F&K. Since our research is based primarily on the work of F&K, it is important to summarize their efforts in order to put our own research into context.
3.2. The Fiegenbaum and Karnani (1991) Study
Fiegenbaum and Karnani (1991) concluded that there is a direct relationship between firm size and output flexibility and that output flexibility is a source of competitive advantage for small firms. These authors studied 3,000 firms in 83 industries during the 1979-1987 time frames and measured volume flexibility as the standard deviation of sales over a time period. F&K argue that small firms are more volume flexible than large firms are. The author’s basic arguments are captured as follow:
· Small firms are more willing to fluctuate their output
· Small firms can trade cost efficiency with volume flexibility to increase their
profits
· Output flexibility is a more viable source of competitive advantage in volatile and
capital-intensive industries, and less viable in profitable industries.
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Fiegenbaum and Karnani’s (1991) Hypotheses
Measured at the Firm level Measured at the Industry level H1: ( - ) H5: ( - ) Demand Volatility Size
H2: ( + ) Volume Capital Flexibility H6: ( - ) Intensity H4: ( - ) H7: ( + ) Profitability Profitability H3: ( 0 )
Figure 3.1 F&K’s Hypotheses
To support these arguments, F&K proposed seven specific hypotheses as shown in Figure 3.1. Note that this chart was not developed by F&K, we included this chart to visually convey the relationships between their hypotheses. The actual list of F&K’s seven hypotheses are presented in the following table:
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Fiegenbaum and Karnani (1991) Hypotheses
Number Hypotheses
H1 In a given industry, the degree of fluctuation in firm output will be inversely related to firm size.
H2 In a given industry there should be a positive relationship between firm size and financial performance.
H3 In a given industry we would expect no relationship between a firm’s output flexibility and its financial performance.
H4 In a given industry we expect the inter-active term of output flexibility and firm size to have a negative relationship with financial performance.
H5 The strength of the relationship between firm size and output fluctuation will be positively related to industry demand volatility.
H6 The strength of the relationship between firm size and output fluctuation will be positively related to industry capital intensity.
H7 The strength of the relationship between firm size and output fluctuation will be inversely related to industry profitability.
Table 3.1 List of F&K's Hypotheses
F&K test their hypotheses using regression analysis and a non-parametric sign test in order to show that the sign of the regression coefficients were consistent with their proposed hypotheses. Using these two statistical criteria, these authors present three basic findings:
1. There is a tradeoff between output flexibility and firm size, in that, small
firms demonstrate more output flexibility than large firms do.
2. Output flexibility is a viable source of competitive advantage for small
firms.
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3. Output flexibility is a source of competitive advantage, especially in
industries facing stronger demand fluctuations, higher capital investments,
and less profitability.
In this research, we will first revalidate F&K’s seven hypotheses in a subset of industries. Then, to accomplish our second objective, we will develop new process-based measures and compare our results to F&K’s second finding.
3.3. Data Set to Revalidate F&K’s Hypotheses
The data set used by F&K (3000 firms in 83 industries) contained both product and service industries (e.g. television broadcasting, cable television, electric services, etc.). We are not certain that output or volume flexibility can be measured the same way in both product and service industries. We want to determine if F&K’s findings can be revalidated in a subset of primarily product industries.
Therefore, in order to revalidate F&K’s hypotheses in a subset of product industries, we acquired data from the Compustat business database. We extracted data representing 20 years (1979-1998) of financial performance data on 550 firms from 29 sectors primarily in the capital goods industries (SICs 3510-3590). For 10 of these firms, we did not have data on the number of employees. Therefore, in the following three tables, we provide a detailed distribution of 540 firms within each SIC broken down by two measures of size (1) using number of employees and (2) using average annual sales.
For each industry sector, the tables also show the average annual sales, average net income, and average total assets for all firms within that SIC and size group.
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Employees Average Sales ($M) SIC Name Variables < 500 500-1000 > 1000 < 50 50 - 100 > 100
3510 ENGINES & TURBINES # Firms 7 7 Avg_Sales($M) 1,158.78 1,158.78 Avg_NI($M) 20.82 20.82 Avg_Assets($M) 923.42 923.42
3523 LAWN, GARDEN EQUI # Firms 4 11 3 2 10 Avg_Sales($M) 27.75 2,169.53 10.66 85.23 2,377.33 Avg_NI($M) 2.11 88.30 0.08 7.24 96.51 Avg_Assets($M) 18.35 2,437.84 7.98 61.91 2,674.19
3524 CONSTR & MINING EQ # Firms 1 1 1 1 Avg_Sales($M) 25.53 800.20 25.53 800.20 Avg_NI($M) -0.33 6.73 -0.33 6.73 Avg_Assets($M) 17.68 691.56 17.68 691.56
3530 CONST MACHINERY # Firms 1 3 1 3 Avg_Sales($M) 20.49 1,060.64 20.49 1,060.64 Avg_NI($M) 0.17 70.95 0.17 70.95 Avg_Assets($M) 13.17 779.03 13.17 779.03
3531 OIL & GAS EQ # Firms 1 1 7 1 1 7 Avg_Sales($M) 9.69 94.40 1,999.45 9.69 94.40 1,999.45 Avg_NI($M) 0.76 6.46 67.46 0.76 6.46 67.46 Avg_Assets($M) 7.97 44.84 2,282.28 7.97 44.84 2,282.28
3533 INDL TRUCKS/TRACTRS # Firms 4 1 7 5 3 4 Avg_Sales($M) 28.24 67.55 488.12 29.08 72.91 808.29 Avg_NI($M) 0.71 3.06 7.81 1.86 5.28 8.84 Avg_Assets($M) 29.81 55.58 531.90 31.60 62.87 887.88
3537 METALWORKING EQ # Firms 1 5 1 5 Avg_Sales($M) 12.08 346.69 12.08 346.69 Avg_NI($M) -0.10 8.07 -0.10 8.07 Avg_Assets($M) 6.17 332.83 6.17 332.83
3540 MACHINE TOOLS # Firms 8 1 7 8 2 6 Avg_Sales($M) 17.55 59.89 853.02 17.55 70.93 981.53 Avg_NI($M) -0.16 0.13 38.47 -0.16 1.49 44.40 Avg_Assets($M) 13.92 50.14 868.22 13.92 62.64 1,000.40
3541 MACHINE TOOLS, CUT # Firms 4 4 Avg_Sales($M) 182.85 182.85 Avg_NI($M) -6.73 -6.73 Avg_Assets($M) 168.71 168.71
3550 SP INDL MACHINERY # Firms 3 1 3 4 3 Avg_Sales($M) 12.55 40.43 741.73 19.52 741.73 Avg_NI($M) -0.13 0.09 45.36 -0.07 45.36 Avg_Assets($M) 10.53 11.98 614.79 10.89 614.79 Table 3.2 SIC Breakdown, (Table 1 of 3)
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Employees Average Sales ($M) SIC Name Variables < 500 500-1000 > 1000 < 50 50 - 100 > 100
3559 SP INDL MACH NEC # Firms 40 9 13 34 14 14 Avg_Sales($M) 25.54 79.77 259.64 18.85 66.75 252.81 Avg_NI($M) -0.29 5.38 16.57 -1.01 3.18 17.29 Avg_Assets($M) 28.82 83.56 239.35 20.29 70.64 238.38
3560 GEN INDL MACH/EQ # Firms 7 3 9 8 3 8 Avg_Sales($M) 14.22 78.26 2,776.04 18.63 82.11 3,115.41 Avg_NI($M) -0.47 6.42 93.34 -0.31 7.90 104.35 Avg_Assets($M) 10.66 60.54 2,591.08 13.23 66.87 2,908.69
3561 PUMPING EQ # Firms 1 2 1 2 Avg_Sales($M) 81.05 229.71 81.05 229.71 Avg_NI($M) 5.90 16.36 5.90 16.36 Avg_Assets($M) 58.87 181.71 58.87 181.71
3562 BALL/ROLLER BEARINGS # Firms 3 1 4 3 2 3 Avg_Sales($M) 25.30 62.13 1,116.92 25.30 60.51 1,469.60 Avg_NI($M) 2.53 9.34 36.24 2.53 6.42 47.15 Avg_Assets($M) 18.22 35.31 1,197.17 18.22 84.86 1,551.42
3564 INDL FANS/BLOWRS # Firms 12 1 2 12 2 1 Avg_Sales($M) 15.72 56.69 202.36 15.72 60.88 339.65 Avg_NI($M) -0.16 2.92 9.73 -0.16 2.94 16.49 Avg_Assets($M) 12.43 35.82 134.75 12.43 54.42 196.48
3567 INDL PROC FURNACES # Firms 8 8 Avg_Sales($M) 16.42 16.42 Avg_NI($M) -0.60 -0.60 Avg_Assets($M) 14.08 14.08
3569 GEN INDL MACH/EQ NEC # Firms 13 7 12 2 6 Avg_Sales($M) 15.76 553.51 11.03 68.97 634.86 Avg_NI($M) -0.14 19.33 -0.18 0.91 22.31 Avg_Assets($M) 12.31 539.05 10.95 35.48 621.84
3570 COMPUTER/OFFICE EQ # Firms 6 1 5 Avg_Sales($M) 18,430.68 69.72 22,102.87 Avg_NI($M) 809.18 -4.98 972.01 Avg_Assets($M) 20,088.48 65.21 24,093.14
3571 ELECTRONIC COMPTRS # Firms 24 3 11 24 3 11 Avg_Sales($M) 10.83 51.50 17,438.47 10.83 51.50 17,438.47 Avg_NI($M) -1.37 -5.54 1,176.01 -1.37 -5.54 1,176.01 Avg_Assets($M) 9.77 45.60 8,355.72 9.77 45.60 8,355.72 Table 3.3 SIC Breakdown (Table 2 of 3)
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Employees Average Sales ($M) SIC Name Variables < 500 500-1000 > 1000 < 50 50 - 100 > 100
3572 COMP STOR DEVICES # Firms 18 1 11 16 3 11 Avg_Sales($M) 27.32 91.43 600.77 23.55 68.84 600.77 Avg_NI($M) 0.37 -5.93 18.28 -0.21 1.33 18.28 Avg_Assets($M) 21.24 60.95 438.67 16.89 57.70 438.67
3575 COMPUTER TERMINALS # Firms 10 1 1 9 2 1 Avg_Sales($M) 25.44 28.70 1,062.32 16.17 68.80 1,062.32 Avg_NI($M) -1.04 -0.68 -176.43 -0.72 -2.31 -176.43 Avg_Assets($M) 22.18 24.46 925.75 13.66 61.65 925.75
3576 COMPUTER COMM EQ # Firms 59 4 7 50 7 13 Avg_Sales($M) 28.25 171.47 2,318.93 17.24 67.99 1,326.72 Avg_NI($M) 0.02 7.53 -255.13 -0.78 1.67 -132.88 Avg_Assets($M) 27.33 206.62 2,398.53 17.20 54.57 1,383.60
3577 COMPUTER PERIPH NEC # Firms 57 3 11 53 6 12 Avg_Sales($M) 20.99 157.33 1,984.69 17.51 69.39 1,846.32 Avg_NI($M) -1.32 -3.27 76.08 -1.00 -3.33 68.71 Avg_Assets($M) 18.83 100.66 2,428.79 16.85 45.59 2,243.79
3578 CALC/ACCT MACH # Firms 14 2 5 16 1 4 Avg_Sales($M) 7.73 54.23 1,039.96 12.71 60.56 1,288.14 Avg_NI($M) -2.21 -3.03 35.28 -2.17 1.93 43.07 Avg_Assets($M) 11.29 56.63 857.96 16.52 46.00 1,062.73
3579 OFFICE MACHINES, NEC # Firms 4 6 3 2 5 Avg_Sales($M) 16.95 2,073.30 5.46 62.30 2,473.33 Avg_NI($M) -0.36 39.46 -0.27 2.12 46.38 Avg_Assets($M) 12.44 2,809.86 4.23 44.18 3,361.57
3580 REFRIG & SV IND MACH # Firms 14 2 5 15 1 5 Avg_Sales($M) 6.25 49.31 496.25 8.11 64.36 496.25 Avg_NI($M) -0.75 -0.98 13.77 -0.87 0.62 13.77 Avg_Assets($M) 5.95 50.73 375.75 9.04 49.07 375.75
3585 AC,HEATING,REFRIG EQ # Firms 4 2 12 5 1 12 Avg_Sales($M) 11.70 48.29 1,042.29 17.73 54.71 1,042.29 Avg_NI($M) -0.40 1.47 17.66 0.03 1.20 17.66 Avg_Assets($M) 7.71 37.31 744.24 13.36 38.67 744.24
3590 INDL, COML, MACHY # Firms 6 5 4 8 3 4 Avg_Sales($M) 16.28 51.01 346.95 18.92 67.13 346.95 Avg_NI($M) 0.75 3.62 -4.84 0.81 5.36 -4.84 Avg_Assets($M) 17.00 52.97 297.83 19.00 71.61 297.83 Table 3.4 SIC Breakdown (Table 3 of 3)
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Avg. Sales ($M)
Data 0-50 50-100 >100
# Firms 306 65 169
Avg_Sales($M) 16.35 67.99 2,868.84
Avg_Net_Income($M) -0.67 1.59 125.74
Avg_Asset($M) 15.36 59.25 2,355.13 Table 3.5 Distribution of Firms by Avg. Annual Sales ($M)
Number of Employees
Data 0-500 500-1000 >1000
# Firms 323 44 173
Avg_Sales($M) 20.4 80.4 2,798.4
Avg_Net_Income($M) -0.5 1.8 122.7
Avg_Asset($M) 19.0 74.44 2,295.7 Table 3.6 Distribution of Firms by Number of Employees
Table 3.5 gives a breakdown based on the average annual sales ($M) of the firms in this sample. The data showed a fair distribution of small, medium, and large firms as measured by average annual sales ($M). For example, 169 of the 540 firms in this sample had average annual sales of more than $100M and 306 of the 540 firms had average annual sales of less than $50M. We also analyzed this sample based on the average number of employees (over 20 years) in each firm (Table 3.6). Again, the data shows a fair representation of small, medium, and large firms as measured by the number of employees. For example, 323 of these firms had less than 500 employees and 173 of these firms had more than 1,000 employees.
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3.4. Data Manipulation
We followed the procedures used by both F&K and Mills and Schumann (1985)
in order to standardize the data over the 20-year period as summarized in Table 3.7.
Notice that we first standardized all the data using the CPI index. In a footnote in their paper, F&K suggested using the Mills and Schumann (1985) procedure to calculate the flexibility measure by using standard errors after extracting the growth trend. However, we called one of the authors (Dr. Karnani) to verify what procedure they actually used and he confirmed that they simply used the standard deviation of sales over the period as their flexibility measure. Therefore, in order to replicate F&K’s study, we decided to follow their exact procedures without any transformation of the variables used in the regression analyses.
Compustat Data Manipulation
• Computing output flexibility measure (SV) · Adjusted all the data based on CPI. · Computed (by SIC), mean and standard deviation of sales, inventory, assets, income, ROA, and ROS. · Used the standard deviation of annual sales over the 20-year period as the measure of output flexibility (SV) of sales over time. · Note that this SV measure replicates what F&K used but it is different from Mills and Schumann (1985) procedure as noted by F&K on page 106 of their paper • Industry level analysis
· Used the computed bj for each 4-digit SIC · Industry volatility (ISV) -- standard deviation of annual sales (by SIC). · Capital intensity (CI) calculated as the mean (Total Assets/ Total sales) for each industry. · Calculate average industry profitability (ROS) as the mean of (Net Income/Total sales)
Table 3.7 Compustat Data Manipulation
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3.5. Revalidating F&K’s Hypotheses
In this section, we describe how we tested the seven hypotheses posited by F&K in three phases. First we test and validate H1. Then we test and validate H2, H3 and H4.
Next, we test and validate H5, H6, and H7. Finally, we present a graphic summary of our findings.
3.5.1. Hypothesis, H1:
Fiegenbaum and Karnani (F&K) hypothesized that bj will be negative, which indicates that small firms are more volume flexible than large firms are. The formulation used to test this hypothesis is:
SVij = aj + bj * Sij + eij
Where: SVij = standard deviation of the CPI-adjusted sales for firm i in industry j.
Sij = size of firm i in industry j, measured by average annual sales over the 20-yr period.
eij = error term of firm i in industry j.
We tested H1 using regression analysis as hypothesized by F&K. We did not use any further data transformations that may cause our results to be different from those of
F&K. We simply recorded the regression results and noted whether the residual errors met at least one of three tests (skewness, kurtosis, and omnibus) for normality as shown in the following table. The results show that bj was positive for 26 of the 28 SICs considered. In addition, the results were significant for 25 of these 26 positive bj’s, as summarized in table 3.8.
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SIC SIC Name # Firms aj aj_Sig bj bj_Sig R2 P-value
3510 ENGINES AND TURBINES 7 -0.32 0.2352 ** 0.87 0.002
3523 FARM MACHINERY AND EQUIPMEN 15 0.17 0.2306 *** 0.75 0.000
3524 LAWN, GARDEN TRACTORS, EQUI 2
3530 CONSTR,MINING,MATL HANDLE E 4 0.00 0.2410 0.59 0.230
3531 CONSTRUCTION MACHINERY & EQ 9 0.19 * 0.2322 *** 1.00 0.000
3533 OIL & GAS FIELD MACHY, EQUI 13 0.39 0.2317 *** 0.78 0.000
3537 INDL TRUCKS,TRACTORS,TRAILR 6 0.00 0.4780 ** 0.75 0.026
3540 METALWORKING MACHINERY & EQ 16 -0.06 0.4476 *** 0.91 0.000
3541 MACHINE TOOLS, METAL CUTTIN 4 0.43 -0.0239 0.01 0.883
3550 SPECIAL INDUSTRY MACHINERY 7 0.19 0.2058 ** 0.70 0.019
3555 PRINTING TRADES MACHY, EQUI 11 -0.07 0.6091 *** 0.88 0.000
3559 SPECIAL INDUSTRY MACHY, NEC 63 -0.05 0.6669 *** 0.63 0.000
3560 GENERAL INDUSTRIAL MACH & E 19 0.51 0.0576 *** 0.80 0.000
3561 PUMPS AND PUMPING EQUIPMENT 3 -0.18 -0.0088 0.89 0.210
3562 BALL AND ROLLER BEARINGS 8 0.04 0.1719 *** 1.00 0.000
3564 INDL COML FANS,BLOWRS,OTH E 16 0.01 * 0.2934 *** 0.99 0.000
3567 INDL PROCESS FURNACES, OVEN 8 -0.01 0.6578 ** 0.67 0.013
3569 GENERAL INDL MACH & EQ, NEC 20 -0.47 0.8215 *** 0.89 0.000
3570 COMPUTER & OFFICE EQUIPMENT 6 22.38 0.2022 * 0.51 0.110
3571 ELECTRONIC COMPUTERS 38 3.31 * 0.1307 *** 0.17 0.009
3572 COMPUTER STORAGE DEVICES 30 -0.22 0.9367 *** 0.88 0.000
3575 COMPUTER TERMINALS 12 0.06 0.4335 *** 0.99 0.000
3576 COMPUTER COMMUNICATION EQUI 75 -1.67 *** 3.1959 *** 0.99 0.000
3577 COMPUTER PERIPHERAL EQ, NEC 72 0.12 0.3110 *** 0.72 0.000
3578 CALCULATE,ACCT MACH,EX COMP 21 0.13 ** 0.1370 *** 0.98 0.000
3579 OFFICE MACHINES, NEC 11 0.33 0.1126 *** 0.97 0.000
3580 REFRIG & SERVICE IND MACHIN 21 0.27 0.1609 *** 0.31 0.008
3585 AIR COND,HEATING,REFRIG EQ 18 0.45 0.1029 *** 0.47 0.002
3590 MISC INDL, COML, MACHY & EQ 15 0.11 0.0456 0.10 0.262
Summary of Regression Analysis aj aj_Sig bj bj_Sig R2 Mean 0.93 0.4042 0.72 Median 0.08 0.2320 0.79 Standard Deviation 4.27 0.5999 0.28 Min Value -1.67 -0.0239 0.01 Max Value 22.38 3.1959 1.00 Number of negative coefficients 9 1 2 Number of positive coefficients 19 4 26 25 Table 3.8 Revalidation of H1
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These results are completely different from those hypothesized and reported by
F&K. These results suggest that in the Capital Goods Industries (SICs 3510-3590), the
output of large firms fluctuate significantly more than that of the smaller firms in each of
these SICs. To further validate our results for H1, we evaluated the bj values using the
Wilcoxon Sign Rank Test. We took the actual bj values for each SIC and used the
Wilcoxon Signed-Rank Test for Difference in Medians and the results also suggest that bj
is positive.
Results with continuity H0 : Median of bj = 0 Correction Exact Prob Decision at Prob Decision at Ha Level 5% Level Z-Value Level 5% Level Median<>0 0.00000 Reject Ho 4.5625 0.000005 Reject Ho Median<0 1.00000 Accept Ho 4.5841 0.999998 Accept Ho Median>0 0.00000 Reject Ho 4.5625 0.000003 Reject Ho
Table 3.9 Wilcoxon Signed-Rank Test on bj
There are three possible reasons why our results for H1 are different than F&K’s
findings. First, the F&K study used only nine years of data, which may not have been a
large enough sample size to cover two business cycles. Second, our data focuses on the
capital goods industry, which has high sales variability and a preponderance of small but
less profitable firms. Third, it is non-intuitive to think that large firms will experience
smaller fluctuations in their output levels that small firms do. To clarify this further,
consider a simple example with one small firm (sales = 100) and one large firm (sales =
1,000). Assuming that they both have the same standard deviation of sales (10), then
their coefficient of variation is 0.1 for the small firm and 0.01 for the large firm. Thus,
we see no reason why such order of magnitude differences should exist across small and
large firms in any industry. Thus, we can conclusively say that, in general (after
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adjusting for CPI), the standard deviation of sales is higher for the larger firms than it is for smaller firms in the capital goods industries.
3.5.2. Revalidating H2, H3, and H4:
The regression analysis model used to test these hypotheses was formulated as follows:
RISKADJij = cj + dj * Sij + ej * SVij + fj * Sij * SVij + eij
Where: RISKADJij = the risk adjusted return on sales (ROS) which is defined as the ROS divided by the
variance of ROS for firm i in industry j over the 20-yr period. Where ROS is calculated as
average net income divided by average annual sales over the period.
Sij = size of firm i in industry j as measured by the average sales for firm i in industry j over the
20-yr period.
SVij = measure of output flexibility for firm i in industry j. This is the same measure used to
evaluate H1.
eij = error term for firm i in industry j.
F&K hypothesized · H2: dj > 0, indicates that size has a positive impact on performance
· H3: ej = 0, indicates that output flexibility has no impact on performance
· H4: fj < 0, indicates that size has a negative impact on output flexibility and performance
We analyzed this risk-adjusted measure using return on sales as suggested by
F&K. Using the risk-adjusted return on sales, our results are summarized in the following table:
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SIC SIC Name # Firms dj dj_Sig ej ej_Sig fj fj_Sig R2 P-value 3510 ENGINES AND TURBINES 7 -0.19 2.18 -0.06 0.23 0.823 3523 FARM MACHINERY AND EQUIPMEN 15 -0.36 2.70 -0.02 0.29 0.269 3524 LAWN, GARDEN TRACTORS, EQUI 2 3530 CONSTR,MINING,MATL HANDLE E 4 3531 CONSTRUCTION MACHINERY & EQ 9 0.05 2.01 -0.03 ** 0.66 0.120 3533 OIL & GAS FIELD MACHY, EQUI 13 -1.95 5.38 0.01 0.06 0.895 3537 INDL TRUCKS,TRACTORS,TRAILR 6 -0.37 -4.90 1.19 0.58 0.559 3540 METALWORKING MACHINERY & EQ 16 1.43 * 4.02 ** -0.32 *** 0.65 0.005 3541 MACHINE TOOLS, METAL CUTTIN 4 3550 SPECIAL INDUSTRY MACHINERY 7 2.35 2.11 -0.28 0.99 0.000 3555 PRINTING TRADES MACHY, EQUI 11 1.99 12.09 -5.25 0.10 0.843 3559 SPECIAL INDUSTRY MACHY, NEC 63 0.33 3.10 -0.41 0.02 0.705 3560 GENERAL INDUSTRIAL MACH & E 19 -2.62 4.64 0.20 0.16 0.444 3561 PUMPS AND PUMPING EQUIPMENT 3 3562 BALL AND ROLLER BEARINGS 8 -0.56 1.92 0.03 0.10 0.922 3564 INDL COML FANS,BLOWRS,OTH E 16 -13.39 223.27 -46.23 0.28 0.252 3567 INDL PROCESS FURNACES, OVEN 8 1.69 19.35 -7.84 0.79 0.156 3569 GENERAL INDL MACH & EQ, NEC 20 -2.75 * 17.95 ** -0.77 ** 0.32 0.115 3570 COMPUTER & OFFICE EQUIPMENT 6 -0.12 0.29 0.00 0.96 0.055 3571 ELECTRONIC COMPUTERS 38 -0.19 -0.61 0.01 0.00 0.991 3572 COMPUTER STORAGE DEVICES 30 -0.79 1.69 -0.03 0.01 0.965 3575 COMPUTER TERMINALS 12 6.57 -14.68 -0.05 0.34 0.324 3576 COMPUTER COMMUNICATION EQUI 75 1.66 -0.82 0.00 0.11 0.037 3577 COMPUTER PERIPHERAL EQ, NEC 72 -0.06 1.35 -0.01 0.31 0.000 3578 CALCULATE,ACCT MACH,EX COMP 21 9.04 -12.74 -1.20 0.13 0.475 3579 OFFICE MACHINES, NEC 11 -1.07 4.07 0.06 0.06 0.943 3580 REFRIG & SERVICE IND MACHIN 21 7.34 *** 1.30 -2.56 *** 0.43 0.025 3585 AIR COND,HEATING,REFRIG EQ 18 -14.12 -116.77 5.80 0.06 0.818 3590 MISC INDL, COML, MACHY & EQ 15 -0.02 10.00 *** -0.58 0.71 0.002
Summary of Regression Analysis dj dj_Sig ej ej_Sig fj fj_Sig R2 P-value Mean -0.24 6.76 -2.33 0.34 0.43 Median -0.12 2.11 -0.03 0.28 0.32 Standard Deviation 4.97 51.56 9.43 0.30 0.38 Min Value -14.12 -116.77 -46.23 0.00 0.00 Max Value 9.04 223.27 5.80 0.99 0.99 Number of negative coefficients 15 1 6 0 16 4 Number of positive coefficients 10 2 19 3 9 0
Wilcoxon Signed-Rank Test Results dj = 0 ej > 0 fj < 0
Table 3.10 Revalidating H2, H3, and H4 (using ROS)
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We used the Wilcoxon Signed-Rank Test for Difference in Medians with the null
hypothesis that the medians are zero for dj, ej and fj. The results for these tests are as
follows:
Results with continuity Correction Ho: Prob Decision at Median = 0 Ha Z-Value Level 5% Level
dj Median <> 0 0.2691 0.7876 Accept Ho Median < 0 -0.2691 0.3939 Accept Ho Median > 0 -0.2960 0.6163 Accept Ho
ej Median <> 0 1.8139 0.0697 Accept Ho Median < 0 1.8379 0.9670 Accept Ho Median > 0 1.8139 0.0348 Reject Ho
fj Median <> 0 1.8392 0.0659 Accept Ho Median < 0 -1.8392 0.0329 Reject Ho Median > 0 -1.8632 0.9688 Accept Ho
Table 3.11 Wilcoxon Signed-Rank Test for H2, H3 and H4
These results are significantly different from those reported by F&K. In the case
of H2 (the dj coefficient), the results are 10 positives (2 significant results) and 15
negatives (1 significant result). Using the Wilcoxon Signed-Ranked Test for all the
values of dj, we were unable to reject the null hypothesis that the median for dj = 0. From
these results, we cannot make any strong conclusions about the size of firms and their
profitability as measured by the risk-adjusted return on sales.
However, a closer look at the data suggests that the larger firms in these SICs are more profitable when measured by average return on sales. We regressed the average return on sales for all 540 firms against the independent variable (average annual sales) as a measure of firm size. The results suggest that large firms are indeed more profitable than smaller firms are in this sample of firms (bj = 0.0789 with p-value = 0.000).
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Therefore, since we are unable to make any strong conclusions using the risk-adjusted
measure of performance, we suspect that this measure may be misleading. For example,
it may be that in these SICs, the variance of the return on sales is proportionally smaller
for smaller firms than that of the larger firms. Consequently the risk adjusted return on
sales may be higher for smaller firms. But, overall, the larger firms are more profitable,
as we noted earlier. Therefore, using F&K’s measures, we are unable to make any strong
conclusions about firm size and performance.
For H3 (the ej coefficient), 19 out of 25 SICs are positive (with 3 significant results). The Wilcoxon Signed-Ranked Test supports the conclusion that the median value of ej > 0. Therefore, these results suggest that output flexibility has a positive impact on firm profitability as measured by the risk-adjusted return on sales.
For H4 (the fj coefficient), 16 out of 25 SIC were negative (with 4 significant results). The Wilcoxon Signed-Ranked Test supports the conclusion that the median value of fj < 0. These results are consistent with those found by F&K. One interpretation of the interaction term in this regression is that smaller firms, who are more output flexible, also experience higher return on sales as measured by the risk-adjusted ROS.
To summarize, in this data set, size has an unpredictable effect on performance.
The H2 result suggests that size has no impact on the risk-adjusted measure of performance. The H3 result suggests that output flexibility has a positive impact on performance. However, the H4 result suggests that small firms that are output flexible are also more profitable than the large firms are. From a financial perspective, the H2 result penalizes the large firms because they have high variability in sales, which leads to more variability (and risky) cash flows. From an operational perspective, these results
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are insightful because they suggest that small firms can better handle their variations in
sales than the large firms can.
3.5.3. Testing H5, H6, and H7
To evaluate the hypotheses at the industry level, F&K suggested that for small firms in a volatile industry, there should be strong fluctuations between size and output.
The model used to test these hypotheses is formulated as follows:
bj = cj + fj x ISVj + gj x CIj + hj x ROSj + Ej
where: bj = coefficient of size for industry j (from H1)
ISVj = output flexibility for industry j. This measure is calculated as the standard deviation of
sales for all firms in industry j.
CIj = capital intensity of the industry (assets divided by sales)
ROSj = return on sales (avg. net income divided by avg. sales) for industry j
F&K hypothesized that:
· H5: fj < 0, which measures the impact of industry volatility
· H6: gj < 0, measures the impact of capital intensity
· H7: hj > 0, measures the impact of industry profitability
The three hypotheses (H5, H6, and H7) were evaluated incrementally and the results are
summarized in the following table.
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Results of Regression with Dependent Variable bj
Indep. Coeff. Model #1 Sig. Model #2 Sig. Model #3 Sig. Variables
Constant cj 0.344 *** 0.374 *** 0.357 ***
ISVj fj -0.0098 -0.01 -0.001
CIj gj -0.023 0.0004
ROSj hj 0.056
R2 0.0323 0.0394 0.0414 P-value 0.3512 0.5930 0.7822 Table 3.12 Revalidating H5, H6, and H7
Although the results of our three models are not statistically significant, the signs of the regression coefficients agree with those found by F&K. Therefore, we report that our results for H5, H6, and H7 are consistent with those of F&K. Since our results are not statistically significant, no strong conclusions can be made from these results.
However, in the F&K paper, they interpret these results in the following manner:
The H5 (fj < 0) result suggests that in industries with high demand fluctuations, small firms are more willing to fluctuate their output than the larger firms are. Therefore, if the industry volatility (ISV) is high, large firms will more likely have low output fluctuations. The H6 result (gj < 0) indicates that capital intensity has a negative impact on output flexibility (bj). Therefore, in high capital intensity industries, the sales variability of large firms is lower. The H7 result (hj > 0) suggests that the average profit
of the industry has a positive impact on the output flexibility. Therefore, in highly
profitable industries, large firms experience high fluctuations in output.
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3.5.4. Summary of the Revalidation
Table 3.13 summarizes the results of our revalidation of F&K’s hypotheses.
Notice that the major differences in our findings are for hypotheses H1, H2 and H3.
Hypotheses Formulations and Results
H1 SVij = aj + bj*Sij + Eij
H1 suggest bj < 0 bj > 0 H2, H3, H4 RISKADJij = cj + dj*Sij + ej*SVij + fj*Sij*SVij + Eij
H2: dj > 0, size has a positive impact d = 0 on performance j
H3: ej = 0, output flexibility has no e > 0 impact on performance j
H4: fj < 0, size has a negative mediating impact on output flexibility fj < 0 and performance
H5, H6, H7 bj = cj + fj*ISVj + gj*CIj + hj*ROSj + Ej
H5: fj < 0, measures the impact of f < 0 industry volatility j
H6: gj < 0, measures the impact of g < 0 capital intensity j
H7: hj > 0, measures the impact of h > 0 industry profitability j Table 3.13 Revalidation of F&K's Hypotheses
It is important to note four key conclusions in this data set. First, the output of large firms fluctuates significantly more than that of small firms. Second, output flexibility (as measured by the standard deviation of sales) has a positive impact on performance. Third, large firms that can’t handle wide variations in sales also do not
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perform well financially. Fourth, the structure of the industry has a significant impact on output flexibility from three perspectives:
· The more volatile an industry (ISV), the lesser the chance that large firms
will have a high standard deviation in sales.
· Also, the more capital intense the industry (CI), the lesser the chance that
large firms will have a high standard deviation in sales.
· The more profitable the industry, the greater the chance that large firms
will have high standard deviation in sales.
Comparing F&K’s Hypotheses vs.Our Revalidation
Measured at the Firm level Measured at the Industry level H1: (- / +) H5: (- / -) Demand Volatility Size
H2:(+/ 0) Volume Capital Flexibility H6: (- / -) Intensity H4: (- / - ) H7: (+ / +) Profitability Profitability H3: ( 0 / + )
Figure 3.2 Summary of Our Revalidation of F&K’s Hypotheses
Figure 3.2 also summarizes our findings where (- / +) indicates the F&K’s
hypothesized relationship versus the results of our revalidation effort (F&K / our results).
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This figure shows that our findings are different from those of F&K in three instances:
H1, H2, and H3.
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3.5.5. Output Fluctuations using Mills and Schumann (1985) Procedure
The first improvement that we investigate over the F&K measure of output flexibility is by using the Mills and Schumann (1985) procedure to remove the seasonal and growth trends from the Compustat data. Mills and Schumann (1985) used a regression analysis procedure to remove the seasonal trends from the Compustat data before computing their measure of volume flexibility. Although F&K cited the Mills and
Schumann study, F&K elected to simply use the standard deviation of sales as their measure of output flexibility.
Therefore, our first task is to recompute output fluctuations using the Mills and
Schumann (1985) procedure. For each firm in each industry sector, s sales is calculated similar to the measure used by F&K (1991) and Mills and Schumann (1985), i.e. the standard error resulting from the regression of Log (CPI-adjusted sales) and a linear trend
(year).
OF @ Log (s sales)
We use the following formulation to test the hypothesis that large firms are more
output flexible than smaller firms are (Ha: bj >0):
OFij = aj + bj * Sij + Eij
Where: OFij = standard error resulting from the log(CPI-adjusted sales) for firm i in industry j.
Sij = size of firm i in industry j, measured by average annual sales over the 20-yr period.
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We present the results of our analysis in Table 3.14. These results clearly show
that bj is positive for 24 out of 28 SICs (and 7 of these results are significant). In order
to make a stronger statistical conclusion about this result, we used the Wilcoxon Signed-
Rank Test on the actual bjs and the results suggest that the median value for bj is positive.
Results with continuity Correction Ho: Prob Decision at Median = 0 Ha Z-Value Level 5% Level
bj Median <> 0 3.4954 0.00047 Reject Ho Median < 0 3.5182 0.99998 Accept Ho Median > 0 3.4954 0.00047 Reject Ho
We also noted that using the Mills and Schumann (1985) procedure to measure output fluctuations, our results were not as compelling as when we used the F&K’s method that uses the standard deviation of sales. Recall that when we used the F&K method, we found that bj was positive for 25 of 29 SICs investigated. However, when we use the Mills and Schumann (1985) procedure, we found that bj was negative for four of these SICs. One explanation for the difference in these results is that the Mill and
Schumann (1985) procedure extracts the true fluctuations of sales after accounting for the growth trend.
Nevertheless, both the number of positive bj’s and the results of the sign test
provide strongly support our hypothesis that bj is positive. Therefore, we conclude that in
this sample of firms, the output of larger firms fluctuate more than the output of smaller
firms in these industries.
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aj bj SIC SIC Name # Firms aj p-value bj p-value R2 Adj-R2
3510 ENGINES AND TURBINES 7 -4.142 0.000 -0.050 0.192 0.313 0.175
3523 FARM MACHINERY AND EQUIPMEN 15 -5.143 0.000 0.014 0.367 0.063 -0.009
3530 CONSTR,MINING,MATL HANDLE E 4 -4.855 0.063 0.002 0.987 0.000 -0.500
3531 CONSTRUCTION MACHINERY & EQ 9 -4.978 0.000 0.001 0.906 0.002 -0.140
3533 OIL & GAS FIELD MACHY, EQUI 12 -4.041 0.000 -0.030 0.618 0.026 -0.072
3537 INDL TRUCKS,TRACTORS,TRAILR 5 -5.278 0.002 0.084 0.561 0.124 -0.168
3540 METALWORKING MACHINERY & EQ 16 -5.343 0.000 0.030 0.096 0.186 0.128
3541 MACHINE TOOLS, METAL CUTTIN 4 -3.897 0.046 -0.130 0.795 0.042 -0.437
3550 SPECIAL INDUSTRY MACHINERY 7 -5.959 0.000 0.251 0.013 0.743 0.692
3555 PRINTING TRADES MACHY, EQUI 10 -5.085 0.000 0.239 0.665 0.025 -0.097
3559 SPECIAL INDUSTRY MACHY, NEC 62 -4.872 0.000 0.164 0.147 0.035 0.019
3560 GENERAL INDUSTRIAL MACH & E 18 -5.619 0.000 0.011 0.044 0.230 0.182
3561 PUMPS AND PUMPING EQUIPMENT 3 -6.876 0.072 0.657 0.349 0.729 0.458
3562 BALL AND ROLLER BEARINGS 7 -5.335 0.000 0.027 0.606 0.057 -0.132
3564 INDL COML FANS,BLOWRS,OTH E 11 -7.245 0.000 3.211 0.030 0.423 0.359
3567 INDL PROCESS FURNACES, OVEN 7 -6.407 0.001 2.318 0.547 0.077 -0.108
3569 GENERAL INDL MACH & EQ, NEC 17 -6.315 0.000 0.099 0.070 0.203 0.150
3570 COMPUTER & OFFICE EQUIPMENT 6 -5.103 0.000 0.000 0.783 0.021 -0.224
3571 ELECTRONIC COMPUTERS 36 -4.920 0.000 0.004 0.313 0.030 0.001
3572 COMPUTER STORAGE DEVICES 28 -4.789 0.000 0.050 0.262 0.048 0.012
3575 COMPUTER TERMINALS 12 -5.769 0.000 0.096 0.580 0.032 -0.065
3576 COMPUTER COMMUNICATION EQUI 70 -5.485 0.000 0.882 0.000 0.276 0.266
3577 COMPUTER PERIPHERAL EQ, NEC 68 -5.061 0.000 0.002 0.811 0.001 -0.014
3578 CALCULATE,ACCT MACH,EX COMP 21 -5.703 0.000 0.011 0.702 0.008 -0.044
3579 OFFICE MACHINES, NEC 9 -5.684 0.000 0.015 0.101 0.337 0.242
3580 REFRIG & SERVICE IND MACHIN 20 -5.861 0.000 0.086 0.243 0.075 0.024
3585 AIR COND,HEATING,REFRIG EQ 18 -5.402 0.000 0.030 0.282 0.072 0.014
3590 MISC INDL, COML, MACHY & EQ 14 -6.472 0.000 0.664 0.022 0.364 0.311 28 516
Summary of Regression Analysis aj aj_Sig bj bj_Sig R2 Adj-R2 Mean -5.42 0.312 0.16 0.04 Median -5.34 0.030 0.07 0.01 Standard Deviation 0.79 0.741 0.20 0.25 Min Value -7.24 -0.130 0.00 -0.50 Max Value -3.90 3.211 0.74 0.69 Number of negative coefficients 28 28 4 0 Number of positive coefficients 0 24 7
Table 3.14 Regression Analysis Results for Output Fluctuation
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3.6. Process Measures of Volume Flexibility
Although we have duplicated the F&K study and found significantly different
results in a subset of industries, we still contend, that F&K’s output flexibility measures
(SVij as defined earlier) essentially captures fluctuations in the environment. Our conclusion is based on the logic that if we measure the standard deviation of a firm’s
sales over a 20-yr period, the only conclusions that we can draw are those related to the
firm’s output fluctuations and not how effectively can the firms cope with these
fluctuations. Therefore, the most that we can say from our revalidation of F&K’s
hypotheses is that using 20 years of financial data on 550 firms in the capital goods
industries, we found that the output of the larger firms fluctuate more than that of the
smaller firms especially in industries with high profitability, low industry volatility, and
low capital intensity. However, if we want to test whether one of these firms is more
volume flexible than another one is, we must consider how the firms use their resources
to support these fluctuations in sales.
But, in order to effectively measure volume flexibility, one must separate the technology impacts from the environmental uncertainties (deGroote, 1994). In addition, one must account for the cost of maintaining a flexible production strategy over a range of uncertain states (Slack, 1983). Therefore, in this section, we focus on developing other measures of volume flexibility that consider the simultaneous impact of a firm’s use of resources in response to fluctuations in output.
A major challenge in developing process-based measures of volume flexibility is that of finding suitable surrogate measures of a firm’s production technology. To support
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our arguments for using these process-based measures, we begin by identifying appropriate surrogate variables.
3.6.1. Surrogate measures of Volume Flexibility
Mills and Schumann (1984) and deGroote (1994b) suggest that surrogate
measures of production technology can be operating cost and inventory levels. Also,
Jordan and Graves (1995) propose that a measure of volume flexibility is the inverse of
the joint probability of under-utilization and stockouts. Therefore, we argue that
inventory levels, production levels, and cost-of-goods sold are appropriate surrogate measures of a firm’s use of production technology.
Consequently, when we decompose the measure of volume flexibility into its separate environmental and technology components, we can derive process-based measures of volume flexibility. For example, firms that can respond to variations in sales with lower cost and/or lower inventory levels are more volume flexible than other firms are. Thus, flexibility is high if the following conditions are met simultaneously:
(a) Sales are high (fewer stockouts)
(b) Costs are low (less under-utilization)
(c) Inventory in low (under-stocking)
(d) Profitability is high (efficient response to demand changes)
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3.6.2. Flexibility Ratios
In order to measure the simultaneous effect of how a firm uses its production technology to respond to environmental diversity, we develop four measures of volume
flexibility using flexibility ratios. The concept of using ratios to measure flexibility is not
new. For example, Ettlie and Penner-Hahn (1994) propose several measures of
manufacturing flexibility to include: the ratio of the number of part types to the number
of part families and its inverse, the ratio of the number of part families to the changeover
time and its inverse, and the ratio of the number of parts types to changeover time, and its
inverse. Also, in the economics literature, in an effort to prove that the variance of
production is larger than the variance of sales, Allen (1999) suggests that the typical
measure of production smoothing is the ratio of the variance of production to the variance
of sales. But, since we are interested in how a firm uses its resources to respond to
demand fluctuations, then we will consider the ratio of sales fluctuations to different
measures of a firm’s use of resources e.g. inventory and cost-of-goods sold.
3.6.3. The Data Set
To accurately compare our process-based measures of volume flexibility to
F&K’s, we use the same data set [20 years of financial data on 550 firms in the capital goods industries (SICs 3510-3590)] that we used earlier to revalidate the F&K hypotheses. Furthermore, in the process of doing the regression analysis on these process-based measures, we performed several detailed diagnostics and remedial procedures to ensure that we met all the conditions for a valid regression in each SIC.
We analyzed the residual errors from the regressions in each SIC. We performed detailed
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diagnostic analyses using the studentized residuals and the covariance ratios to identify outliers in the data set. In some SICs validated outlier firms were subsequently omitted from the analysis in order to meet the requirements for a valid regression. We then re-ran the regressions and used the Wilk-Shapiro test to check the normality of the final residuals for each SIC. These procedures were duplicated for each measure that we tested. These regression diagnostic procedures are documented in appendix A.
3.6.4. Volume Flexibility Measures
In this section, we develop and evaluate four new process-based measured of volume flexibility. In each instance, we explain the rationale for the model then we test to see what effect the size of a firm has on each measure of volume flexibility.
To demonstrate the intuitiveness of each of these measures, we use a simple example to compare one small and one large firm as summarized in Table 3.15. Firm A is a relatively small firm with relatively low fixed cost (40% of sales), variable cost (40% of sales), inventory buffers to cover one operating quarter (25% of sales), and inventory carrying cost of 20%. Firm B is a relatively large firm with higher fixed cost (50% of sales), variable cost (20% of sales), inventory buffers to cover two operating quarters
(50% of sales), and inventory carrying cost of 20%. The average annual sales is 125 for firm A and 1,250 for firm B. The standard deviation of sales over the 3-yr period is 25 for firm A and 250 for firm B.
For example, if we measure output fluctuations as the standard deviation of sales over the 3-yr period, notice that the output of the large firm fluctuates more than that of the small firm. If we take the natural logarithm of these fluctuations, we get an output
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fluctuation (OF) measure; which is 3.22 for the small firm and 5.52 for the larger firm.
This result indicates that over this 3-year horizon, the output of the large firm fluctuates more than that of the small firm.
In the remainder of this analysis, after we present the rationale for each of these four measures (VF1-VF4), we will also continually return to this example to develop some intuitive insights from each of these process-based measures.
Small Firm (A) Large Firm (B) Periods 1 2 3 1 2 3 Sales 100 125 150 1000 1250 1500 Fixed_Cost 40 50 60 500 625 750 Var_Cost 40 50 60 200 250 300 F&V_Costs 80 100 120 700 875 1050 Inventory 25 31.25 37.5 500 625 750 Inventory_Cost 5 6.25 7.5 100 125 150 Total Cost 85 106.25 127.5 800 1000 1200 Net Income 15 18.75 22.5 200 250 300 Return on Sales (ROS) 0.15 0.15 0.15 0.2 0.2 0.2
Statistics Avg_Sales 125.00 1250.00 StdDev_Sales 25.00 250.00 Log(StdDev_Sales) 3.22 5.52 StdDev_(F&V_Cost) 21.25 200.00 StdDev_Inventory 6.25 125.00 StdDev_Net_Income 3.75 50.00
VF_Measures OF 3.22 5.52 VF1 1.39 0.69 VF2 0.22 0.36 VF3 0.18 0.15 VF4 0.026 0.030
Table 3.15 Measuring Volume Flexibility with one Small and one Large Firm
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3.6.4.1. Output Fluctuations and Inventory Buffers
If a firm is able to use its inventory buffers wisely, then it will be able to minimize s sales since a fixed safety stock (k*s sales ) would cover its sales variability. On the other hand, wide variation variations in inventory imply widely varying working capital requirements (often at higher marginal costs) or inefficient warehouse operations. Thus,
VF1 respresents a process-based measure of volume flexibility.
æ s sales ö @ Logç ÷ VF1 ç ÷ ès inventoryø
Returning to our simple example in Table 3.15, when we consider how the two
firms use their inventory buffers to support sales fluctuations, we can measure their
volume flexibility as the ratio of the standard deviation of sales divided by the standard
deviation of inventory. Notice that this measure is uni-dimensional because the standard
deviation of sales and the standard deviation of inventory are both measured in the same
units. Then, taking the natural logarithm of this measure, we get our first process-based measure, VF1. Notice that this measure suggests that the small firms are more efficient in using their inventory buffers to support their sales fluctuations (the VF1 measure for firm
A is 1.39 and for firm B it is 0.69).
To test the relationship between VF1 and the size of a firm in our Compustat data
set, we operationalized this measure in the following manner. For each firm in each
industry sector, s sales is calculated similar to the measure used by F&K (1991) and Mills
and Schumann (1985), i.e. the standard error resulting from the regression of Log (CPI-
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adjusted sales) and a linear trend (year). Similarly, we can calculate the s inventory
measure as the standard error of the regression of Log (CPI-adjusted inventory measured in $) and a linear trend (year). Hence our VF measure provides a dimensionless index that can be used to compare how firms use their inventory as a source of volume flexibility. We then use the following formulation to test the hypothesis that smaller firms are more volume flexible than larger firms are (Ha: bj <0) on this measure:
VFij = aj + bj * Sij + Eij
Where: VFij = standard error resulting from the log(CPI-adjusted sales) for firm i in industry j.
Sij = size of firm i in industry j, measured by average annual sales over the 20-yr period.
Eij = error for firm i in industry j.
We present the results of our analysis in Table 3.16. These results show that bj is
negative for 27 out of the 28 SICs (and 15 of these results are significant). In order to
make a stronger conclusion about these results, we used the Wilcoxon Signed-Rank Test
on the actual bjs and the results suggest that the median value for bj is negative (pvalue =
0.00004). Therefore, these results provide strong evidence of the use of inventory as
source of volume flexibility in small firms. These results suggest that small firms are
more efficient in using their inventory buffers to support their output fluctuations. This
result may be so because (a) small firms are exposed to lower sales variations and so can
create effective strategies for inventory control; (b) small firms do not have the resources
to sustain high variation in inventory levels; or (c) they have less capital intensity and
automation, as in job-shops, and hence they also have lower needs to buffer their process
from variations as an assembly line would.
89
aj bj SIC SIC Name # Firms aj p-value bj p-value R2 Adj_R2
3510 ENGINES AND TURBINES 7 1.609 0.027 -0.088 0.061 0.538 0.445
3523 FARM MACHINERY AND EQUIPMEN 15 0.685 0.003 -0.022 0.009 0.418 0.373
3530 CONSTR,MINING,MATL HANDLE E 4 0.884 0.071 -0.087 0.065 0.874 0.812
3531 CONSTRUCTION MACHINERY & EQ 9 0.672 0.022 -0.010 0.197 0.225 0.114
3533 OIL & GAS FIELD MACHY, EQUI 11 1.270 0.002 -0.064 0.188 0.184 0.094
3537 INDL TRUCKS,TRACTORS,TRAILR 5 0.422 0.644 -0.060 0.782 0.030 -0.294
3540 METALWORKING MACHINERY & EQ 16 0.925 0.000 -0.045 0.058 0.234 0.179
3541 MACHINE TOOLS, METAL CUTTIN 4 1.078 0.389 -0.223 0.699 0.091 -0.364
3550 SPECIAL INDUSTRY MACHINERY 7 1.194 0.000 -0.128 0.000 0.931 0.917
3555 PRINTING TRADES MACHY, EQUI 10 1.340 0.005 -0.532 0.183 0.210 0.111
3559 SPECIAL INDUSTRY MACHY, NEC 59 1.205 0.000 -0.109 0.111 0.044 0.027
3560 GENERAL INDUSTRIAL MACH & E 16 0.967 0.000 -0.008 0.001 0.582 0.552
3561 PUMPS AND PUMPING EQUIPMENT 3 0.700 0.380 -0.132 0.686 0.224 -0.552
3562 BALL AND ROLLER BEARINGS 7 0.920 0.061 -0.032 0.353 0.174 0.008
3564 INDL COML FANS,BLOWRS,OTH E 12 1.323 0.000 -0.300 0.086 0.267 0.193
3567 INDL PROCESS FURNACES, OVEN 7 1.835 0.029 -0.419 0.875 0.006 -0.194
3569 GENERAL INDL MACH & EQ, NEC 17 1.053 0.008 -0.134 0.059 0.218 0.166
3570 COMPUTER & OFFICE EQUIPMENT 6 0.335 0.361 -0.003 0.084 0.568 0.460
3571 ELECTRONIC COMPUTERS 38 1.105 0.000 -0.011 0.025 0.133 0.108
3572 COMPUTER STORAGE DEVICES 26 1.392 0.000 -0.154 0.004 0.303 0.274
3575 COMPUTER TERMINALS 10 1.164 0.000 -0.199 0.001 0.761 0.732
3576 COMPUTER COMMUNICATION EQUI 73 1.439 0.000 -0.158 0.001 0.147 0.135
3577 COMPUTER PERIPHERAL EQ, NEC 68 1.082 0.000 -0.008 0.180 0.027 0.012
3578 CALCULATE,ACCT MACH,EX COMP 20 1.102 0.000 -0.034 0.073 0.168 0.122
3579 OFFICE MACHINES, NEC 9 0.739 0.034 0.001 0.921 0.002 -0.141
3580 REFRIG & SERVICE IND MACHIN 20 1.024 0.000 -0.033 0.370 0.045 -0.008
3585 AIR COND,HEATING,REFRIG EQ 17 0.909 0.001 -0.036 0.046 0.240 0.189
3590 MISC INDL, COML, MACHY & EQ 14 1.105 0.000 -0.190 0.256 0.106 0.032 28 510
Summary of Regression Analysis aj aj_Sig bj bj_Sig R2 Adj-R2 Mean 1.05 -0.115 0.28 0.16 Median 1.08 -0.075 0.21 0.12 Standard Deviation 0.33 0.129 0.26 0.34 Min Value 0.33 -0.532 0.00 -0.55 Max Value 1.84 0.001 0.93 0.92 Number of negative coefficients 0 0 27 15 Number of positive coefficients 28 24 1 0
Table 3.16 Regression Results for VF1
90
3.6.4.2. Output Fluctuations and Cost-Of-Goods-Sold
If we want to measure how a firm uses its production technology to respond to changes in customer demand, then we must first find a surrogate measure for the firm’s production technology. As we have argued previously, the cost-of-goods-sold is an appropriate surrogate measure of the firm’s production technology (deGroote, 1994b).
Thus, we can operationalize this measure in the following manner:
æs sales ö @ Log ç ÷ VF2 ç ÷ è s CGS ø
Returning to our simple example in Table 3.15, we now consider how the two
firms use their cost-of-goods sold to support sales fluctuations. The VF2 measure (0.22
for the small firm and 0.36 for the large firm) indicates that in this case, the large firm is
more volume flexible. At first, this is surprising. However, this large firm has a higher
percentage of fixed cost and its variable cost as a percentage of sales (20%) is lower than
that of the small firm (40%). Therefore, this measure rewards a firm for responding to
sales fluctuations with a smaller variation in both fixed and variable cost. Thus, in this
instance, the large firm is able to respond to its sales fluctuations more efficiently than the
small firm can.
To test the relationship between VF2 and the size of a firm in our Compustat data
set, we operationalized this measure in the following manner. For each firm in each
industry sector, s sales is calculated similar to the measure used by F&K (1991) and Mills
91
and Schumann (1985), i.e. the standard error resulting from the regression of Log (CPI-
adjusted sales) and a linear trend (year). Similarly, we can calculate the s CGS measure as
the standard error of the regression of Log (CPI-adjusted cost-of-goods-sold measured in
($) and a linear trend (year). Hence our VF measure provides a dimensionless index that can be used to compare how efficiently firms use their production technology as a source of volume flexibility.
We use the following formulation to test the hypothesis that larger firms are more volume flexible than smaller firms are (Ha: bj <0) on this measure:
VFij = aj + bj * Sij + Eij
Where: VFij = standard error resulting from the log (CPI-adjusted sales) for firm i in industry j.
Sij = size of firm i in industry j, measured by average annual sales over the 20-yr period.
Eij = error for firm i in industry j.
We present the results of our analysis in the following table 3.17. These results
show that bj is negative for 27 out of the 28 SICs (and 15 of these results are significant).
In order to make a stronger statistical conclusion about these results, we used the
Wilcoxon Signed-Rank Test on the actual bjs and the results suggest that the median
value for bj is negative (pvalue = 0.00001). Therefore, these results provide strong
evidence of the differences between small and large firms in how efficiently they use
their production technology (measured by the cost of goods sold) as source of volume
flexibility. These results suggest that small firms are more efficient in using their
production technology to support their output fluctuations. Clearly, these results support
the Stigler’s (1939) argument that we can measure volume flexibility as the slope of the
92
firm’s average cost curve. Therefore, on this measure, we conclude that small firms are more volume flexible than larger firms are. There could be several reasons for this result:
· Small firms have less tolerance for CGS variation since they do not
normally have the line of credit to sustain wide cost variations [see
Robinson and Pearce (1984), Keats and Bracker (1988), and Dean et al
(1998)].
· Small firms enter into captive markets with fairly steady business
prospects e.g. blanket orders as opposed to open market situations (Dean
et al, 1998).
· Small firms sustain more long-term employment policies with less hiring
and firing with correspondingly less variation in CGS (Jayaram et al,
1999).
· Small firms require more skilled labor than larger firms do. Deskilling of
the workforce (e.g. assembly lines) in large firms, leads to fluctuations in
CGS (Jayaram et al, 1999).
93
aj bj SIC SIC Name # Firms aj p-value bj p-value R2 Adj_R2
3510 ENGINES AND TURBINES 7 0.277 0.013 -0.015 0.035 0.623 0.548
3523 FARM MACHINERY AND EQUIPMEN 15 0.210 0.004 0.000 0.889 0.002 -0.075
3530 CONSTR,MINING,MATL HANDLE E 4 0.099 0.403 -0.010 0.380 0.385 0.077
3531 CONSTRUCTION MACHINERY & EQ 7 0.176 0.000 -0.006 0.002 0.887 0.864
3533 OIL & GAS FIELD MACHY, EQUI 9 0.206 0.001 -0.011 0.054 0.432 0.351
3537 INDL TRUCKS,TRACTORS,TRAILR 4 0.217 0.290 -0.018 0.846 0.024 -0.465
3540 METALWORKING MACHINERY & EQ 16 0.185 0.000 -0.009 0.070 0.215 0.159
3541 MACHINE TOOLS, METAL CUTTIN 4 0.323 0.168 -0.128 0.240 0.578 0.367
3550 SPECIAL INDUSTRY MACHINERY 7 0.342 0.009 -0.031 0.076 0.499 0.399
3555 PRINTING TRADES MACHY, EQUI 10 0.389 0.112 -0.028 0.905 0.002 -0.123
3559 SPECIAL INDUSTRY MACHY, NEC 57 0.547 0.000 -0.159 0.001 0.174 0.159
3560 GENERAL INDUSTRIAL MACH & E 14 0.268 0.000 -0.080 0.036 0.316 0.259
3561 PUMPS AND PUMPING EQUIPMENT 3 0.148 0.685 0.037 0.838 0.064 -0.873
3562 BALL AND ROLLER BEARINGS 7 0.304 0.014 -0.009 0.239 0.263 0.116
3564 INDL COML FANS,BLOWRS,OTH E 12 0.365 0.014 -0.130 0.304 0.105 0.016
3567 INDL PROCESS FURNACES, OVEN 6 0.259 0.001 -0.167 0.274 0.287 0.108
3569 GENERAL INDL MACH & EQ, NEC 15 0.238 0.007 -0.064 0.215 0.116 0.048
3570 COMPUTER & OFFICE EQUIPMENT 5 0.157 0.002 0.000 0.010 0.918 0.891
3571 ELECTRONIC COMPUTERS 34 0.397 0.000 -0.003 0.014 0.174 0.149
3572 COMPUTER STORAGE DEVICES 24 0.255 0.000 -0.013 0.067 0.145 0.106
3575 COMPUTER TERMINALS 11 0.284 0.000 -0.030 0.002 0.660 0.622
3576 COMPUTER COMMUNICATION EQUI 66 0.610 0.000 -0.076 0.000 0.213 0.200
3577 COMPUTER PERIPHERAL EQ, NEC 43 0.550 0.000 -0.405 0.051 0.090 0.067
3578 CALCULATE,ACCT MACH,EX COMP 19 0.352 0.000 -0.083 0.115 0.140 0.089
3579 OFFICE MACHINES, NEC 9 0.247 0.025 -0.003 0.259 0.178 0.060
3580 REFRIG & SERVICE IND MACHIN 19 0.479 0.000 -0.229 0.111 0.142 0.092
3585 AIR COND,HEATING,REFRIG EQ 16 0.214 0.001 -0.012 0.005 0.437 0.397
3590 MISC INDL, COML, MACHY & EQ 14 0.444 0.000 -0.142 0.008 0.460 0.415 28 457
Summary of Regression Analysis aj aj_Sig bj bj_Sig R2 Adj-R2 Mean 0.30 -0.065 0.30 0.18 Median 0.27 -0.023 0.21 0.13 Standard Deviation 0.13 0.093 0.25 0.35 Min Value 0.10 -0.405 0.00 -0.87 Max Value 0.61 0.037 0.92 0.89 Number of negative coefficients 0 0 26 15 Number of positive coefficients 28 23 2 0
Table 3.17 Regression Results for VF2
94
3.6.4.3. Output Fluctuations, Inventory buffers, and Cost-of-Goods Sold
If we want to measure the combined impact of how a firm uses both its inventory buffers and its production technology to respond to changes in customer demand, then we can operationalize this measure in the following manner:
æ ö s sales VF @ Log ç ÷ 3 ç 2 2 ÷ è (s inventory) + (s CGS ) ø
Returning to our simple example in Table 3.15, we now compare the relative
efficiency of the two firms in using both their inventory buffers and their cost-of-goods
sold to support fluctuations in sales. The VF3 measure (0.18 for the small firm and 0.15
for the large firm) indicates that the small firm is more volume flexible. This is not
surprising. The small firm operates on significantly lower inventory buffers (25% of
annual sales) than the large firm does (50% of annual sales). Therefore, this measure
rewards the small firm for responding to sales fluctuations with lower fluctuations in its
resource base, as measured by inventory buffers and cost-of-goods sold.
To test the relationship between VF3 and the size of a firm in our Compustat data
set, we operationalized this measure in the following manner. Here we also extract the
standard errors as we explained in the previous two sections. Also, notice that we
utilize the relationship of the sum of the variances to calculate the combined impact due to inventory buffers and cost-of-goods-sold. We use the following formulation to test the
95
hypothesis that larger firms are more volume flexible than smaller firms are (Ha: bj <0) on this measure:
VFij = aj + bj * Sij + Eij
Where: VFij = standard error resulting from the log(CPI-adjusted sales) for firm i in industry j.
Sij = size of firm i in industry j, measured by average annual sales over the 20-yr period.
Eij = error for firm i in industry j.
We present the results of our analysis in the Table 3.18. These results bj is negative for all 28 SICs (17 significant results). In order to make a stronger conclusion about this result, we used the Wilcoxon Signed-Rank Test on the actual bjs and the results suggest that the median value for bj is negative (pvalue = 0.0000). Therefore, these results provide strong evidence of the differences between small and large firms in how they use the combined impact of their inventory buffers and production technology (measured by the cost of goods sold) as source of volume flexibility.
Overall, we can conclude that from a resource efficiency perspective, small firms operate with greater volume flexibility. These results suggest that small firms are more efficient in using their inventory buffers and their production technology (measured by
CGS) as a source of volume flexibility.
96
aj bj SIC SIC Name # Firms aj p-value bj p-value R2 Adj_R2
3510 ENGINES AND TURBINES 7 0.156 0.324 -0.027 0.040 0.602 0.522
3523 FARM MACHINERY AND EQUIPMEN 15 -0.030 0.756 -0.010 0.013 0.387 0.340
3530 CONSTR,MINING,MATL HANDLE E 4 0.009 0.953 -0.041 0.078 0.850 0.776
3531 CONSTRUCTION MACHINERY & EQ 8 0.010 0.871 -0.005 0.025 0.594 0.527
3533 OIL & GAS FIELD MACHY, EQUI 12 0.106 0.259 -0.023 0.127 0.218 0.139
3537 INDL TRUCKS,TRACTORS,TRAILR 4 0.089 0.120 -0.051 0.020 0.960 0.940
3540 METALWORKING MACHINERY & EQ 15 0.056 0.148 -0.019 0.001 0.557 0.523
3541 MACHINE TOOLS, METAL CUTTIN 4 0.081 0.831 -0.099 0.620 0.145 -0.283
3550 SPECIAL INDUSTRY MACHINERY 7 0.273 0.006 -0.074 0.001 0.917 0.901
3555 PRINTING TRADES MACHY, EQUI 10 0.309 0.196 -0.183 0.455 0.072 -0.044
3559 SPECIAL INDUSTRY MACHY, NEC 60 0.354 0.000 -0.082 0.003 0.147 0.132
3560 GENERAL INDUSTRIAL MACH & E 16 0.083 0.002 -0.004 0.000 0.875 0.867
3561 PUMPS AND PUMPING EQUIPMENT 3 0.003 0.970 -0.027 0.512 0.481 -0.038
3562 BALL AND ROLLER BEARINGS 7 0.101 0.441 -0.014 0.214 0.288 0.146
3564 INDL COML FANS,BLOWRS,OTH E 12 0.263 0.049 -0.154 0.210 0.152 0.067
3567 INDL PROCESS FURNACES, OVEN 7 0.244 0.008 -0.175 0.500 0.095 -0.086
3569 GENERAL INDL MACH & EQ, NEC 16 0.284 0.066 -0.424 0.000 0.798 0.784
3570 COMPUTER & OFFICE EQUIPMENT 6 -0.189 0.484 -0.002 0.122 0.489 0.361
3571 ELECTRONIC COMPUTERS 38 0.129 0.077 -0.005 0.006 0.195 0.173
3572 COMPUTER STORAGE DEVICES 28 0.142 0.026 -0.032 0.014 0.212 0.181
3575 COMPUTER TERMINALS 10 0.230 0.002 -0.177 0.236 0.170 0.067
3576 COMPUTER COMMUNICATION EQUI 69 0.410 0.000 -0.082 0.000 0.238 0.227
3577 COMPUTER PERIPHERAL EQ, NEC 68 0.201 0.002 -0.012 0.002 0.132 0.119
3578 CALCULATE,ACCT MACH,EX COMP 19 0.214 0.009 -0.020 0.014 0.304 0.263
3579 OFFICE MACHINES, NEC 9 0.028 0.848 -0.003 0.544 0.055 -0.080
3580 REFRIG & SERVICE IND MACHIN 20 0.157 0.034 -0.003 0.844 0.002 -0.053
3585 AIR COND,HEATING,REFRIG EQ 17 0.038 0.577 -0.020 0.002 0.495 0.461
3590 MISC INDL, COML, MACHY & EQ 14 0.249 0.007 -0.128 0.042 0.302 0.243 28 505
Summary of Regression Analysis aj aj_Sig bj bj_Sig R2 Adj-R2 Mean 0.14 -0.068 0.38 0.29 Median 0.14 -0.027 0.29 0.20 Standard Deviation 0.13 0.091 0.29 0.33 Min Value -0.19 -0.424 0.00 -0.28 Max Value 0.41 -0.002 0.96 0.94 Number of negative coefficients 2 0 28 17 Number of positive coefficients 26 14 0 0
Table 3.18 Regression Results for VF3
97
3.6.4.4. Output Fluctuations, Profitability, Inventory Buffers, and CGS
While models VF1, VF2, and VF3 address the cost implications of achieving volume flexibility, they do not explicitly address the profitability issue. Volume flexibility of a manufacturing system is "its ability to be operated profitably at different overall output levels” (Sethi and Sethi, 1990). Also, Upton (1994) defines volume flexibility as "the extent of change and the degree of fluctuation in aggregate output level, which the system can accommodate without incurring high transition penalties or large changes in performance outcomes” (Upton, 1994).
Therefore, in model VF4, we consider the profitability issue by measuring the combined impact of output fluctuations, changes in net income, fluctuations in inventory, and fluctuations in cost-of-goods sold. Thus, a firm should be considered volume flexible if it can simultaneously have high profitability, high environmental uncertainty, and low variations in inventory buffers and CGS.
One of the key challenges in developing this measure is in finding an appropriate surrogate variable to measure profitability while still having a dimensionless volume flexibility measure. We considered net income, return on sales and return on assets to measure profitability. Since there is a significant disparity in the net incomes of small and large firms in this data set, we determined that it is better to use either return on assets (ROA) or return on sales (ROS) than net income as a measure of profitability.
Then, in order to achieve a dimensionless measure, we elected to simply multiply our
VF3 measure by the firm’s ROA or ROS. Hence, if we want to capture the combined impact of the firm’s profitability (using ROS) and effectiveness in how it uses resources
98
to achieve volume flexibility, then we can operationalize this measure in the following manner:
æ ö s sales @ ROS * Log ç ÷ VF4 ç 2 2 ÷ è (s inventory) + (s CGS ) ø
Returning to our simple example in Table 3.15, we now compare the relative profitability and efficiency of the two firms in using both their inventory buffers and their cost-of-goods sold to support fluctuations in sales. The VF4 measure (0.026 for the small firm and 0.030 for the large firm) indicates that the large firm is more volume flexible.
This result is interesting. While the small firm is more volume flexible from a resource perspective, this firm is clearly not as profitable as the large firm is. The ROS for the small firm is 0.15 while the ROS for the large firm is 0.20. Therefore, this measure rewards the large firm for its overall effectiveness in achieving higher profitably while simultaneously using its resources to respond to sales fluctuations.
To test the relationship between VF4 and the size of a firm in our Compustat data set, we operationalized this measure in the following manner. As we did for the VF3 measure, we extracted (as M&S did) the standard errors for sales, inventory, and cost of goods sold. Also, for each firm in each industry sector, ROS is calculated by dividing the average net income by the average sales over the time period. Once again our VF measure provides a dimensionless index that can be used to measure volume flexibility across large and small firms.
99
We use the following formulation to test the hypothesis that larger firms are more
volume flexible than smaller firms are (Ha: bj >0) on this measure:
VFij = aj + bj * Sij + Eij
Where: VFij = standard error resulting from the log (CPI-adjusted sales) for firm i in industry j.
Sij = size of firm i in industry j, measured by average annual sales over the 20-yr period.
Eij = error for firm i in industry j.
We present the results of our analysis in the Table 3.19. These results show that
bj is positive for 22 out of the 28 SICs and that bj is negative for 6 of the 28 SICs and that
one of these results is significant. In order to make a stronger conclusion about this
result, we used the Wilcoxon Signed-Rank Test on the actual bjs and the results of this
test suggest that the median value for bj is positive.
Therefore, these results suggest that there is a difference between small and large
firms in how they use their resources to respond profitably to environmental diversity
(sales fluctuations). Clearly, the previous results show that small firms are more efficient
at using their resources as measured by the combined impact of their inventory buffers
and production technology (measured by the cost of goods sold) as source of volume
flexibility. However, our VF4 measure suggests that larger firms derive a competitive
advantage from their ability to fluctuate their output levels more profitably than smaller
firms can. On the other hand, small firms are able to compete by being more efficient in
their use of inventory buffers and production technology. The results suggest that when
all four factors are considered (output fluctuations, profitability, inventory buffers, and
cost of goods sold), large firms are more volume flexible.
100
SIC SIC Name # Firms aj aj_pval bj bj_pval R2
3510 ENGINES AND TURBINES 7 -3.370 0.020 -0.050 0.508 0.092
3523 FARM MACHINERY AND EQUIPMEN 15 -3.418 0.000 0.001 0.922 0.001
3524 LAWN, GARDEN TRACTORS, EQUI 2
3530 CONSTR,MINING,MATL HANDLE E 4 -4.027 0.022 0.051 0.465 0.286
3531 CONSTRUCTION MACHINERY & EQ 8 -3.062 0.000 -0.001 0.916 0.002
3533 OIL & GAS FIELD MACHY, EQUI 12 -1.455 0.000 -0.006 0.821 0.005
3537 INDL TRUCKS,TRACTORS,TRAILR 4 -4.295 0.080 0.078 0.809 0.037
3540 METALWORKING MACHINERY & EQ 15 0.845 0.002 0.017 0.552 0.028
3541 MACHINE TOOLS, METAL CUTTIN 4 -0.679 0.087 -0.546 0.037 0.927
3550 SPECIAL INDUSTRY MACHINERY 7 -0.673 0.379 0.074 0.556 0.074
3555 PRINTING TRADES MACHY, EQUI 10 -0.313 0.538 0.434 0.426 0.081
3559 SPECIAL INDUSTRY MACHY, NEC 60 2.072 0.000 0.005 0.160 0.034
3560 GENERAL INDUSTRIAL MACH & E 16 1.795 0.000 0.000 0.957 0.000
3561 PUMPS AND PUMPING EQUIPMENT 3 -2.683 0.101 -0.010 0.972 0.002
3562 BALL AND ROLLER BEARINGS 7 -2.607 0.001 -0.047 0.141 0.380
3564 INDL COML FANS,BLOWRS,OTH E 12 1.675 0.000 0.128 0.511 0.044
3567 INDL PROCESS FURNACES, OVEN 7 -1.059 0.237 3.056 0.398 0.146
3569 GENERAL INDL MACH & EQ, NEC 16 1.377 0.000 0.051 0.522 0.030
3570 COMPUTER & OFFICE EQUIPMENT 6 -3.481 0.006 0.003 0.259 0.302
3571 ELECTRONIC COMPUTERS 38 1.841 0.000 0.001 0.587 0.008
3572 COMPUTER STORAGE DEVICES 28 -0.526 0.000 0.021 0.322 0.038
3575 COMPUTER TERMINALS 10 -0.249 0.656 1.487 0.343 0.113
3576 COMPUTER COMMUNICATION EQUI 69 1.184 0.000 0.009 0.122 0.035
3577 COMPUTER PERIPHERAL EQ, NEC 68 0.805 0.000 0.002 0.629 0.004
3578 CALCULATE,ACCT MACH,EX COMP 19 2.852 0.000 0.011 0.769 0.005
3579 OFFICE MACHINES, NEC 9 -1.581 0.000 0.000 0.940 0.001
3580 REFRIG & SERVICE IND MACHIN 20 1.663 0.000 0.033 0.706 0.008
3585 AIR COND,HEATING,REFRIG EQ 17 0.884 0.033 0.020 0.510 0.030
3590 MISC INDL, COML, MACHY & EQ 14 -1.680 0.000 0.124 0.326 0.080 29 507
Summary of Regression Analysis aj aj_pval bj bj_pval R2 Mean -0.649 0.077 0.177 0.542 0.100 Median -0.600 0.000 0.010 0.517 0.035 Standard Deviation 2.120 0.170 0.645 0.274 0.189 Min Value -4.295 0.000 -0.546 0.037 0.000 Max Value 2.852 0.656 3.056 0.972 0.927 Number of negative coefficients 17 13 6 1 Number of positive coefficients 11 10 22 0 Wilcoxon Signed-Rank Test bj > 0
Table 3.19 Regression Results for VF4
101
3.6.4.5. Summary of Results on Process-Based Measures
The key findings of our process-based measures are as follows:
1. Small firms are more volume flexible (VF1) than large firms are when
measured by their use of inventory buffers to support output fluctuations.
2. Small firms are more volume flexible (VF2) than large firms are when
measured by their cost efficiency in responding to output fluctuations.
3. Small firms are more volume flexible (VF3) than large firms are when
measured by their combined use of inventory buffers and their cost
efficiency in responding to sales fluctuations.
4. Large firms are more volume flexibility (VF4) than small firms are when
we measure the combined impact of output fluctuations, return on sales,
inventory, and cost-of-goods sold.
5. Large firms derive their volume flexibility competitive advantage from
their ability to fluctuate their output more profitably than small firms can.
6. Small firms derive their volume flexibility competitive advantage from
their ability to use inventory buffers and production technology more
efficiently than large firms can.
102
Regression Analysis Summary of Process Measures of Volume Flexibility (# of positive and negative Bjs by VF measure) 30 25 20 15 10 5 0 OF VF1 VF2 VF3 VF4 #_Pos 24 1 2 0 22 #_Neg 4 27 26 28 6 #_Pos-Sig 7 0 0 0 0 #_Neg_Sig 0 15 15 17 1 # of Bjs
Figure 3.3 Summary of Process-Based Measures using 550 Firms in 28 SICs
3.6.5. Evaluating Process-based Measures with a Larger Sample Size
In the previous section, we used a sample of 550 firms in the capital goods industries to compare our process-based measures to F&K’s findings. In an effort to improve the generalizability of our results results on our process-based measures of volume flexibility, we decided to acquire and test our hypotheses on a much larger sample of firms from the Standard and Poors Compustat business database. The new sample consists of 2100 firms in 97 industries (including 2800s, 3400s, 3500s, 3600s and
3800s). The list of SICs included in the analysis is summarized in Appendix A.
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Here is a breakdown of this new sample of firms using average annual sales as a measure of size.
Avg. Sales ($M)
Data 0-50 50-100 >100
# Firms 1,262 229 608
Avg_#_Employees 170 790 14,000
Avg_Sales($M) 14.02 68.43 1,623.63
Avg_Net_Income($M) -1.14 2.32 79.97
Avg_Asset($M) 16.50 65.13 1,674.41 Table 3.20 Distribution of Firms by Avg. Annual Sales ($M)
Here is a breakdown of the distribution of firms using Avg. Number of
Employees as a measure of size of the firm:
Number of Employees
Data 0-500 500-1000 >1000
# Firms 1,287 215 597
Avg_#_Employees 150 710 14,350
Avg_Sales($M) 19.75 79.74 1,638.13
Avg_Net_Income($M) -0.73 4.27 79.97
Avg_Asset($M) 23.04 77.37 1,687.6 Table 3.21 Distribution of Firms by Number of Employees
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3.6.5.1. Data Analysis
In general, we duplicated our analytical methods that we followed in the previous sample of 550 firms in 28 SICs. However, since we had accomplished detailed diagnostics and outlier remedial tests to ensure that our results met all the basic requirements for a valid regression, we now elected to simply run the regressions for the new sample of 97 SICs and record the bj coefficient. The detailed results are listed in
Appendix A.
3.6.5.2. Summary of Results using 90 SICs and 2100 Firms
Using the sample of 2100 firms, the results were quite similar to those of the previous sample of 550 firms in the 3500 SICs. These results are summarized in Figure
3.10. Also, the key findings from the previous sample of 550 firms were also supported by the results from the new sample of 2100 firms. The key findings are:
1. Using F&K’s procedure (OF1), the output of large firms fluctuates more than
that of small firms. The regression coefficient (bj) was positive for 89 of the
90 SICs tested, and 82 of these were significant.
2. Using Mills and Schumann’s (1985) procedure (OF2), the output of large
firms fluctuates more than that of small firms. The regression coefficient (bj)
was positive for 82 of the 90 SICs tested, and 22 of these were significant.1
1 A firm that has increased sales over the last 20 years will inherently have high sales variance. Since a small firm grows over time to become a large firm, any longitudinal analysis of sales fluctuations versus size would suggest that large firms have greater fluctuations in output. The results in item1 are thus biased. Therefore, when we remove the growth trend using the Mills and Schumann (1985) procedure, our results are somewhat weaker.
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3. Small firms are more volume flexible (VF1) than large firms are when
measured by their use of inventory buffers to support output fluctuations. The
regression coefficient (bj) was negative for 87 of the 90 SICs tested and bj was
significant in 43 of these SICs.
4. Small firms are more volume flexible (VF2) than large firms are when
measured by their cost efficiency in responding to output fluctuations. The
regression coefficient (bj) was negative for 73 of the 90 SICs tested and bj was
significant in 17 of these SICs.
5. Small firms are more volume flexible (VF3) than large firms are when
measured by their combined use of inventory buffers and their cost efficiency
in responding to sales fluctuations. The regression coefficient (bj) was
negative for 83 of the 90 SICs tested and bj was significant in 33 of these
SICs.
6. For the VF4 measure, there were 54 positives (44 significant) and 36 negatives
(20 significant). These results (particularly the the significant findings)
suggest that bj is positive. These results were consistent with those from the
previous sample of 550 firms. Therefore, we conclude that large firms are
more volume flexible than small firms are when we measure the combined
impact of output fluctuations, ROS, inventory, and cost-of-goods sold.
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Regression Analysis Summary (90 SICs, 2100 Firms) of Process-based Measures of Volume Flexibility (# of positive and negative Bjs for each VF measure)
100
80
60
40
20
0 OF1 OF2 VF1 VF2 VF3 VF4 #_Pos 89 82 3 17 7 54 #_Neg 1 8 87 73 83 36 #_Pos-Sig 82 22 0 2 0 44 #_Neg_Sig 0 0 43 16 33 20 # of Bjs
Figure 3.4 Summary of Process-based measures using 2100 Firms in 90 SICs
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Limitations and Extensions of this Analysis
In reviewing this analysis, there are a few limitations that should be mentioned.
First, we know that there is a high correlation between sales and cost-of-goods sold and also between sales and inventory levels. These relatively high correlations make the use of the ratios (ssales / sCGS) and (ssales / sInv) somewhat complex. However, we have no theoretical basis to assume that since sales and CGS are correlated, so will ssales and sCGS
2 2 or ssales and sInv. Second, (sCGS + sInv ) are difficult to interpret since CGS and
Inventory levels may also be highly correlated.
One possible extension of this work is to use the theoretical basis of the inventory
balance equation (Holt et al, 1960) to capture the true relationship between the variance
of sales and the variance of inventory. For example,
If Pt = St + It - I t-1
where
Pt = amount produced in period t
St = amount sold in period t
It = inventory remaining at the end of period t
I t-1 = inventory remaining at the end of period t-1
Then, Pt = St + DIt (1)
Var (Pt) = Var (St) + Var (DIt) + 2*COV (St , DIt) (2)
Thus, we suggest that measuring the variance in production may be a better way to capture the true relationship between these variables. If firms use inventories to smooth production, then production should vary less than sales. For the variance of
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production to be less than the variance of sales, the covariance of sales and the change in inventories, COV (St , DIt) must be negative and greater in absolute value than half the variance of inventories. Allen (1999) shows that empirical testing of this hypothesis has yielded mixed results. Using a simple ratio of the variance of production to the variance of sales, several researchers have found a ratio greater than 1.0. These findings contradict the theory that the variance in production should be less than the variance in sales. However, Allen(1999) argues that to observe the differences between the variance in production and sales, the data must be adjusted to remove seasonal trends.
But, even with equation (2), we have still not accounted for the cost of being able to make the necessary changes in production and inventory levels in order to respond to demand fluctuations. Hence, we suggest that even if one uses the variance in production to measure volume flexibility, one will still need to capture the tradeoffs involved in exercising a volume flexibility strategy.
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Chapter 4. Case Studies
The results from our empirical analysis suggest that small firms have a distinct
competitive advantage in three of the four process-based volume flexibility measures.
However, when we simultaneously measure (VF4) environmental diversity (using sales fluctuations), technology (using inventory buffers and CGS), and performance (using
ROS), we find that on this measure large firms are more volume flexible. Since these
results are significantly different from the findings of previous researchers [(Fiegenbaum
and Karnani (1991), and Suarez et al (1996)], we believe that the underlying dynamics of why and how firms develop and execute a volume flexibility strategy is still not well understood. Therefore, we have elected to use the case study method as the second research methodology in a triangulated study of the construct, Volume Flexibility.
The purpose of this case study is to attempt to overcome some of the natural
limitations of pure empirical methods based on rationalist perspectives (Meredith, 1998).
The literature suggests that there are many tradeoffs between rationalist and case/field research studies. The former tends to focus on explanation/prediction (what?) while the latter is more concerned with understanding (how? and why?). Also, McCutcheon and
Meredith (1993) suggest that case research typically employ triangulation, using multiple data sources and analytical techniques to improve the representational accuracy of the resulting theory.
Therefore, we use the case study method in order to gain some insights into why and how firms use volume flexibility. In this effort, we analyze three firms in order to gain some insights into why and how these firms develop and maintain a volume
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flexibility strategy. We have documented in rich detail the context and background of the drivers and sources of volume flexibility within these firms.
4.1. Measuring Volume Flexibility within the Firm
In this study, we defined Volume Flexibility of a manufacturing system as: “the ability to profitably increase or decrease aggregate production (output) in response to changes in customer demand.” Volume flexibility directly affects customers’ perceptions by preventing out-of-stock conditions for products that are suddenly in high demand (Hayes and Wheelwright, 1984). Volume flexibility has also been shown to have a significant impact on the firm’s performance (Vickery et al, 1999).
But, to successfully measure this construct, Slack (1983) suggest that flexibility must be defined by three basic elements (range, cost, and time): range of possible states the system can adopt, cost of moving from one state to another, and the time required to move from one state to another. Similarly, Upton (1994) suggests that there are three basic elements of manufacturing flexibility: range (possible states), mobility (ease of movement between states), and uniformity (similarity of performance within a given range).
The case study method presents a challenge in that it is difficult to compare self- reported perceptual measures of volume flexibility within the firm. Therefore, in order to operationalize a definition of volume flexibility that allows us to compare self-reported measures of volume flexibility, we use Slack’s (1983) framework to define the range, cost, and time as shown in the following table:
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Criteria Definition Range Percent by which output (measured in dollars) can be increased or decreased over current levels within a specific time period. To compare firms, the range is divided into three groupings: 1. 0 to 24% (small changes in output) 2. 25 to 50% (medium changes in output) 3. ± 51 to 100% (large changes in output) Cost Percent increase or decrease in resources (measured in dollars) required to achieve the range changes. To compare firms, the cost is divided into three groupings: 1. 0 to 24% (small changes in cost) 2. 25 to 50% (medium changes in cost) 3. 51 to 100% (large changes in cost)
Time Divided into three possible periods: 1. Short-run (less than 3 months) 2. Mid-term (3 to 6 months) 3. Long-term (more than 6 months)
Table 4.1 Defining Range, Cost, and Time for Volume Flexibility
Using these definitions of range, cost and time, we consider a manufacturing firm to be volume flexible in the short-run if it can increase or decrease its output by at least
25% within a 3-month timeframe, and simultaneously, this firm can also remain profitable (after considering the costs of adjusting to this new output level). In comparing one firm against another, we consider one firm to be more volume flexible than another one is if this firm can increase or decrease its output by a larger range and at a smaller cost over a given time frame.
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4.2. Case Study Methodology
The case study methodology has its roots in the social sciences for example,
Margret Mead’s famous studies of Pacific Island societies. The case study approach has been used to accomplish many aims: to provide description, test theory, or generate theory. However, this methodology is relatively new to the management science disciplines and case and field research studies continue to be rarely published in operations management journals (Meredith, 1998). Our purpose for using the case research method is to build theory, not to test it.
Eisenhardt (1989) suggest that building theory from case study research is most appropriate in the early stages of research on a topic or to provide freshness in
perspective to an already researched topic. Handfield and Melnyk (1998) argue that to
successfully turn empirical generalizations into theory, the researcher should use
selection criteria to determine whether the relationships investigated are interesting,
plausible, consistent, or appropriate. These authors also provide further support in the
literature for these criteria as follows:
1. ‘That’s Interesting’ (Davis, 1971) -- is the relationship not obvious at first?
2. ‘That’s Connected’ (Crovitz, 1970) -- are the events related when others
have assumed that they are unrelated?
3. ‘That’s Believable’ (Polanyi, 1989) -- is the relationship convincing?
4. ‘That’s Real’ (Campbell, 1986 and Whetten, 1989) -- Is the relationship
useful to managers?
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Therefore, we have chosen to use this case study methodology in order to advance theories on (1) the factors that drive firms to adopt a volume flexibility strategy and (2) the sources of volume flexibility within the firm. In selecting these factors we will be guided by the criteria suggested by Handfield and Melynik (1998), as outlined above.
The literature on case study research suggests several different approaches for conducting a case study. For example, McCutcheon and Meredith (1993) outline a procedure for conducting case research and address case study design, data analysis and the philosophical rationale for the methodology. They suggest that if case studies are done appropriately, using more paradigms and varying forms of data, then these case studies can provide discoveries not possible through different means. Ellram (1996) argue that there is a lack of understanding of when and how to use the case study methodology in business research. Ellram provides a detailed example by showing how Yin’s (1994) framework can be used for effective research design, development and analysis in business management research. However, perhaps Eisenhardt’s (1989) provides the most detailed description of the process of conducting case study research. Eisenhardt provides a detailed framework that can be used to develop theory from case studies by identifying many of the key issues that should be addressed from specifying the research questions to reaching closure. Therefore, we decided to adopt Eisenhardt’s framework for conducting these case studies.
Using Eisenhardt’s framework we developed a methodology for conducting these case studies by addressing the nine key steps as shown in Table 4.2.
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Case Study Methodology Adapted from Eisehardt (1989) Steps Comments
1. Definition of Are small firms more volume flexible than large firms are? research question
2. Objectives This case methodology will seek answers to two specific questions:
1. What are the drivers or key factors that influence small and large firms to adopt a volume flexibility manufacturing strategy?
2. What are the sources of volume flexibility within small and large firms?
3. Selecting cases We developed a research design that classified manufacturing firms in two dimensions (Figure 4.1). We identified four quadrants and described the type of firms that would meet the criteria for each quadrant. Using an opportunity sample, we selected two small firms and two large firms in the local area.
4. Crafting Instruments We developed and pilot-tested an 8-page survey instrument and Protocols (Appendix B) to address the major structural and infrastructural issues on volume flexibility. Case study data was collected using three sources (1) survey instrument, (2) interviews, and (3) observations.
5. Entering the Field We contacted each of the firms via telephone and sought agreement for the study. Then, we proceeded to gather data using the survey, interviews and observation. The target respondent in each firm was the manufacturing or operations manager. Multiple respondents were also interviewed to get a richer understanding of the issues involved.
6. Analyzing the data We compared the different approaches that firms (in each quadrant of our research design matrix) use to achieve volume flexibility.
7. Shaping hypotheses Iterative tabulation of evidence for the drivers and sources of the volume flexibility construct to improve definition, validity, and measurability.
8. Enfolding literature Comparisons with similar and conflicting literature to sharpen generalizability and improve construct definition.
9. Reaching closure When iterative tabulations yield marginal returns.
Table 4.2 Case Study Methodology
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As summarized in Table 4.2, the purpose of the case studies is to develop theory and to provide a richer understanding of the drivers and sources of volume flexibility within firms.
However, the research design to address this question using case research presented a significant challenge considering such issues as the population of firms, the sample size required for generalizability of the study, and the realities of conducting such a study within a reasonable timeframe. As much as we would have liked to use a longitudinal analysis of a few firms to mimic our results of Chapter 3, time constraints led us to believe that a cross-sectional study using in-depth interviews would give us
enough insights about the why and how questions on volume flexibility.
Since the case studies are only one leg of a triangulated study on volume
flexibility, we employed a research design that classified manufacturing firms in two
dimensions (Figure 4.1). We used the Boynton, Victor and Pine (1993) product-process change matrix to segregate manufacturing firms into four categories. We defined the four quadrants and described the type of firms that would meet the criteria for each quadrant.
Then, using an opportunity sample, we selected firms in the local area.
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Case Study Research Design adapted from Boynton, Victor and Pine (1993)
Dynamic Mass Product Innovation Customization (hyper-competition)
Firm C Firm A
Mass Continuous Production Improvement (JIT) Product Change
Stable (not done) Firm B
Stable Process Change Dynamic
Figure 4.1 Case Study Research Design
Each of the cases was done using a four-phase approach for data collection. Phase
one begins with the survey instrument (Appendix B). Ideally, the target respondent
(manufacturing/operations manager) was asked to complete the questionnaire before our
first working meeting. In phase two, we met with the respondent to discuss the volume
flexibility issues that the questionnaire identified. This meeting typically took about one
to two hours and was ideally held at the respondent’s location where we could become acquainted with the operations and document our observations. In the third phase, we wrote up the case and identified areas where additional information was needed. In the fourth phase we followed-up on specific issues and provided feedback to the firm. For participating in the case study, we provided the firm a synopsis of the study and some benchmark data.
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4.3. The Case Study Questionnaire
After conducting a detailed survey of the operations strategy literature to determine the drivers and sources of volume flexibility (see Tables 4.3 to 4.6), we used this information to identify scaled items that assess the extent of volume flexibility usage within the firm. This detailed questionnaire is listed at Appendix B. Overall, this questionnaire employed several methods to solicit specific information on the drivers and sources of volume flexibility and how they are used within each firm. For example, we used a variety of scaled items based on 5-point Likert scales, item rankings, and written comments in order to capture the rich context and background of volume flexibility issues within the firm. The questionnaire was then used to streamline the interview process during each case study. Ideally, the questionnaire was given to each respondent prior to the interview and then we spend 1-2 hours discussing the context of volume flexibility issues in the following areas:
1. Demographic Data 2. Assessing the competitive environment 3. Forecasting demand 4. Measuring delivery performance 5. Measuring overall performance 6. Impact on marketing strategy 7. Business strategy decision-making 8. Manufacturing strategy 9. Manufacturing planning and control 10. Workforce development and usage 11. Equipment and technology 12. Networks and strategic alliances 13. Short-term sources of volume flexibility 14. Mid-term sources of volume flexibility 15. Long-term sources of volume flexibility
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4.4. Company Profiles
In this section we provide a short description of each of the companies that we analyze in this case study. Since the focus of the case studies is on the theoretical development of volume flexibility as opposed to confirmation of specific relationships, we have chosen not to focus on the detailed performance data on each of these firms.
Instead, we present a brief description of each company in order to facilitate the
interpretation of the reported results in the following sections.
Company Ownership Sales Employees Primary Products & Structure Processes A. Cables-To-Go · Private $40M 160 · Product: computer cables and connectors. Standard · Founded in 1985 products with many new and emerging product families · Primary type of sales: catalog · Process: assembly and fabrication at four locations B. United Air · Public-division of $48M 300 · Product: industrial and Specialists a large American environmental filtration corporation systems. · Founded in 1966 · Primary type of sales: catalog and customization · Process: standard designs, customization, fabrication and assembly within a product-focused plant C. Rotex · Private- owner $17M 120 · Product: industrial managers screeners and separators · Founded in 1844 · Primary type of sales: customization · Process: design, customization, fabrication and assembly within a product-focused plant Table 4.3 Company Profiles
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Company A is a manufacturer of standard and custom computer cable products
specializing in cable connectors, data switches, fiber optic multiplexers, twisted pair
cables, network gateways and surge protectors with noise filters. These products are sold
to several customers including resellers, OEMs and retail customers, mostly through
catalogs. Since its formation in 1985, the company has grown steadily. Recent
acquisitions include ASP Computer Products Inc., Pen Cabling Technology, and CTG
International. The company employs 160 employees with reported annual sales of $36M
in fiscal year 1997. The company has operations at four locations: Dayton, Ohio; San
Jose, California; Salt Lake City, Utah; and Taiwan. The plant in Dayton, Ohio primarily
does assembly of specialty items. Orders for large volumes are handled through the plant
in Taiwan. The plant in Salt Lake City, Utah is ISO 9002 certified and handles orders for
OEM customers. At the Dayton plant, the actual manufacturing processes are based
primarily on manually controlled systems with relatively low technology (disconnect
technology, termination technology, crimping, and soldering). More sophisticated
technology is available at the plants in Utah and Taiwan. Transcripts and further details
of Firm A can be found at Appendix B. Transcripts and further details of Firm B can be
found at Appendix B.
Company B manufacturers seven different types of products: industrial air
cleaning systems (SMOG-HOG® and DUST-CAT®), industrial air pollution control systems (SMOG-HOG®), industrial dust collection systems (DUST-HOG®), commercial air cleaning systems (SMOKEETER®), heat recovery ventilators (FRESH-X-
CHANGER®), electrostatic oil cleaning systems (KLEENTEX®), and electrostatic liquid coating systems (TOTALSTAT ®). The technologies that are used in these products
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include air handling and control systems, air filtration, electrostatic air precipitation,
ferromagnetics, and electrostatic liquid applications. The plant utilizes state-of-the-art
CAD systems and DNC machine tools (turret and break press), various skilled craftsmen
(e.g. machine operators, welders and electricians), various paint application systems, manual assembly, and packaging. During our visits to the plant, we noticed the variety of activities (incoming materials, manufacturing, warehousing, and shipping) that are accomplished within a relatively small space. Very little finished goods were kept inventory.
Company C manufacturers three types of products: normal capacity screeners/separators, high capacity screeners, and particle analyzers. The principal product produced at this plant is a series of over 100 industrial screeners or separators.
These products are used for sifting and screening in a wide range of applications including chemical and food processing, petrochemicals, agribusiness, minerals, and primary metal processing. For each of these applications, the products are customized to customer specifications based on the input and output feed rate, size of the screening area, type of screening materials, number of screening levels required (up to five), temperature requirements, and, commercial sanitary designs. The screeners process hundreds of materials, such as fertilizer, coffee, cereals, dextrose, roofing granules, pharmaceuticals, sugar, salt, and grains. During our visit to the plant, we witnessed the materials used, the workmanship, the variety of products and the extent of customizations that the company offers along its product line. This plant utilizes state-of-the-art DNC machine tools, laser cutting equipment and CAD systems. We also noticed the relatively low levels of raw material inventories and work-in-process inventories. Finished goods
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were not kept inventory. There were also separate inventories of spare parts that are used
to service installed equipment at customer sites. Transcripts and further details of Firm C
can be found at Appendix B.
4.5. Drivers of Volume Flexibility
Our first objective in these case studies is to determine the factors that drive firms
to adopt a volume flexibility strategy. Vickery et al (1999) define a volume flexibility
strategy as one that “directly impacts customers’ perceptions by preventing out-of-stock conditions for products that are suddenly in high demand.” These authors suggest that a volume flexibility strategy may require close coordination between a manufacturer and its suppliers, especially in the face of increasing demand. From our analysis of the literature, it is clear that environmental uncertainty provides a foundation for the factors that drive the need for volume flexibility. The basic argument is that the various dimensions of manufacturing flexibility are essentially options that firms may use in response to uncertainties in the business environment. Uncertainty requires flexibility in response in order for a firm to be both efficient and effective in the market. Flexibility should enable a manufacturer to respond quickly and efficiently to dynamic market changes.
The need for volume flexibility occurs in response to uncertainty in the amount of customer demand (Gerwin, 1993). Other authors have shown that in response to demand uncertainty, firms may also use product mix flexibility and new product flexibility
(Suarez et al, 1996). Thus, in our search to determine the drivers of volume flexibility, we are aware that there are other options available to the firm. Therefore, we must
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carefully assess the factors that lead to demand uncertainty in order to determine which of these factors are the drivers of volume flexibility.
We began our search by surveying the extant literature and summarizing the current knowledge on the factors that create environmental uncertainty. D’Aveni (1995) describes the hyper-competitive forces that characterize the most heightened state of uncertainty in the business environment. In a hyper-competitive environment, market stability is threatened by short product life cycles, short product design cycles, new technologies, frequent entry by unexpected outsiders, repositioning by incumbents, and radical redefinition of market boundaries as diverse industries merge. Other authors have attempted to measure environmental uncertainty. For example, Miller and Droge (1986) identified five dimensions of environmental uncertainty: volatility in marketing practices, product obsolescence rate, unpredictability of demand and taste, and changes in product and service delivery modes.
Therefore, using this existing knowledge of the various dimensions of environmental uncertainty, we then surveyed the literature and found support for many of the factors that drive a firm to adopt a volume flexibility strategy. Table 4.4 summarizes our assessment of the current knowledge on the drivers of volume flexibility. In the remainder of this section we address each of these drivers of volume flexibility and document how these drivers impact the three companies in this case study.
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Drivers of Comment References volume flexibility Forecasting Inaccurate forecasts drive need for VF Raturi et al (1990), Aldrige and inaccuracy Betts (1995) Customer Different customer segments drive Miller and Roth (1994), Bozart segmentation different needs for VF (e.g. OEMs, and Edwards (1997), Safizadeh wholesale, retail, end-users) and Ritzman (1997) Variability in order Growth in volume is positively related Hayes and Wheelwright (1984), volume to VF Skinner (1974), and Hill (1989), Cox 91989), Fiegenbaum and Karnani (1991) Delivery lead time Delivery time is inversely related to VF Stalk and Hout (1990), Blackburn (1991), Wacker (1996) Responsiveness Responsiveness (delivery reliability) is Blackburn (1991), Vickery positively related to VF (1993) Product life-cycle Products with shorter life cycles require Hayes and Wheelright (1979), higher VF Hayes and Pisano (1996) Hyper-competitive Hyper-competitive forces are (new D’Aveni (1995) forces technologies, short product design cycles, higher levels of uncertainty, dynamism, heterogeneity of the players, and hostility) Increasing number of competitors and low barriers to entry create a need for VF Product Requires both product mix and volume McCutcheon et al (1994), customization flexibility Gilmore and Pine (1993), Safizadeh and Ritzman (1997) Core competency Supports an outsourcing VF strategy Prahalad and Hammel (1994), strategy for products Witney (1995) Lead factory strategy Drives the firm to acquire off-shore Ferdows (1997) capacity ex-ante to create new products/processes
Table 4.4 Drivers of Volume Flexibility
4.5.1. Forecasting Inaccuracy
There is a broad stream of literature that documents the challenges that companies face in the build-to-forecast environment. The greater the challenges in forecasting the
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demand for its products, the more a company will be driven to adopt a volume flexibility
manufacturing strategy. For a detailed discussion of these issues, see Raturi et al (1990).
Our concern in this case study is to find evidence within these three companies to support
the notion that forecasting inaccuracy is a driver of volume flexibility.
Company A, operates in the highly volatile computer industry where product life
cycles are measured in months. In this environment, demand forecasting is a significant
challenge at the CTG plant in Dayton, Ohio. According to the operations manager, forecast for the past few years have either been over or short. The forecasts are developed internally by the sales and marketing team who collaborate very closely with their customers. These forecasts are projected for a six month planning horizon and updated weekly. But, as the operations manager sees the problem, the company is driven to be flexible because the sales forecast can be very unreliable.
At company B, forecasting is done using a three-year business plan and a rolling twelve-month planning horizon. However, all of the managers expressed serious concerns about the effectiveness and reliability of the forecast. The VP of sales summarized the forecasting challenge in three areas: seasonality of capital equipment, the variety of capital budgeting approval processes that the customers must overcome, and the reliance on manufacturing representatives for forecasting information in the industrial segment. The VP of Marketing summarized the challenges in forecasting as being caused by limited direct presence in the industrial segment, reliance on sales leads in the customer segment, and the consolidation of the entire forecast into monthly and annual sales dollar targets rather than by product or process. The Operations managers
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expressed concern that the inability to forecast essentially leads to a 3-4 week planning
horizon that is difficult to manage and execute efficiently.
In Company C, forecasting is done on two different time horizons (12-month and
3-year business plan). The VP of Operations and the Quality/Service manager both expressed some concern that it is rather difficult to forecast their products. Their basic perspective is that “sales forecasts are either lucky or wrong”. We agreed that I should follow-up with the VP of Marketing and a separate interview was arranged for this purpose. In my follow-up meeting with the V.P. of Marketing, he also expressed his concerns about the difficulties in trying to forecast demand. The regional marketing managers collaborate very closely with the 150 manufacturing representatives to develop a 3-year business plan and a rolling 12-month sales forecast. The 12-month rolling forecasts are updated on a 30, 60, and 90-day horizon. At the aggregate level, the company generally meets its forecast targets. However, the main challenge is in forecasting the mix of products required. Some evidence of this problem can be seen in the case of screener products of the size of over 100 SF, (referred to as MegaTex). In past years, the company has sold a relatively high volume of these products; however, this current year, the company will ship less than 10 of these products.
Clearly our findings in these three companies provide support for the notion that forecast inaccuracy is a driver of volume flexibility. Thus, we present our first proposition:
Proposition 1: The greater the inaccuracy of the sales forecasts, the more the company will be driven to adopt a volume flexibility manufacturing strategy.
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4.5.2. Customer Market Segments
There is some evidence in the literature to suggest that differences in customer segments drive the need for volume flexibility. Consequently, the more segments that a company attempts to serve, the more this company will be driven to adopt a volume flexible strategy. For a detailed discussion of these arguments, we cite the work of Miller and Roth (1994) and Safizadeh and Ritzman (1997). For example, Miller and Roth
(1994) used data from the 1987 Manufacturing Futures Survey to identify three basic clusters of firms based on their competitive capabilities and the customers they serve.
Safizadeh and Ritzman (1997) looked at the relationship between performance drivers and process choice. These authors show that the characteristics of the markets served by a manufacturing firm are the primary determinants of process choice. Thus, using these ideas from the existing literature, we analyzed these three companies and found the following evidence:
Company A, operates in three market segments: resellers, OEMs and retail.
These different market segments clearly create a competitive need for volume flexibility.
For example, the cabling industry is filled with companies that build custom cable assemblies for industry OEMs. Most of those suppliers have only one core capability, making them attractive in very specific situations. There are the well-established ISO- certified assembly houses and the "garage shop" price leaders. There are the hungry newcomers looking to gain a foothold, and the industry powerhouses who command the highest prices for their vast experience. There are some that invest very little in technical resources and others that are burdened with the cost of an extensive arsenal of engineering personnel. For many, their focus is creating new cable designs quickly with
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complete prototyping, while others rely on their low cost manufacturing capability.
Through its recent acquisitions Company A has gained the capability to meet the needs of
different market segments. Since its formation in 1985, the company has grown steadily.
Recent acquisitions include ASP Computer Products Inc., Pen Cabling Technology, and
CTG International.
Company B competes in three main segments (industrial, commercial and the
electrostatic spray systems segments). The nature of competition and the types of
customers vary by market segment. For example, a competitor dominates the industrial
segment and in order to effectively compete in this market segment, Company B relies on
niche-marketing opportunities that result in mass customization and product proliferation.
The company has made some attempts to benchmark itself against the competition. In
the $100M industrial segment, the company ranks second for the dust collection product;
but, a competitor dominates 90% of the market. For the air pollution product, the
company has strong brand recognition and ranks #1. The commercial segment is very
competitive and there are no clear market leaders. In the electrostatic liquid application
segment, there are three major players and the company ranks #1. The varying nature of
competition in each market segment drives the company to adopt a more flexible
manufacturing strategy.
Company C competes in five market categories dictated by application: chemical,
plastics, food, mineral, and agribusiness. Each of these application areas is divided into
approximately 40 different subcategories. The company has made some attempts to
benchmark itself against the competition. In each of the five major application markets
(chemicals, plastics, food, minerals, and agribusiness), the company’s dominant position
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is #2, #1, #1, #4, and #1 respectively. Compared to 35 major competitors, the company
ranks 2nd in customer brand awareness (35%) compared to the other major competitor
(45%). However the brand recognition for the rest of the competitors falls to 15%.
Therefore, the company is in a very strong competitive position. The operations manager attributed this result to the fact that the company adopts a flexible manufacturing strategy.
Our findings in these three companies provide support for the notion that
differences in customer market segments is a driver of volume flexibility. Thus, we
present our second proposition:
Proposition 2: The greater the number of market segments served, the more the
company will be driven to adopt a volume flexibility manufacturing strategy.
4.5.3. Variability in Order Volume
There is ample evidence in the literature to suggest that variability in output is a driver of volume flexibility. For a detailed discussion of these arguments, see
Fiegenbaum and Karnani (1991) and Cox (1989). Other researchers have also investigated this phenomenon and here we cite the work of Hayes and Wheelwright
(1984), Skinner (1974), and Hill (1989). For example, Hayes and Wheelwright (1984)
suggest that the variability of order volume in a highly cyclical industry drives the need
for volume flexibility. A volume flexible capability allows a firm to “accelerate or
decelerate production very quickly and juggle orders so as to meet demand for unusually
rapid delivery.” Therefore, using these arguments, we analyzed our three companies and
found the following evidence:
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In Company A, the operations manager expressed major concerns about the
variability in the orders that they receive. The variability in order volume also
complicates the task of forecasting demand. This company operates in both the retail and
industrial customer segments. The different markets create a competitive need for
volume flexibility with the OEMs wanting large truckload shipments within a fixed
delivery window and the retail customers wanting small replacement batches delivered
quickly and reliably. Therefore, the operations manager says that his function must be
flexible in order to respond efficiently to the variability in customer orders.
In Company B, we found five factors that cause high variability in order volume.
First, except for TOTALSTAT ® the company’s products are essentially capital equipment that are not used directly in the end-users production processes. This means that many customers view these products as discretionary items in their capital budgets; which presents significant challenges in demand forecasting and leads to variability in order volume. Second, one would think that government regulations through OSHA and
EPA would be driving sales in the industrial pollution business. However, because enforcement is sporadic from state to state, these regulations essentially drive awareness
(not sales) for air cleaning and air pollution control. Third, there is a strong trend of market consolidations through acquisition; Clarcor recently acquired this company and their competitors are making similar acquisitions. Fourth, the company essentially competes in three main segments (industrial, commercial and the electrostatic spray systems segments). The nature of competition and the types of customers vary by market segment. For example, a competitor dominates the industrial segment and in order to effectively compete in this market segment, our case firm relies on niche-marketing
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opportunities that result in mass customization and product proliferation. Fifth, the company is adopting a more global focus with a market presence in Europe and emerging opportunities in Asia. These five factors all contribute to the high variability in order volume. In the words of the operations manager, “we could be much more effective and efficient if our orders were more stable.” But, faced with the reality of unpredictable order volume, the plant must seek to be flexible.
In Company C, the V.P. of Marketing highlighted the problem with the variability in order volume by describing the nature of competition in the different market segments.
The company competes in five market segments dictated by product application: chemical, plastics, food, mineral, and agribusiness. Each of these application areas is divided into approximately 40 different subcategories. For example, food can be divided into sugar, flour, salt, etc. The markets are generally divided into two geographical regions (North and South of Cincinnati, Ohio). The products are sold through a network of 150 independent manufacturer’s representatives in North America, Latin America,
Asia, and Europe. Process engineers and manufacturing systems integrators are the main customers in each of the application areas. Sales are highly variable in each of these market segments. Therefore, the net impact on operations is that the order sizes can vary widely.
Our findings in these three companies provide support for the notion that variability in order volume is a driver of volume flexibility. Thus, we present our third proposition:
Proposition 3: The greater the variability in order volume, the more the company will be driven to adopt a volume flexibility manufacturing strategy.
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4.5.4. Delivery Lead-Time
In today’s business environment companies are competing on time-based competition. With short delivery lead times, a firm is exposed to market variations in demand immediately. With longer delivery lead times, a firm has more time to adjust.
For example, Stalk and Hout (1990) argue that “competing on time-based measures requires a value delivery system that is two to three times faster and more flexible than the competition.” Other authors for example, Blackburn (1991) argues that cycle-time compression translates into faster asset turnover and increased output flexibility. Wacker
(1996) analyzed manufacturing lead times and their relationship to manufacturing goal hierarchy. Wacker presents a detailed literature review that supports his conclusion that
“lead time is statistically related to the manufacturing goals of quality, productivity
(cost), delivery, and flexibility.” Therefore, using these arguments from the existing literature, we analyzed the three companies and found some evidence of this relationship.
In Company A, the company experiences different lead times in the retail and industrial segments. These varying lead times are related the to the wide range of customer orders for its products. The lead times range from 2 days in the retail segments to 3 months in the OEM segment. Consequently, the operations manager highlighted the need for volume flexibility because his environment is characterized by varying lead times.
In Company B, the operations managers expressed serious concerns about short delivery lead-times. In a typical sales quotation for a 4-week delivery, there may be 2- weeks of engineering and order processing time. This only leaves a 2-week effective
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delivery lead-time for operations. The operations managers stated that not enough lead- time was given to operations to efficiently schedule and flow the jobs to meet customer demands. The result is that there are huge swings in plant output and inefficient used of resources to meet monthly and quarterly dollar targets.
In Company C, three bottlenecks effectively shorten the lead-time for the operations function. For example, in a normal price quotation of 10-14 weeks for a typical screening product, the allocations for order processing, engineering, and manufacturing can be 2 weeks, 6 weeks, and 6 weeks respectively. The first bottleneck is actually at the customer end and it overlaps the front end of the quotation cycle. For example, any delays in the approval process at the customer’s end can eat into the remaining cycle time for delivery performance. The other bottleneck is in engineering.
While investments in CADD systems have helped to reduce engineering cycle time, the extent of customizations offered, makes engineering a bottleneck process. Finally, the customization processes in manufacturing can also be a bottleneck. Delays can occur either because of technical process complications or engineering design changes to meet the product specifications. The net result is that the planned operations lead-time of 6 weeks can easily be reduced to 2-3 weeks.
Our findings in these three companies provide support for the notion that the demand lead-time is a driver of volume flexibility. Thus, we present our fourth proposition:
Proposition 4: The shorter the delivery lead-time, the more the company will be driven to adopt a volume flexibility manufacturing strategy.
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4.5.5. Delivery Reliability (Responsiveness)
Delivery lead-time is also closely related to delivery reliability. Thus, we argue that delivery reliability can also be a driver of volume flexibility. There is ample evidence in the literature to support this notion. For a detailed description of these issues, we cite the work of Blackburn (1991) who showed that in a time-based competitive environment, delivery reliability issues could drive a firm to adopt a flexible manufacturing strategy. Also Vickery (1993) tested a measure of production competence using 31 components of production competence such as delivery speed and process flexibility. The notion of delivery reliability is similar to that of service level. For example, Jordan and Graves (using a multiple product and multiple plant networking strategy) suggest how a volume flexible strategy can lead to higher service levels and higher capacity utilization simultaneously. Therefore, using this theoretical support, we analyzed the three companies and found the following evidence:
Company A places high emphasis on its delivery reliability. The company sees itself as providing a critical service to its customers who need the products to keep their computer systems running reliably. At the Dayton plant, the average lead-time for retail orders is two days. However, most orders are shipped within 24 hours. Orders are tracked through their internal logistics system and most of the items are shipped via UPS or Federal Express. The company uses internal metrics to track delivery performance that is maintained above 90%. The company believes that in this industry, their delivery performance gives them a competitive advantage.
In Company B, their short delivery time when combined with the high variability in order volume, inherently result in delivery reliability problems. Difficulties in
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forecasting lead to significant problems in delivery performance. The company’s internal metrics suggest that delivery performance has a 70% average success rate as measured by promise date versus actual ship date. Each of the managers gave a different perspective on the causes of the delivery problems. The sales manager highlighted the problem and emphasized the difficulties in anticipating high-impact orders with short delivery time frames. Engineering expressed concerns about the quotation process that leads to customization without engineering input on the front-end. Incidentally, the company is currently implementing a plan to matrix engineers to the sales team. In addition the company has also implemented an automated product configuration process in order to shorten the internal planning and engineering lead-time. The marketing manager views the delivery problem from the perspective of inefficiencies in the internal order flow and information management processes. The operations managers view the delivery problem as being primarily caused by not enough lead-time given to operations to efficiently schedule and flow the jobs to meet customer demands. Faced with these delivery reliability problems, the company uses an internal expediter in order to track orders and respond to customer inquiries.
In Company C, they are also plagued by delivery reliability problems. The operations managers express serious concern about their delivery performance. Delivery performance was acceptable for commodities and spare parts. However, their internal metrics suggest a 35% on-time delivery performance rate for manufactured products.
The complicating factor is the extent of customization that is done of each type of screener. Customization causes delivery problems in both the engineering and the manufacturing phases. However, the managers believe that for many of their customers,
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the screeners represent a small part of a new manufacturing system and there are usually
delays in other parts of the processes that ultimately give more realistic lead-times for this company’s products. Nevertheless, the operations manager stated that delivery reliability problems cause him to be an advocate for an agile manufacturing strategy.
Our findings in these three companies provide mixed support for the notion that the delivery reliability is a driver of volume flexibility. Although Company A has good delivery reliability performance results, they are still driven to adopt a volume flexibility strategy for other reasons. In both Company B and Company C, delivery reliability problems drive them to adopt a more flexible manufacturing strategy. Thus, we present our fifth proposition:
Proposition 5: The lower the actual delivery reliability, the more the company will be driven to adopt a volume flexibility manufacturing strategy.
Proposition 5b: The higher the market need for delivery reliability, the more likely the firm will adopt a volume flexibility strategy.
4.5.6. Product Customization
Product customization typically leads to higher production lead-time (Raturi et al,
1990). This means that all the tenets of high production lead-time firms apply here. Also, if there is simultaneous pressure on delivery lead-time, this creates even more of a need for volume flexibility. There is even more evidence in the literature to suggest that the greater the need for customized products, the more the company will be driven to adopt both a product mix flexibility and a volume flexibility strategy. For a detailed description of these arguments, we cite the work of Hayes and Wheelwright (1984), McCutcheon et
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al (1994), Gilmore and Pine (1997), and Safizadeh et al (1997). For example, the Hayes
and Wheelwright (1984) product-process matrix proposes that a company that produces
low volume products with lots of variety need a flexible production system. Also,
Safizadeh et al (1997) report empirical results that show that the degree of customization and product volume heavily influence the decision on product choice. Therefore, using this theoretical support, we analyzed these three companies and found the following evidence:
At Company A, the internal manufacturing planning and control system is based primarily on a make-to-order process. New orders are continually screened to determine how best to fulfill these orders. Depending on the volume, technical requirements, and the lead-time, the orders are filled using resources at one of the four locations. The plant in Dayton, Ohio fills orders for retail and specialty items. Orders for large volumes are handled through the plant in Taiwan. The plant in Salt Lake City, Utah is ISO 9002 certified and handles orders for OEM customers.
Company B competes in the industrial segment using a niche marketing strategy.
In this segment, process engineers and manufacturing systems integrators are the main customers. A competitor dominates 90% of the industrial segment and in order to effectively compete in this market segment, Company B relies on niche-marketing opportunities that result in customization and product proliferation. In the industrial segment, the products are sold through a network of independent manufacturer’s representatives in North America, Asia, and Europe. In the commercial segment, the company uses a sales force that is organized by region. Sales are highly influenced by
technical requirements as specified by these customers. The company responds to these
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differing requirements in each market segment by using its in-house engineering staff and manufacturing expertise to assist in sales quotations.
At Company C, the emphasis on product customization was clearly evident. In assessing the marketing factors that had the most impact on volume flexibility there were several different perspectives from each of the managers. Sales believed that pricing had the most impact on volume flexibility particularly in the industrial segment. In addition, the sales manager also viewed the wide breadth of the company’s product offerings and the extent of customizations as having a significant impact on the need for volume flexibility. The marketing manager viewed effective advertising and leads management as having the most significant impact on the need for volume flexibility. The CEO viewed pricing as having the most impact on the need for volume flexibility with promotions and service running a close second. The engineering manager viewed product customizations as having the most impact on the need for volume flexibility.
Our findings in these three companies provide support for the notion that the extent of product customization is a driver of volume flexibility. Thus, we present our sixth proposition:
Proposition 6: The greater the extent of product customizations, the more the company will be driven to adopt a volume flexibility manufacturing strategy.
4.5.7. Core Competency
There is also evidence in the literature to suggest that the inherent core competency of a company drives the need for different types of flexibility strategies. For example, the need for volume flexibility is created by erratic demand patterns placed on
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the business. If a firm has a wide product line (i.e. a broad core competency) then the
portfolio effect of these products might mitigate the need for volume flexibility. For a
detailed discussion of these arguments, we cite the work of Prahalad and Hammel (1994)
and Whitney (1995). For example, Prahalad and Hammel (1994) define core competence
into three categories: collective learning, diverse productive skills, and multiple
production processes. These authors suggest, “the real sources of advantage are to be found in management’s ability to consolidate corporate-wide technologies and production skills into competencies that empower individual businesses to adapt quickly to changing opportunities.” Therefore, using ideas from the existing literature, we analyzed our three companies to find evidence of the relationship between core competency and volume flexibility.
In Company A, at the Dayton plant, the operations manager saw their core competency to be in knowledge of customer applications and the ability to fabricate the computer cables to meet those unique customer requirements. Therefore, to compete successfully, the company screened all orders to determine how best to fulfill them. The operations manager stated that the Dayton plant is used to fill specialty orders from the
retail customers. Therefore, he argues that his plant is able to remain volume flexible on
the items that they fabricate and assemble. Other plants within the company’s network
fill orders that are beyond this plant’s core capability.
At Company B, the different managers each had a different view of what the core
competency of the company was. The operations managers believed that the core
competency of the company was in sheet metal fabrication of a certain size and form. In
their perspective, any requirements beyond this capability should be outsourced or
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customers should be referred to other sources. The sales manager thought that the core
competency of the company was in being able to customize product offerings to meet
customer requirements. The engineering manager believed that the core competency was
in the technology of electrostatic precipitation. As such, he saw no real problems with
the wide range of products that the company offers.
At Company C, the operations manager described their core competencies in three
aspects: (1) their experience (150 years); (2) their long-term service capability; and (3) their ability to customize products for systems integrators and manufacturing process designers. The operations manager stated that the plant responds very quickly to the variety of products that it produces and a lot of time is spent on customizing products for customers. However, when asked about the relatively high costs of customizations, the managers believe that the cost impacts were minor and that profit margins were good on these custom products and in addition, they have a competitive advantage with their knowledge and manufacturing capabilities.
Our findings in these three companies provide some support for the notion that the core competency of a company is a driver of volume flexibility. Thus, we present our seventh proposition:
Proposition 7: The wider the charter for the core competency of the company, the less the company will be driven to adopt a volume flexibility manufacturing strategy.
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4.6. Sources of Volume Flexibility
We also surveyed the current literature to determine how firms use their resources
to develop and execute a volume flexibility strategy. Essentially, the sources of volume
flexibility are embedded in how efficiently and effectively the company uses its resources
to adapt to demand uncertainty. The notion that adaptability characterizes a firm’s use of
flexibility is not new. For example, Stigler (1939) argues that the greater the adaptability, the less the need for flexibility. Stigler further argues that the amount of flexibility built into a plant depends on the cost and gains of that type of flexibility. But, a major problem, in assessing the extent and use of the sources of volume flexibility, is that manufacturing flexibility inherently represents a capability that may not be exercised.
Therefore, some authors argue that these sources of manufacturing flexibility cannot be accurately measured. For a detailed discussion of these arguments, see Slack (1983) and
Gerwin (1993).
Recognizing that it may be difficult to clearly identify and measure the actual sources of volume flexibility, we searched the extant literature to find support for the sources of volume flexibility within the firm. We focused on the resources available to the firms and categorized them into four categories: manufacturing capabilities, workforce development, networks and strategic alliances, and organizational changes.
Another way of mapping these sources of volume flexibility is to consider the internal and the external sources of this flexibility. Furthermore, since there is a time element involved in exercising these flexibility options, we must also consider what capabilities
are available to the firms in the short-run and also in the long-run. We have summarized the current knowledge on the sources of volume flexibility in the following tables:
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Sources of Volume Flexibility Category Sources of Comment References VF Manufacturing Product and Automated manufacturing technology Suarez, Cusamano Capabilities process (AMT) such as FMS and CIM increases VF and Fine (1995) technologies Batching Large production batches require more Ward et al (1995), automated equipment; which restricts VF Safizadeh and Ritzman (1997) Production Scheduling slack supports VF Cox (1989) planning and control systems Capacity Slack capacity allows larger orders to be Cox (1989), Fine processed without negatively impacting on- and Freund (1990) time delivery performance Setup-time/cost High setup cost reduces VF Gupta and Goyal (1989) Facilities and Facilities and equipment influence Cox (1989) Equipment production cycle time; which has an inverse relationship with VF Workforce/ labor Work-time slack, Overtime, multiple shifts, Cox (1989), Upton seasonal labor (1994), Suarez, Cusamano and Fine (1995) Layout Dedicated processes and production Upton (1995) equipment increases VF Product design Cross-functional product design can Swaminathan and facilitate a modular approach to supports Tayur (1995) product postponement and increases VF Overhead cost Overhead cost is inversely related to VF in Mills and the long run Schumann (1985) Inventory slack Higher parts, materials, and finished goods Cox (1989) facilitate upward adjustment in volume Range of Smaller range/mix support higher VF Gerwin (1987), products Kekre and Srinivasan (1990) Employee Employee Emphasis on organizational learning Skinner (1996) Skills and training through employee cross-training and skills Knowledge development and anticipating future skill needs Organizational Selection and growth of new and unique Hayes and Pisano learning operational capabilities (1994) Beer and Spector (1993)
Table 4.5 Sources of Volume Flexibility (1 of 2)
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Sources of Volume Flexibility Cont’d Category Sources of Comment References VF Networks and Vendor/supplier Impacts the lead-time for orders and the Cox (1989) Strategic networks volume range for orders obtainable within a Alliances given lead time Supplier and Subcontractors/suppliers can absorb volume Suarez, Cusamano subcontractor fluctuations and Fine (1995) relationships (outsourcing) including JIT sourcing Network of Chaining multiple plants increases mix and Jordan and Graves plants VF (1995) Off-shore plants Off-shore plants provide surge capacity and Ferdows (1997) support VF Strategic Improves delivery reliability, streamlines Cooper et al (1997) alliances in the the supply chain and supports VF Distribution network
Organizational Organizational Flatten organizational structures to improve Astley and Brahm Changes realignment cross-functional integration and employee (1989) participation Manufacturing Manufacturing’s proactiveness improves Skinner (1974), involvement strategic alignment and effectiveness Boyer (1994) Manufacturing The key manufacturing task is to help the Clark (1996), transformation firm build new capabilities for the long run Wheelwright and Bowen (1996)
Table 4.6 Sources of Volume Flexibility (2 of 2)
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4.6.1. Manufacturing Capabilities
Tables 4.5 and 4.6 summarize a broad stream of literature that address how a firm uses its resources to adapt to uncertainties in demand. In analyzing a firm’s manufacturing capabilities we note that the range of options available to the firms are embedded in the wide variety of production processes, equipment, and planning and control procedures that the firm uses. In this regard, we have found support in the literature for a range of options that include: slack production capacity, slack scheduling, production equipment, layout, and inventory buffers. For a detailed description of these arguments, we cite the work of the following authors: Cox (1989), Suarez, Cusamano and Fine (1995), Upton (1995), Ward et al (1995), and Safizadeh and Ritzman (1997).
Using this literature support, we analyzed our three firms to find evidence of how they use their manufacturing capabilities as sources of volume flexibility in the short and long terms.
At Company A, slack production capacity provided clear evidence of how the company uses this resource to remain volume flexible. The Dayton plant normally operates at 50-80 percent of its production capacity. Note that this range is fairly wide so, the conclusion of using slack capacity as a source of volume flexibility is validated.
The use of slack capacity is clearly an option in the short run. In the long run, the operations manager stated that they could increase or decrease the output of this company by 100% within a six-month timeframe. The major input required to achieve this increase would be an increase in the workforce by 50%.
At Company B, two important sources of volume flexibility were evident: plant capacity and overtime. The managers stated that the plant does not operate at full
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capacity. Estimates of the current operating plant capacity ranges from 50-70%. The
plant operates four days/week using two 10-hour shifts. The second shift is primarily
used to operate the high-use manufacturing equipment and essential skills e.g. welding.
If necessary, the second shift could be fully staffed to increase the plant’s output.
However, staffing the shift with skilled labor could be problematic in a tight labor
market. The second major source of volume flexibility is the use of overtime. Since
forecasting is problematic and there is high variability in order volume, the operations
managers rely heavily on overtime to meet monthly and quarterly sales targets.
However, extended use of overtime has its drawbacks and there are occasional problems
with quality when the plant uses too much overtime to meet surges in demand.
At Company C, we found evidence of two sources of volume flexibility: slack capacity and inventory buffers. The operations managers stated that the plant currently operates at 45-60% capacity. There are currently two shifts (8 a.m. to 5 p.m. and 5 p.m. to 12 a.m.). The first shift represents 70% of the output and the 2nd shift represents 30%.
The managers believe that they could easily surge to a third shift with a skeleton crew.
However, staffing the shift with skilled labor could be problematic in a tight labor
market. The second source of volume flexibility was the inventory buffers. The plant
also produces spare parts and commodity items by using a make-to-stock process. The operations managers believe that this inventory buffer allows the plant to be volume flexible for these items. However, the plant does not maintain any significant inventories of finished goods because of the extent of customizations that are done on these products.
Therefore, inventory buffers are only an option in the short run for the commodity items and service parts.
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Our findings in these three companies provide some support for the notion that a company can use elements of its manufacturing capabilities as sources of volume flexibility. Thus, we present the following propositions:
Proposition 8: The lower the actual operating capacity at the plant, the greater the source of volume flexibility that is available to this company. The corollary is that the higher the slack production capacity, the greater the source of volume flexibility available to the plant.
Proposition 9: The larger the size of inventory buffers at the plant, the greater the source of volume flexibility that is available to this plant.
Proposition 10: The greater the ability to use overtime, seasonal labor, or flexible shift schedules at the plant, the greater the source of volume flexibility that is available to this plant.
4.6.2. Labor Flexibility
It is said that labor is generally the most flexible resource in any production system. Several human resource management practices have been touted as key factors affecting both manufacturing performance and competitive advantage (Jayaram et al,
1999). There is also considerable consensus in the HRM literature for the use of several items identified as “best HRM practices. ” Some of these practices include: employee training, cross-functional teams, cross training, employee autonomy, and effective labor
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management relations. For a detailed discussion of these items, see Arthur (1994) and
MacDuffie (1995). Therefore, using these concepts from the HRM literature, we analyzed our three companies to find evidence of how they use their labor force as a source of volume flexibility.
At Company A, labor flexibility is highly valued. In a highly competitive business, the company believes that its workforce is key to success. Workforce flexibility is emphasized and encouraged. After having tried multiple shifts over the past few years, the company now maintains a single-shift operation. Overtime is used sparingly. The rationale given for the single shift and sparse use of overtime is based on the desire to balance customer demands on the one hand and the needs of the workers (in terms of job security and employee morale). Workers are continually cross-trained to improve their flexibility. The company has an internal training organization called the CTG University that provides training to all employees. On two different visits, while sitting in the waiting room, we noticed that even the receptionist had to take time out for training while others filled her position. In addition the company promotes and supports employee training through other local sources. Employees are also encouraged to work in teams and rewarded for their effort. Employees are also rewarded for their ideas and suggestions that are incorporated into the internal planning processes. The value that the company places on their employees is perhaps best understood by the senior managers concern that they maintain a no-layoff policy. They also attempt to promote many employees from within the company. Every decision on the company’s business strategy must also consider the impact on the employees.
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At Company B, the managers view their workforce as an important source of
volume flexibility. However, there are no formal programs for cross training, skills-
based compensation, or team/group assignments. The plant operates in a non-union environment and the operations manager reassigns workers to meet requirements as necessary. But reassignments to tasks that require highly skilled labor is obviously limited. Discussions with the HR manager gave further insights into the challenges in attracting and retaining a highly-skilled workforce. The availability of skilled labor in the
Cincinnati area is relatively low in a very tight labor market. The company competes by offering very competitive compensation package to the employees, including a profit-
sharing plan. In addition, the company has an unofficial no-layoff policy and has never had a layoff. On the other hand, the company proceeds very cautiously in hiring additional skilled workers. Over the last three years, the company has hired an additional
35 workers and has experienced a 50% retention rate. Temporary workers are used for unskilled tasks in assembly and shipping. When asked about the need for cross-training, the HR manager discussed the difficulties in administering a formal cross-training program. In addition, he explained that they had tried a skills-based compensation program but that it was too costly and difficult to administer.
At Company C, there is currently an effort to cross-train workers to improve their flexibility. In fact, a major concern is the engineering design time and manufacturing prototyping time that are consumed in the delivery process. The managers place a premium on any training that will help improve the engineering-manufacturing hand-off.
Of course, in a union-shop environment, the company is somewhat limited in the amount of cross-training that it can accomplish. In addition, there are skill certification
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requirements and other technical concerns that limit their ability to cross-train more
workers (e.g. in welding). The company also uses 26 different Continuous Improvement
(C&I) teams to help streamline their processes and these teams have been in existence for
3 years. In fact, they have recently adopted a buyer-planner process that empowers the workers to improve the purchasing process to support their manufacturing efforts.
Finally, incentive plans are used to motivate and reward workers for improving their productivity.
Our findings in these three companies provide some support for the notion that a company can use its labor force as a key source of volume flexibility. Thus, we present the following propositions:
Proposition 10: The greater the deployment and use of HRM “best practices”
(such as cross-training and employee empowerment) within the company, the greater the source of volume flexibility in this company.
4.6.3. Networks and Strategic Alliances
There is a broad and growing stream of literature on supply chain management
[Stevens (1989), Bowersox (1989), Cooper et al (1997)]. For example, Stevens (1989) defines a supply chain as “a connected series of value activities concerned with the planning and controlling of raw materials, components and finished goods from supplier to customer.” Supply chain management seeks to enhance competitive performance by closely integrating the internal functions within a company with the external operations of suppliers and channel members. Effective supply chain management allows a firm to
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efficiently use its network of suppliers and vendors to respond to uncertainties in demand.
The ability of a supplier to absorb demand fluctuations lessens the need for a firm to carry safety stock. The lower the amount of safety stock a firm carries, the less inventory costs are incurred in responding to demand fluctuations. Consequently, firms can use their supply chain network as a source of volume flexibility. Further evidence of the value of strategic sourcing can be found in the work of Narasimhan and Das (1999).
These authors define strategic sourcing as “the use of supplier competencies to achieve flexibility goals.” Narasimhan and Das (1999) argue that strategic sourcing will have a positive influence on volume flexibility. Therefore, using these concepts from the supply chain literature, we analyzed our three firms to see how they used their supply chain as a source of volume flexibility.
At company A, there is clear evidence of how this company uses its supply chain network as a source of volume flexibility. The company maintains long-term relationships with its suppliers and rates the performance of their suppliers very highly.
By participating in industry standards groups like BCSI, ANSI X3T10, PCMCIA, and others, the company is able to stay abreast of new developments in connector and wire technology, and the evolution of standards governing the interconnect business. Also, by developing relationships with the elite computer hardware OEMs, they proactively participate in the industry's ongoing goal of producing more compact, robust, and affordable cabling interconnects. To further enhance its responsiveness, the company has teamed up with some of the most recognized distributors in the world such as Ingram
Micro, Merisel Inc., and Tech Data.
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At Company B, their vendor/supplier network is also a source of volume
flexibility. The company has over 100 suppliers and they have maintained long-term
relationships with several of these suppliers/vendors. These vendors/supplier network serves as a buffer between the internal logistics functions and the ultimate customers.
However, Company B does not use outsourcing as a source of volume flexibility. We asked each manager why outsourcing was viewed so low as a source of volume
flexibility. Discussions revealed that the company had tried outsourcing in the past and
had some poor results. The main problems are: (1) inability to adequately specify
requirements and volume levels to get good prices in the contract; (2) high costs of
outsourcing; (4) poor quality levels; and (5) delivery reliability problems. When asked to
rank the importance of their logistics functions in relation to how responsive these
functions are in supporting significant changes in output levels, the managers state that
their logistics functions need to be improved, primarily in the purchasing area. When we
focused on their internal inventory management and warehousing functions, we note that
a limited amount of finished goods are kept in inventory to support the commercial
segment. The company also uses its distribution network to respond to demand in the
commercial segment. In the industrial segment, they rely heavily on their network of
independent manufacturing representatives and they also use licensing agreements to sell
their manufactured products. However, as the company begins to compete in the global
market-place, they see the need for a more efficient distribution network, especially for
the service portion of the business. Although the company has two manufacturing plants
in the U.K. and Germany, off-shore production capability is rarely used for shipments
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back to the US. However, standard components are made in the US and shipped to the
U.K. and Germany for local customizations.
At Company C, the vendor/supplier network is another important source of volume flexibility. The company has over 600 suppliers and they have maintained long- term relationships (over 75 years) with several of these suppliers/vendors. These vendors/supplier networks help the internal logistics functions to respond efficiently to changes in demand. When asked to rank order the importance of their logistics functions in relation to how responsive to these functions are in supporting significant changes in output levels, the managers ranked the transportation and warehousing functions as the most responsive. Since finished goods are not kept in inventory, the company does not use the traditional type of distribution network. They rely heavily on their network of independent manufacturing representatives and they also use licensing agreements to sell their manufactured products. However, as the company begins to compete in the global market-place, they are placing more emphasis on a distribution network to support the
service portion of their business. In fact, they have recently used a distributor to get
access to the market in Mexico. Another aspect of the company’s supply chain is their
network of plants. But, although the company has some agreements with manufacturing
plants in Canada, off-shore production capability is rarely used.
Our findings in these three companies provide some support for the notion that a
company can use its vendor/supplier network as a source of volume flexibility. Thus, we
present the following propositions:
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Proposition 11: The greater the deployment and use of supply chain management
practices (such as outsourcing, supply and distribution networks, and strategic alliances) within the company, the greater the source of volume flexibility that is available to this company.
4.6.4. Short-Term Sources of Volume Flexibility
We have previously mapped the sources of volume flexibility into internal and external sources by looking at the firm’s manufacturing capabilities, its labor flexibility and its use of networks and strategic alliances. But, to adequately measure volume flexibility, one must also consider the time element involved in using this flexibility option. The theoretical underpinnings of this concept can be found in the arguments of
Slack (1983, and 1987) and Upton (1994). Therefore, we also analyzed these three cases to identify the range of options that are available to the firm in the short-run. In this analysis, we defined a short-run to be an operating quarter (<3 months) for a firm.
4.6.4.1. Inventory and Capacity Buffers
Our findings suggest that in the short-run firms primarily rely on their internal
buffers as sources of volume flexibility. We identified two primary types of buffers:
inventory and slack capacity. For example, Company B uses its inventory buffers of a
relatively standard product (SMOKEETER®) to respond to demand in their commercial
segment. Similarly, company C uses their internal buffers of commodity items to
respond to short-term requirements in the service portion of their business. We also
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noticed the use of slack capacity buffers in each of the three companies. In each instance,
the managers explained that in the short-run, they would first, use their slack capacity buffers to respond to uncertainties in demand. For example, at company A, the operations manager explained that by choice, his Dayton plant normally operates at 50-80
percent of its production capacity. This built-in slack capacity gives the company the option to be volume flexible in the short-run.
4.6.4.2. Labor Flexibility
In addition to the buffers, we also noted that companies rely heavily on their labor flexibility to respond to demand uncertainties in the short-run. The sources of this labor flexibility were manifested in three ways: (1) overtime; (2) cross-trained workers; and (3) temporary employees. For example, company B relied heavily on overtime in the short- run to respond to unexpected changes in demand. The operations manager explained that there is tremendous pressure to meet corporate and company financial targets at the end of the month and end of each quarter. Therefore, to increase their output, this plant uses a considerable amount of overtime in these short-run periods. At company B, they also use temporary workers in low-skilled jobs like assembly, packaging, and warehousing to meet short-term fluctuations in output. At company C, there is a heavy reliance on the use of cross-trained workers to meet unexpected changes in demand. This option was also evident at company A where cross-training is heavily emphasized and managers use this flexibility to ensure that customer orders are shipped out on time.
Our analysis of these three companies provides evidence of how they use their resources to be volume flexible in the short-run. Thus, we state the following proposition:
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Proposition 12: In the short-run, companies will rely on their internal buffers
(capacity and inventory) and their labor flexibility as key sources of volume flexibility.
4.6.5. Long-Term Sources
In this analysis, the long-term was defined as more than half a work year (> 6 months). Using this planning horizon, we asked the managers to explain how they would use their resources to respond to uncertainties in demand. After analyzing their responses, we have identified the following four long-term sources of volume flexibility:
(1) network of plants; (2) outsourcing; (3) the ability to increase/decrease plant capacity; and (4) the ability to increase/decrease the current labor force or the number of shifts.
4.6.5.1. Network of plants
The current literature clearly supports the argument that a network of plants can significantly increase a company’s output flexibility in the long-run. For example, Jordan and Graves (1995) showed that the concept of chaining can be effectively used to increase the output flexibility of plants. These authors showed that limited flexibility, configured the right way, yields most of the benefits of total flexibility. They also showed that limited flexibility has the greatest benefits when configured to chain products and plants together to the greatest extent possible. Therefore, they argued that the right way to add flexibility is to create fewer longer plant-product chains. We found some evidence of this at company A. This company employs a deliberate strategy of building plant excess capacity ex-ante and then using this network of plants to meet
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variations in demand. Since its formation in 1985, the company has grown steadily.
Recent acquisitions include ASP Computer Products Inc., Pen Cabling Technology, and
CTG International. . New orders are continually screened to determine how best to
fulfill these orders. Depending on the volume, technical requirements, and the lead-time,
the orders are filled using resources at one of the four locations. The plant in Dayton,
Ohio fills specialty items. Orders for large volumes are handled through the plant in
Taiwan. The plant in Salt Lake City, Utah is ISO 9002 certified and handles orders for
OEM customers.
4.6.5.2. Outsourcing
There is a wide body of support in the logistics and supply chain management literature to suggest that outsourcing in an effective way to respond to demand uncertainties in the long-run. For example Bowersox et al (1989) looked at marketing strategies such as outsourcing and postponement and show that integrating channel-wide marketing strategies can provide enhanced potential for strategic leveraging of channel efficiency and effectiveness. Other authors [Gentry (1996), Choi and Hartley (1996),
Cooper et al (1997)] look at the buyer-supplier strategic partnerships and show that outsourcing can enhance the effectiveness on the supply chain and provide a competitive advantage.
In company C, we found some evidence of the use of outsourcing in the long-run
as a source of volume flexibility. The operations manager explained that they have
developed the capability to outsource some products and components through a network
of fabrication vendors. The ability to surge with this option is limited by the technical
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capability of the vendors along with the need for training and quality compliance.
Despite these potential problems, outsourcing through vendors is viewed as a core
capability and the company has continually fought at union bargaining agreements to
retain this flexibility option. Therefore, the operations manager explained that this is an
option for the firm to respond to demand uncertainties in the long-run.
4.6.5.3. Increasing Plant Capacity
Increasing plant capacity has been shown to be a source of volume flexibility.
For example, Narasimhan and Das (1999) investigated the relationship between strategic sourcing, manufacturing flexibility, and performance. They defined volume flexibility as the “capability of the system to respond to volume fluctuations and to expand production on short-notice beyond normal installed capacity.” They studied a small sample of manufacturing firms and showed that the ability to increase plant capacity had a positive impact on plant performance.
In our case studies, the managers gave clear indications that they would increase plant in response to demand uncertainty in the long-run. However, when we asked for anecdotal evidence, it was difficult to see where the increase in plant capacity was clearly used to respond to changes in demand. Here we acknowledge the difficulties that Slack
(1983), Gerwin (1993), and Upton (1995) foreshadowed. Since increasing long-term plant capacity is an option for volume flexibility that may never be exercised, we understand the difficulties involved in measuring volume flexibility as a capability.
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4.6.5.4. Workforce Levels
There is clear evidence that companies adjust their workforce levels in the long- run to adjust to uncertainties in demand. In fact, as demand uncertainties increase in the long-run, companies are forced to adjust their number of permanent workers needed to respond effectively to these demand levels. At company A, there was clear evidence of cautious increases in its permanent workforce in response to increasing demand for its products. This company has increased the number of permanent workers from 10 in
1985 to 160 in 1999. At company B, there was also evidence of increasing workforce levels to respond to increasing demand. Over the last three years, the company has experienced a 15% increase in demand and has correspondingly hired an additional 35 workers. At company C, we see the same effect in the reverse. For example the current workforce is 120 permanent employees, which is 20 less than the previous year. The operations manager explained that they had to downsize because of fluctuations in demand.
Proposition 13: In the long-run, some of the key sources of volume flexibility are embedded in four capabilities (1) network of plants; (2) outsourcing; (3) the ability to increase/decrease existing plant capacity; (4) the ability to increase/decrease the labor force or number of shifts.
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4.7. Summary
In this chapter we have used the rich context and background of the three case
studies to identify the drivers and sources of volume flexibility. Throughout this chapter
(Tables 4.4 to 4.6) we have showed how the extant operations management literature
provides support for the influence that the drivers and sources have on volume flexibility.
We have also used the evidence from the case studies to develop 13 propositions that
need to be tested in order to advance operations management theory about volume
flexibility. Our next objective is to test these propositions in the field survey, the third
leg of this triangulated study of volume flexibility. However, one conclusion that we can
make here is that in order to develop a more effective volume flexibility strategy, firms
should strive for a better match between the drivers of volume flexibility and the sources
available to firms to become volume flexible.
The evidence in these three case studies shows that external market forces and internal strategic choices are the drivers of volume flexibility. We have categorized these drivers in the following table.
Drivers of Volume Flexibility
· Variability in order volume External Market Forces · Number of market segments · Delivery lead time · Competitive strategy · Forecasting challenges Internal Strategic Choices · Delivery reliability (Responsiveness) · Core competency Table 4.7 Drivers of Volume Flexibility
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Similarly, the evidence from the case studies suggests that firms use several sources to become volume flexible. We have chosen to categorize the sources of volume flexibility into short-term and long-term sources as shown in the following table.
Sources of Volume Flexibility
· Workforce o Overtime o Cross-training Short-Term o Temporary workers · Inventory buffers · Capacity buffers · Current technology and processes · Planning and control systems · Workforce and shifts flexibility Long-Term · Network of plants · Network of suppliers and vendors · Distribution networks Table 4.8 Sources of Volume Flexibility
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Chapter 5. Field Survey
The field survey is the third methodology used in a triangulated study of volume flexibility. In the first leg of this triangulated study, we used two different samples of objective performance data that showed that small firms have distinct competitive advantage in three of the four process-based volume flexibility measures. However, when we simultaneously measure environmental diversity, technology, and performance, we find no statistical difference in the volume flexibility of small and large firms. In the second leg, we used the case study methodology to gain a deeper understanding of the drivers and sources of volume flexibility within the firms. Now, in this third leg of the study, we measure the relative importance that small and large firms place on volume flexibility in response to demand uncertainty.
The literature suggests that the ability to correctly identify significant relationships among variables depend on our ability to adequately measure the variables
(O’Leary-Kelly and Vokurka, 1998). In this study we rely on self-reported perceptual measures to determine the relative importance that small and large firms place on volume flexibility in response to demand uncertainty. In this effort, we rely on the existing scales, for example, Dixon (1992), Gupta and Somers (1992 and 1996), and Upton
(1995). We also augment these scales using the rich context of information that we gathered on volume flexibility during the case study analysis.
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5.1. Developing the Constructs
In this section, we show how we developed arguments to support the constructs
that we use in this field survey. We emphasize that the focus of this field survey is to
measure how managers at the firm level perceive the value of volume flexibility. As we
have shown in chapter 2, there is a wide stream of literature that focuses on the valuation
of flexibility [Fine, C. and Freund R. (1990); Ramasesh and Jayakumar (1991);
Huchzermeier and Cohen (1996); Kogut and Kulatilaka (1994)]. In this research stream,
many of the models are based on financial valuations of discounted cash flows derived
from estimated levels of uncertainty or risks in the environment.
Unlike this valuation stream that focuses on financial flows to assess the value of
flexibility, we will use the perceptions of managers to measure the value of volume
flexibility. We develop a model that shows that there is a link between the drivers of
volume flexibility, the importance that firms place on volume flexibility, the short-term
and long-term sources of volume flexibility and the performance of the firm.
5.1.1. Drivers of Volume Flexibility
In the previous chapter, we focused on the drivers and sources of volume flexibility. We also developed several propositions by using evidence from the cases to identify the drivers and sources of volume flexibility. We summarize these drivers of
volume flexibility as shown in Table 5.1.
Now, in this field survey, we test to see if these drivers of volume flexibility can be validated by a wide sample of manufacturing firms. For example, in the case studies, we identified the inaccuracy of sales forecast as a driver of volume flexibility. The
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inability to accurately forecast demand has a significant impact on the importance a firm places on volume flexibility. The greater the difficulty that a firm has in forecasting its demand, the higher the importance it will place on a volume flexibility strategy.
Therefore, using these arguments, we developed the scaled items in our field survey questionnaire to determine whether these factors are the drivers of volume flexibility for our sample of firms.
Drivers of Propositions References volume flexibility Forecasting The greater the inaccuracy of the sales Raturi et al (1990), Aldrige and inaccuracy forecasts, the more the company will be Betts (1995) driven to adopt a volume flexibility manufacturing strategy. Customer The greater the number of market Miller and Roth (1994), Bozart segmentation segments served, the more the company and Edwards (1997), Safizadeh will be driven to adopt a volume and Ritzman (1997) flexibility manufacturing strategy. Variability in order The greater the variability in order Hayes and Wheelwright (1984), volume. volume, the more the company will be Skinner (1974), and Hill (1985), driven to adopt a volume flexibility Cox (1989), Fiegenbaum and manufacturing strategy. Karnani (1991) Delivery lead time The shorter the delivery lead-time, the Stalk and Hout (1990), more the company will be driven to Blackburn (1991), Wacker adopt a volume flexibility (1996) manufacturing strategy. Delivery Reliability The lower the actual delivery Blackburn (1991), Vickery et al (Responsiveness) reliability, the more the company will (1993) be driven to adopt a volume flexibility manufacturing strategy. Product The greater the extent of product McCutcheon et al (1994), customization customizations, the more the company Gilmore and Pine (1993), will be driven to adopt a volume Safizadeh and Ritzman (1997) flexibility manufacturing strategy. Core competency The wider the charter for the core Prahalad and Hammel (1994), strategy competency of the company, the less Witney (1995) the company will be driven to adopt a volume flexibility manufacturing strategy.
Table 5.1 Drivers of Volume Flexibility
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5.1.2. Importance of Volume Flexibility
Although we can measure whether or not our list of factors are indeed drivers of
volume flexibility at a given firm, we also need to clearly assess whether the managers
also perceive that volume flexibility is important to the firm. We argue that the true
importance of volume flexibility will ultimately manifest itself by its perceived impact on
the company’s bottomline. To the extent that a volume flexibility strategy will help a
firm achieve and sustain a competitive advantage, then managers within the firm will
value this strategy. We can also measure the relative importance of volume flexibility by
assessing whether a volume flexibility strategy is inherently important either to the firm
or to the primary customers.
Therefore, using these arguments, we developed the scaled items in our field
survey questionnaire to measure the importance firms place on a volume flexibility
strategy.
5.1.3. Volume Flexibility Capability
We define the construct, Volume Flexibility Capability as the ability of the firm to vary output levels without high transition penalties in cost or quality. In developing this construct, we surveyed the existing literature to document how volume flexibility has been measured. For example, Gupta and Somers (1992) measured volume flexibility with one scaled item as “the number of part types or range of sizes and shapes of the set of part types that the system can produce without major setups.” Suarez et al (1996) measured volume flexibility as “the ability to vary production with no detrimental effect on efficiency and quality.” Das and Narasimhan (1999) measured volume flexibility with
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one scaled item as “the capability of the system to respond to volume fluctuations and to expand production on short notice beyond normal installed capacity.”
To consider the impact of both the production technology issues and the environmental fluctuation as recommended by deGroote (1994b), we considered each of these scaled items and augmented them with the findings from our case studies. We then developed four scaled items to measure volume flexibility capability as shown at Table
5.2.
5.1.4. Short-term Sources of Volume Flexibility
In chapter 4, we identified some of the short-term sources of volume flexibility within the firm. First, we argued that in order to adequately measure volume flexibility, one must also consider the time element involved in using this flexibility option. The theoretical underpinnings of this concept can be found in the arguments of Slack (1983, and 1987) and Upton (1994). Therefore, in this analysis, we defined a short-term to be an operating quarter (<3 months) for a firm.
We also showed that the short-term sources of volume flexibility were: (1) inventory buffers; (2) capacity buffers; and (3) labor flexibility. For example, firms may use their inventory buffers as a key source of volume flexibility. When we measure how firms effectively use their inventory buffers to respond to demand, we expect to see that a firm will be more volume flexible than another one is, if it can respond efficiently to changes in demand by using lower inventory levels than a similar firm can. Another source of volume flexibility is the actual operating capacity of the firm’ production plants. Plants that operate at less than full capacity can use their slack capacity buffers to
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respond to changes in demand. Therefore, one plant can demonstrate more volume
flexible capability if it can respond to changes in demand by using less of its capacity
buffers than a similar plant can. Firms also use their labor force as a source of volume
flexibility. The higher the use of overtime, temporary workers or multi-skilled craftsmen,
the higher the lever of flexibility the firm demonstrates. Finally, firms also use their
existing outsourcing capability to respond to changes in demand. Therefore, using these
arguments, we developed scaled items to measure the sources of volume flexibility in the
short-run.
5.1.5. Long-term Sources of Volume Flexibility
In chapter 4, we also identified the long-term sources of volume flexibility.
Firms, we defined the long-term as more than half a work year (> 6 months). Using this planning horizon, we asked the managers to explain how they would use their resources to respond to uncertainties in demand. After analyzing their responses, we identified the following four long-term sources of volume flexibility: (1) network of plants; (2) outsourcing; (3) the ability to increase/decrease plant capacity; and (4) the ability to increase/decrease the current labor force or the number of shifts. Therefore, using these arguments, we developed the scaled items in our field survey questionnaire for the volume flexibility practices construct.
5.1.6. Performance
In this portion of our research, since we are relying on the self-reported measures of the firms, we use a much broader conceptualization of business performance that
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includes three categories: delivery performance, financial performance, and growth
performance. The inclusion of these three aspects of a firm’s performance will go
beyond the “black box” approach that seems to characterize the exclusive use of financial
indicators. This method has also been used by previous researchers e.g. Venkatraman
and Ramanugam (1987). Also, Ward, Leong and Boyer (1996) documented that firms
are generally reluctant to share objective performance data because of confidentiality
issues. In addition, these authors also showed that there is a high correlation between
objective data and perceptive performance measures. Thus, using these ideas, we
developed three scaled items in our field survey questionnaire for the performance
construct.
5.1.7. The Field Survey Questionnaire
To operationalize each of the constructs that we discussed in the previous sections, we developed scaled items in a detailed questionnaire to gather the data on firm
size, drivers of volume flexibility, importance of volume flexibility, volume flexibility
capability, short-term sources of volume flexibility, long-term sources of volume flexibility, and firm performance. We pilot-tested these scaled items during our case study and also by using managers attending an Operations Strategy MBA class at the
University of Cincinnati. Based on feedback from managers we modified the questionnaire and the final version of this instrument is shown at Table 5.2.
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Measurement Instructions: Please circle your Questions responses. For example, if you strongly disagree, please circle 1. Drivers of Volume Flexibility D1. Our annual demand forecasts have been Strongly 1 2 3 4 5 Strongly inaccurate ( > ± 25%) over the past 3 yrs. Disagree Agree D2. The monthly demand for our products Strongly 1 2 3 4 5 Strongly varies significantly. Disagree Agree D3. The lead-time for fulfilling demand in our Strongly 1 2 3 4 5 Strongly industry is relatively short. Disagree Agree D4. We operate in several (>3) market Strongly 1 2 3 4 5 Strongly segments. Disagree Agree D5. A few (<5) major customers drive our Strongly 1 2 3 4 5 Strongly output levels. Disagree Agree D6. Our customers often require us to vary Strongly 1 2 3 4 5 Strongly order sizes. Disagree Agree Importance of Volume Flexibility IMP1. We gain a significant competitive Strongly 1 2 3 4 5 Strongly advantage by being able to ship products in Disagree Agree varying volume levels. IMP2. We place a high value on being able to Strongly 1 2 3 4 5 Strongly ship products in varying volume levels. Disagree Agree IMP3. Our customers place a high value on Strongly 1 2 3 4 5 Strongly being able to order products in varying order Disagree Agree volume levels. Volume Flexibility Capability VF1. Our production processes and equipment Strongly 1 2 3 4 5 Strongly give enable us to produce high volume levels. Disagree Agree VF2. We can significantly ( > ± 25%) increase Strongly 1 2 3 4 5 Strongly (or decrease) our output levels to support Disagree Agree fluctuations in demand. VF3. When we increase (or decrease) our Strongly 1 2 3 4 5 Strongly volume levels we do not experience more than Disagree Agree proportionally higher (or lower) production costs. VF4. When we increase (or decrease) our Strongly 1 2 3 4 5 Strongly volume levels we do not experience more than Disagree Agree proportionally higher (or lower) product quality problems. Short-Term Sources of VF ST1. In the short-term (<3 months), we use Strongly 1 2 3 4 5 Strongly temporary labor to address fluctuations in Disagree Agree demand. ST2. In the short-term (<3 months), we use Strongly 1 2 3 4 5 Strongly cross-trained workers to address fluctuations in Disagree Agree demand. ST3. In the short-term (<3 months), we use Strongly 1 2 3 4 5 Strongly overtime to address fluctuations in demand. Disagree Agree
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ST4. In the short-term (<3 months), we use Strongly 1 2 3 4 5 Strongly internal inventory buffers to address fluctuations Disagree Agree in demand. ST5. In the short-term (<3 months), we use Strongly 1 2 3 4 5 Strongly slack production capacity buffers (>20%) to Disagree Agree address fluctuations in demand. ST6. In the short-term (<3 months), we Strongly 1 2 3 4 5 Strongly outsource through our network of vendors and Disagree Agree suppliers to address fluctuations in demand. Long-Term Sources of VF LT1. In the long-term (>6 months), we can Strongly 1 2 3 4 5 Strongly significantly improve our internal planning and Disagree Agree control systems to address fluctuations in demand. LT2. In the long-term (>6 months), we can Strongly 1 2 3 4 5 Strongly significantly adjust the number of shifts to Disagree Agree address fluctuations in demand. LT3. In the long-term (>6 months), we can Strongly 1 2 3 4 5 Strongly significantly ( > ± 25%) adjust our workforce Disagree Agree levels to address fluctuations in demand. LT4. In the long-term (>6 months), we use Strongly 1 2 3 4 5 Strongly outsourcing to address fluctuations in demand Disagree Agree LT5. In the long-term (>6 months), we use our Strongly 1 2 3 4 5 Strongly network of plants to address demand fluctuations Disagree Agree LT6. In the long-term (>6 months), we use our Strongly 1 2 3 4 5 Strongly network of vendors and distributors to address Disagree Agree fluctuations in demand. Performance P1. Our customers are satisfied with our Strongly 1 2 3 4 5 Strongly delivery performance. Disagree Agree P2. Relative to our competitors, our firm’s Much Much 1 2 3 4 5 financial performance is ______. worse than better than competitors competitors P3. Relative to our competitors, our firm’s sales Much Much 1 2 3 4 5 growth performance is ______. worse than better than competitors competitors P4. Relative to our competitors, our firm’s Much Much 1 2 3 4 5 market share growth performance is ______. worse than better than competitors competitors Demographic Data DD1. The average number of employees at our (a) £ 500 ; (b) 500 < Employees £ 1,000 ; (c) > 1,000 firm is: Employees. DD2. Our average annual sales place us in the (a) £ $50M ; (b) $50M < Sales £ $100M ; (c) > $100M following category: DD3. As a percent of our annual sales, the (a) Products ; (b) Services primary focus of our business is on: DD4. The primary industry that our firm ______competes in is: DD5. At our firm, my (respondent’s) primary (a) Senior Management ; (b) Sales ; (c) Engineering ; functional area of responsibility is in: (d) Operations ; (e) Purchasing and Logistics ; (f) Customer Service ; (g) Controller ; (h) Consultant ; (i) Other Table 5.2 Field Survey Questionnaire
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5.2. Framework and Hypotheses
Our findings in the previous two legs of this triangulated study suggest that small firms may not be more volume flexible than large firms are. The case study also demonstrated in rich detail how firms use their resources to achieve volume flexibility.
Now, in this section we focus on how firms perceive the value of volume flexibility by testing several hypotheses. In general, the hypotheses are based on our previous research.
For example, H1-H5 follow directly from the empirical validation using secondary data in chapter 3. Hypotheses H6-H11 follow directly from the propositions1-13 in chapter 4.
In translating the propositions to specific hypotheses, we have aggregated the propositions into short-term and long-term sources of volume flexibility.
5.2.1. Volume Flexibility and Performance
Previous researchers [Vickery et al (1999), Suarez et al (1996), Kekre and
Srinivasan (1991)] have shown that volume flexibility has a positive impact on the firm’s performance. For example, Vickery et al (1999) studied 65 firms in the cyclical furniture industry and showed that volume flexibility is positively related to all measures of overall firm performance and highly related to market share and market share growth. Other studies have tested the relationship between volume flexibility and firm size (Fiegenbaum and Karnani, 1991). We have also revalidated these finding using two different samples and documented the drivers and sources of volume flexibility (chapter 3 and Chapter 4).
The question now is whether these findings represent a conscious effort by firms to seek a competitive advantage through the use of a volume flexibility manufacturing strategy.
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To answer this question, we need to determine the impact of volume flexibility on the
performance of the firm. In this effort, we rely on the firm’s self-reported measures of
performance. We are also interested in the mediating effect that the size of a firm has in
moderating the relationship between volume flexibility and firm performance.
H1. Large firms are more profitable than small firms are.
H2. Volume flexibility has a positive impact on financial performance of firms.
H3. Volume flexibility has a positive impact on the delivery performance of firms.
H4. Volume flexibility has a higher positive impact on performance in large firms than it does in small firms.
5.2.2. Hypotheses on Importance of Volume Flexibility
In chapter 4, we documented in rich detail how some firms use their resources to execute a volume flexibility manufacturing strategy. The question now is whether firms that place a high emphasis (importance) on volume flexibility also demonstrate a correspondingly high use of volume flexibility capabilities. To answer this question, we need to determine how firms use their resources when they place different emphasis on volume flexibility. Again, in this effort, we rely on the self-reported perceptions of the firms.
Thus H5. Volume flexibility has a higher positive impact on performance when the importance placed on a volume flexibility strategy is high than when this importance is low.
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5.2.3. Hypotheses on Sources of Volume Flexibility
In the previous section, we developed the constructs that measure the short-term and long-term sources of volume flexibility within the firm. We now present some hypotheses to measure the relationship between the short-term/long-term sources of volume flexibility and the volume flexibility capability of the firm.
H6. Short-term sources of volume flexibility have a positive impact on a firm’s volume flexibility capability.
H7. Short-term sources of volume flexibility have a higher positive impact on a firm’s volume flexibility capability in large firms than it does in small firms.
H8. Short-term sources of volume flexibility have a higher positive impact on a firm’s volume flexibility capability when the importance placed on volume flexibility is high than when it is low.
H9(a). Internal Long-term sources of volume flexibility have a positive impact on a firm’s volume flexibility capability.
H9(b). External long-term sources of volume flexibility have a positive impact on a firm’s volume flexibility capability.
H10(a). Internal long-term sources of volume flexibility have a higher positive impact on a firm’s volume flexibility capability in large firms than it does in small firms.
H10(b). External long-term sources of volume flexibility have a higher positive impact on a firm’s volume flexibility capability in large firms than it does in small firms.
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H11. Long-term sources of volume flexibility have a higher positive impact on a
firm’s volume flexibility capability when the importance placed on volume flexibility is
high than when it is low.
5.2.4. The Hypothesized Model
We hypothesize that the importance firms place on volume flexibility has a moderating effect on their use of volume flexibility practices that in turn, have a positive impact on firm performance. This model is shown in Figure 5.1.
Perceived Value of Volume Flexibility
Short-Term Size Sources (+) (+)
VF (+) Performance Capability
(+) Long-Term (+) Sources Importance
Figure 5.1 Perceived Value of Volume Flexibility
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From the evidence in all three firms in our case studies, we find that a variety of short-term and long-term sources were in use to create the capability to respond to the need for volume flexibility. The aggregate volume flexibility capability is based on observations similar to that of Gerwin (1993). There is a difference between having a volume flexibility option (source) and being able to execute it (capability). Thus, a firm may have very flexible overtime policies but may never use overtime. Consequently, this firm’s ability to be volume flexible may not be great. Clearly, the ability to become volume flexible also depends on the importance placed on market factors such as the importance of delivery reliability, etc. Although size was not a specific concern in the case study, our results from the secondary data analysis in chapter 3 suggest that size plays a mediating role between volume flexibility and performance.
5.3. The Pre-Test
The survey instrument was pre-tested in two different forums. A preliminary version of the survey was given to executives during the three case studies. This allowed us to narrow down and specify constructs parsimoniously. Their qualitative feedback also helped improve the content validity of the survey instrument. The survey was also given to 19 students in an MBA Operations Strategy class. Most of these students worked full-time and came from manufacturing and science backgrounds. In addition, these students had recently finished a segment of the course on volume flexibility and had discussed the HBR Barrilla SpA case, which assesses the exaggeration of sales variation in a supply chain and the limited volume flexibility at the plant. Their suggestions [such as: (1) using the term operations flexibility in lieu of manufacturing flexibility; (2)
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including planning and control systems as long-term sources of volume flexibility; and
(3) including demographic data to analyze the responses from product and service firms
separately] were incorporated into the final version of the survey.
5.4. The Sample
Since data to evaluate our hypotheses was not readily available, we have elected
to collect the data using a mail survey methodology. The unit of analysis for this
research is the firm. This requires us to get firm-level data on each of the variables.
Also, since we are using surveys to collect this data, the initial sample size needed to be large enough to allow for the historical 10 to 30 percent response rate (Flynn et al., 1990).
However, we were fortunate in this endeavor because the local APICS chapter agreed to sponsor our survey, and we elected to sample the entire population of 750 managers who are members of the Greater Cincinnati Chapter of APICS in Cincinnati, Ohio. It is noteworthy that these APICS managers represent a wide variety of manufacturing and service firms and they had particular experience in operations, sales, purchasing and logistics, and consulting. A detailed breakdown of the APICS respondents to this survey is provided in tables 5.3 and 5.4.
Even with an opportunity sample, the question is whether or not a study based on a sample of 750 firms is abnormal when compared to published research in the operations management literature. The answer is no. For example, Safizadeh and Ritzman (1997) in their research on performance drivers and process choice used only 144 plants from the
Harris Industrial Manufacturing Directory. In addition, Bozart and Edwards (1997) investigated plant performance by analyzing data on 28 manufacturing plants in the auto industry. Miller and Roth (1994) developed their taxonomy of manufacturing strategy by
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extracting data on only 164 firms that participated in the 1987 Manufacturing Future
Survey.
5.5. Data Analysis
In this section we present the results of several analytical methods that were used
to evaluate our hypotheses. First, we conduct a preliminary data analysis to review some
descriptive statistics and check for any obvious errors in the data. Second, we assessed
the uni-dimensionality of the constructs that we measured in the survey. Third, we use
canonical correlation analysis to highlight the potential significant relationships between
the constructs. Fourth, we test and validate some of our hypotheses using regression
analysis. Fifth, we test the mediating impact of firm size and importance using the
analysis of variance (ANOVA). Finally, we use structural equation modeling to validate
the relationships between our constructs in a nomological network.
5.5.1. Preliminary Data Analysis
Of the 750 surveys mailed out, 20 were returned for wrong addresses and 121
were completed and returned. This yielded an initial response rate of 16.1 percent. We
then called a random sample of 55 non-respondents to determine the overriding reason for their non-response. The majority of people indicated that they were too busy to respond or that they were not interested in participating in the survey. We also compared
the firm size and performance of the 121 responding firms to the 19 responses we had
received from managers in our pre-test. Since the responding firms appeared to match
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those in our pre-test, we decided to utilize all the data that we had in our analysis and a
final breakdown of the data is listed at Tables 5.3 and 5.4.
Type of Firm Firm Size ($M) Function Total Manufacturing Services Senior Mgt. 3 1 4 Sales 1 1 Engineering 1 1 Operations 12 3 15 <$50M Purchasing/Logistics 3 1 4 Customer Service 1 1 Comptroller 3 3 Consultant 1 1 Other 2 2 Total (<$50) 25 7 32 Operations 14 14 $50M to $100M Purchasing/Logistics 13 2 15 Customer Service 2 1 2 Total ($50 to $100M) 29 3 32 Senior Mgt. 2 2 Sales 1 1 2 Operations 28 6 32 Purchasing/Logistics 26 26 >$100M Customer Service 1 1 2 Comptroller 2 2 Consultant 2 1 2 Other 4 1 5 Total (>$100M) 66 10 76 Grand Total 120 20 140 Table 5.3 Breakdown of the Survey Data
Table 5.3 shows that of the 140 responses, 76 came from large firms with annual sales of over $100M and 32 were from small firms with annual sales of less than $50M.
In addition, 120 (86%) of the respondents represented manufacturing firms while 20
(14%) represented service firms. We also analyzed the data by the type of industry represented and the results indicate that the respondents represented firms from 60 different industries as shown at Table 5.4.
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Size of Firm ($M) INDUSTRY Total <50 50-100 >100 ADHESIVES 1 1 AEROSPACE/DEFENSE 2 1 3 AGRICULTURE/FORESTRY 1 1 AIR CLEANING 1 1 AIR COMPRESSORS 1 1 AIRCRAFT ENGINES 1 1 AUTOMOTIVE 1 7 8 BARCODES 3 3 CABLE 1 1 CAPITAL EQUIPMENT MANUF. 1 1 CHEMICAL 1 2 5 8 COMMUNICATIONS 1 1 COMPUTER HARDWARE 1 1 CONDITION MONITORING 1 1 CONSTRUCTION 1 1 CONSUMER PRODUCTS 6 6 COSMETICS 1 1 DEATHCARE 1 1 ELECTRIC MOTORS 1 1 2 ELECTRICAL COMPONENTS 1 1 FINANCIAL FULFILLMENT 1 1 FOOD 2 1 1 4 GREETING CARDS 1 1 HARDWARE/HANDTOOLS 1 1 HEALTH CARE 1 1 HEALTHCARE TEXTILES 1 1 HOME FASHIONS 1 1 HVAC MANUFACTURING 1 1 LIGHTING 1 3 4 LOGISTICS 1 1 MANUFACTURING 1 4 1 6 MATERIAL HANDLING EQ 1 1 MEAT PACKING 2 2 MEDICAL 3 3 METAL WORKING 1 1 1 3 MILITARY 1 1 MUTUAL FUNDS 1 1 N/A (no information provided) 7 3 15 25 PACKAGING 2 2 4 PAPER 3 3 Table 5.4 Industry Breakdown (Table 1 of 2)
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Size of Firm ($M) INDUSTRY TOTAL <50 50-100 >100 PET FOOD 1 1 PHARMACEUTICALS 1 1 PLAYING CARDS/GAMES 2 2 PRINTING 1 1 1 3 PROCESS CONTROL 1 1 PROCESS MACHINERY 1 1 PROCESSED FOOD 1 1 PUBLISHING 1 1 PULP AND PAPER 2 1 3 REFRACTORY PRODUCTS 1 1 RESIDENTIAL PRODUCTS 1 1 RETAIL TOYS 1 1 SEMICONDUCTOR 1 1 2 SOFTWARE 2 2 4 TECHNICAL CONSULTING 1 1 TELECOMM 1 1 TRANSPORTATION 2 2 UNIFORMS RENTAL 1 1 WATER PUMPS 1 1 WIRE & CABLE 2 2
Grand Total 32 32 76 140
Table 5.5 Industry Breakdown (Table 2 of 2)
5.5.2. Assessing Unidimensional Measurement
According to Anderson and Gerbing (1988), the traditional methods employed for the development and evaluation of measurement scales include item-total correlations, reliability estimation using Cronbach’s alpha, and exploratory factor analysis. Item-total correlation refers to a correlation of an item or indicator with the composite score of all the items forming the same set. The item-total method does not account for external consistency because it does not account for the relations of the posited alternate indicators with indicator of different factors.
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5.5.2.1. Item Analysis
Item analysis is a methodology for assessing the accuracy of measurements that
are obtained in the social sciences where precise measurements are often difficult to
secure. The accuracy of a measurement may be divided into two dimensions: validity
and reliability. The validity of an instrument refers to whether it accurately measures the
attribute of interest (Churchill ,1979). Gerbing and Anderson (1988) suggest that
construct validity (contained in the theoretical definition of the construct) measures how
well the content of the construct is captured by the study’s measure of the construct. The
reliability of an instrument concerns whether it produces identical results in repeated
applications. An instrument may be reliable but not valid. However, it cannot be valid
without being reliable. Cronbach’s alpha (or coefficient alpha) is the most popular of the
internal consistency coefficients. If the data are standardized by subtracting the item
means and dividing by the item standard deviations before the above formula is used, we
get the standardized version of Cronbach’s alpha. Since Cronbach’s alpha is a
correlation, it ranges between -1 and 1. In most cases it is positive, although negative
values arise occasionally. An instrument’s Cronbach’s alpha value may be improved by
either adding more items or by increasing the average correlation among the items. What value of alpha should be achieved? Carmines (1990) stipulates that as a rule, a value of at least 0.8 should be achieved for widely used instruments. However, a Cronbach’s alpha value of 0.70 is generally considered acceptable for published research (Nunnally,
1978).
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Item Values If this Item is Removed
Highest Mean Std. Dev. Coef. Alpha Performance Coef. Alpha P1. Our customers are satisfied with our delivery performance. 3.593 1.053 0.836
P2. Relative to our competitors, our firm’s financial performance is 3.459 0.879 0.654 0.890 ______. P3. Relative to our competitors, our firm’s sales growth performance is 3.452 0.879 0.564 0.706 ______. P4. Relative to our competitors, our firm’s market share growth 3.444 0.878 0.526 0.702 performance is ______.
Standardized Cronbach’s Alpha 0.737 0.836
Table 5.6 Item Analysis of Performance
In analyzing the scaled items that measure firm performance, we note the following results:
· The item with the highest mean response was P1. This result suggests that firms
in this sample gave the highest rating to their delivery performance. This result is
interesting because one of the benefits of volume flexibility is improved delivery
reliability. There is a broad stream of literature that suggests that delivery
reliability is related to volume flexibility [Wacker (1996), Blackburn (1991), and
Vickery et al (1993)].
· Three of the four items (P2, P3, and P4) appear to be reliable representation of the construct (financial performance) with a standardized Cronbach’s alpha of 0.836.
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Item Values If this Item is Removed
Importance of Volume Highest Mean Std. Dev. Coef. Alpha Flexibility Coef. Alpha IMP1. We gain a significant competitive advantage by being able 3.59 1.283 0.854 to ship products in varying volume levels. IMP2. We place a high value on being able to ship products in varying 3.59 1.212 0.804 volume levels. IMP3. Our customers place a high value on being able to order products 3.76 1.154 0.821 in varying order volume levels.
Standardized Cronbach’s Alpha 0.878
Table 5.7 Item Analysis of Importance of Volume Flexibility
In analyzing the scaled items that measure the Importance of Volume Flexibility, we note the following results:
· Item with the highest mean response and with the smallest standard deviation was
IMP3. This result suggests that firms place a high value on volume flexibility as
their customers also value this capability.
· The three scaled items appear to be reliable representation of the construct.
Indeed, standardized Cronbach’s alpha is 0.878.
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Item Values If this Item is Removed
Highest Mean Std. Dev. Coef. Alpha Volume Flexibility Capability Coef. Alpha VF1. Our production processes and equipment give us the capability to 3.548 1.208 0.631 0.631 produce high volume levels. VF2. We can significantly ( > ± 25%) increase (or decrease) our 3.007 1.212 0.660 0.660 output levels to support fluctuations in demand. VF3. When we increase (or decrease) our volume levels we do not experience more than 2.681 1.163 0.669 0.669 proportionally higher (or lower) production costs. VF4. When we increase (or decrease) our volume levels we do not experience more than 3.215 1.199 0.610 0.610 proportionally higher (or lower) product quality problems. Standardized Cronbach’s Alpha 0.706 0.706
Table 5.8 Item Analysis of Volume Flexibility Capability
In analyzing the scaled items that measure the Volume Flexibility Capability, we note the following results:
· Items with the highest mean response were VF1, and VF4 with approximately the same standard deviation. These responses suggest that firms perceive themselves to be volume flexible if they have (a) have high volume capable processes or equipment and (b) they can vary their output levels without significant impact on cost or output quality levels. These results are consistent with the findings of several authors including: Gupta and Somers (1992), Suarez et al (1996), Das and Narasimhan (1999), and Vickery et al (1999).. · The four scaled items appear to be a reliable representation of the same construct. The highest standardized Cronbach’s alpha (0.706) was achieved by including all four items to measure the construct.
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Item Values If this Item is Removed
Highest Mean Std. Dev. Coef. Alpha Short-Term Sources Coef. Alpha ST1. In the short-term (<3 months), we use temporary labor to address 2.784 1.499 0.390 fluctuations in demand. ST2. In the short-term (<3 months), we use cross-trained workers to 3.448 1.080 0.368 address fluctuations in demand. ST3. In the short-term (<3 months), we use overtime to address 4.313 0.871 0.336 fluctuations in demand. ST4. In the short-term (<3 months), we use internal inventory buffers to 3.231 1.256 0.251 0.421 address fluctuations in demand. ST5. In the short-term (<3 months), we use slack production capacity 2.679 1.094 0.169 0.223 buffers (>20%) to address fluctuations in demand. ST6. In the short-term (<3 months), we outsource through our network of 2.701 1.343 0.327 0.503 vendors and suppliers to address fluctuations in demand. Standardized Cronbach’s Alpha 0.367 0.494
Table 5.9 Item Analysis of Short-Term Sources
In analyzing the scaled items that measure the Short-term Sources of Volume
Flexibility, we note the following results:
· Item with the highest mean response and low standard deviation was ST3. This result suggests that overtime is the most used short-term source of volume flexibility. The next two most frequently used sources were ST2 (cross-trained workers) and ST4 (inventory buffers). See section 4.6.4 for a list of references and a discussion of these sources. · Taken together, the six scaled items do not appear to measure the same construct. Indeed, we perceive that there may be two underlying factors: internal resource capability (workforce, inventory buffers and slack capacity) and external outsourcing capability.
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Item Values If this Item is Removed
Coef. Highest Mean Std. Dev. Long-Term Sources Alpha Coef. Alpha LT1. In the long-term (>6 months), we can significantly improve our internal planning and 3.455 1.121 0.603 control systems to address fluctuations in demand. LT2. In the long-term (>6 months), we can significantly adjust the number of shifts to 3.082 1.233 0.584 address fluctuations in demand. LT3. In the long-term (>6 months), we can significantly ( > ± 25%) adjust our workforce 3.291 1.162 0.609 levels to address fluctuations in demand. LT4. In the long-term (>6 months), we use outsourcing to address fluctuations in demand 2.955 1.387 0.591 0.626 LT5. In the long-term (>6 months), we use our network of plants to address fluctuations in 2.866 1.353 0.589 0.632 demand. LT6. In the long-term (>6 months), we use our network of vendors and distributors to address 3.082 1.177 0.527 0.409 fluctuations in demand. Standardized Cronbach’s Alpha 0.631 0.662
Table 5.10 Item Analysis of Long-Term Sources
In analyzing the scaled items that measure the Long-term Sources of Volume
Flexibility, we note the following results:
· The Item with the highest mean response and low standard deviation was LT1. This result suggest that firms view improvements in their internal planning and control systems as a key long-term source of volume flexibility. The next three most frequently used long-term sources were LT3 (workforce levels), LT2 (shift work), and LT6 (network of vendors and distributors). See section 4.6.5 for a list of references and a discussion of these sources. · Taken together, the six scaled items do appear not to measure the same construct. The highest standardized Cronbach’s alpha (0.662) was achieved from three items (LT4, LT5 and LT6) that measure the external long-term sources of volume flexibility. Indeed, we perceive that there are perhaps two underlying factors: Internal resources (planning and control systems and workforce flexibility) and external sources.
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Item Values If this Item is Removed
Highest Mean Std. Dev. Coef. Alpha Drivers of Volume Flexibility Coef. Alpha D1. Our annual demand forecasts have been inaccurate ( > ± 25%) over 3.201 1.243 0.229 the past 3 yrs. D2. The monthly demand for our products varies significantly. 3.642 1.093 0.198 0.312 D3. The lead-time for fulfilling demand in our industry is relatively 3.485 1.336 0.178 0.376 short. D4. We operate in several (>3) market segments. 3.552 1.505 0.241 0.395 D5. A few (<5) major customers drive our output levels. 3.052 1.533 0.406 D6. Our customers often require us to vary order sizes. 4.000 1.131 0.260 0.324
Standardized Cronbach’s Alpha 0.320 0.434
Table 5.11 Item Analysis of Drivers of Volume Flexibility
In analyzing the scaled items that measure the drivers of volume flexibility, we note the following results:
· Items with the highest mean response were D6, D2, and D4. These responses suggest that market factors have the biggest influence as drivers of volume flexibility. · The six scaled items do not appear to measure the same construct. Indeed, we perceive that there are perhaps two underlying factors: internal strategic choices (number of customers, and forecasting mechanisms) and external market factors (customer segments, order variability, lead time). See section 4.5 for a list of references and a discussion of these drivers of volume flexibility. · To the extent that these drivers may constitute a formative measure, we have elected not to proceed with further analysis of these constructs in this study. We will identify this shortcoming in our results and conclusions and make recommendations for future extensions of this research.
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5.5.2.2. Exploratory Factor Analysis (EFA)
Using Gerbing and Anderson’s (1988) paradigm for scale development, the analysis began with an exploratory factor analysis of all the scaled items. EFA helps to determine how many latent variables underlie the complete set of items and also provides a means of explaining variation among a relatively large number of original variables
(indicators) using relatively few newly created variables (factors). EFA is primarily used at the early stages of analysis and when strong theory is lacking. Scales are developed from the items loading high together (clusters) on the same factor while loading low on all other factors. However, according to Gerbing and Anderson (1988) exploratory factor analysis does not provide an explicit test of unidimensionality. Several other test should be considered including: confirmatory factor analysis (CFA), tests for discriminant validity, and tests for reliability .
A key decision in EFA is to determine the number of factors to retain. The SAS manual suggests that one rule-of-thumb is to retain those factors whose eigenvalues are greater than one. Kaiser (1960) proposed dropping factors whose eigenvalues are less than one since these provide less information than is provided by a single variable.
Jolliffe (1972) feels that Kaiser's criterion is too large. He suggests using a cutoff on the eigenvalues of 0.7 when correlation matrices are analyzed. Other authors note that if the largest eigenvalue is close to one, then holding to a cutoff of one may cause useful factors to be dropped. However, if the largest factors are several times larger than one, then those near one may be dropped.
Therefore, we conducted an EFA using all the observed items. If we use the eigenvalues-greater-than-one rule, the results suggested that 7 factors should be retained.
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However, since the eigenvalue of the 8th factor is very close to 1, and cumulatively these
8 factors explain 99.4 percent of the variation in the data, we elected to retain 8 factors as
follows:
Individual Cumulative Factors Items Factor Description Eigenvalue Percent Percent
1 P2, P3, and P4 Performance 2.153 16.23 16.23
2 LT4, LT5, LT6 and ST6 External Sources 2.265 17.08 33.31
3 IMP1, IMP2, IMP3, and D6 Importance 2.826 21.31 54.62
4 VF1, VF2, VF3, and VF4 VF Capability 1.980 14.93 69.55
5 LT1, and LT2 LT Internal Sources 1.160 8.75 78.30
6 ST3 ST Internal Source 0.891 6.72 85.02
7 LT2, and LT3 LT Internal Sources 1.016 7.66 92.68
8 D1 Driver of VF 0.896 6.75 99.43
Table 5.12 Factor Analysis Summary
In assessing the unidimensionality of our constructs, we note that the combined results from item analysis, Cronbach’s alpha and EFA results have provided strong support for three of our hypothesized constructs: Performance, Importance, and Volume
Flexibility Capability. The other emerging factors suggest that, in the presence of the other factors, perhaps the best measure of the Short-Term sources of VF is ST3
(overtime). Also, in the presence of the other factors, the best measures of Long-term
External Sources are (LT4, LT5, LT6, and ST6) and the best measures of Long-Term
Internal Sources are (LT1 and LT2) or (LT2 and LT3). It is worth noting that although we expected to find that firms use some outsourcing (ST6) as a short-term source of volume flexibility, the results indicate that outsourcing is viewed as a long-term source.
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5.5.3. Canonical Correlation Analysis
Canonical correlation analysis is a multivariate statistical technique that is used to
analyze the relationship between sets of multiple independent and dependent variables
[Hair, et al., 1998]. As opposed to multiple regression analysis, canonical correlation
simultaneously predicts multiple dependent variables from multiple independent variables, which are normally latent (unobservable). Each latent variable is a linear combination of the observable (indicator) variables in the sample data. Canonical correlation assumes that each set can be given some theoretical meaning, at least to the extent that one set can be defined as the independent variables, and the other as the dependent variables. Under this assumption, canonical correlation assists in determining whether the two sets of variables are independent of one another, identifies the optimum structure of each variable set that maximizes the relationship between the linear composites (canonical variates), and helps to explain the nature of whatever relationships exist between the sets by measuring the relative contribution of each variable to the extracted canonical functions.
Hair, et al. (1998) suggest using three criteria for determining how canonical functions should be interpreted: (1) the level of statistical significance, (2) the magnitude of the canonical correlation, and (3) the redundancy measure for the percentage of variance accounted for from the two sets. As is generally accepted practice, we used a
.05 significance level as the threshold. However, no generally accepted guidelines have
been established regarding the magnitude of the canonical correlations. Hair, et al.
[1998] suggest using common guidelines for significance of factor loadings (a minimal
level of ±0.30 for practical significance). Similarly, no generally accepted guidelines for
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a minimum acceptable redundancy measure have been established. We have selected an index of 0.05 or greater as the threshold.
Table 5.13 shows the results of the significant canonical correlations between selected latent constructs. The canonical correlation shows the correlation between the latent constructs (canonical variates). The canonical R2 suggest how much of the variation in the dependent variate is explained by the independent variate. The dependent variate shared variance shows the amount of variance in the observed dependent variables that is explained by the dependent variate. The redundancy index explains the amount of variance in the observed variables of the independent variate that is explained by the dependent variate.
Dep. Independent Can. Canonical Variate Redundancy Dependent Variate P-value 2 Variate Corr. R Shared Index Variance VF Capability Importance (IMP1, IMP2, and IMP3) (VF1, VF2, VF3, VF4) 0.0001 0.415 0.172 0.494 0.085 Short-Term Sources VF Capability (ST2, ST3) (VF1, VF2, VF3, VF4) 0.0000 0.472 0.223 0.317 0.071 Short-Term Sources VF Capability (ST4, ST5) (VF1, VF2, VF3, VF4) 0.015 0.306 0.094 0.366 0.094 Long-Term Sources VF Capability (LT4, LT5, LT6, ST6) (VF1, VF2, VF3, VF4) 0.012 0.365 0.133 0.378 0.050 Long-Term Sources VF Capability (LT4, LT5, LT6) (VF1, VF2, VF3, VF4) 0.094 0.325 0.106 0.224 0.106 Long-Term Sources VF Capability (LT1, LT2, LT3) (VF1, VF2, VF3, VF4) 0.010 0.335 0.112 0.401 0.045 VF Capability Performance (VF2, VF3) (P2, P3) 0.039 0.267 0.071 0.766 0.055
Table 5.13 Summary of Canonical Correlation Analysis
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The results from our canonical correlation analysis suggest that there is a significant relationship between the dependent and independent variates shown. For example, several of the canonical correlations suggest that there is a positive relationship between sources of volume flexibility and volume flexibility capability. Also, there is a positive relationship between volume flexibility and performance. But, in order to validate our hypotheses, we need to employ more specific analytical techniques such as regression analysis and ANOVA.
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5.5.4. Regression Analysis
We used regression analysis to test the following hypotheses:
· H2. Volume flexibility has a positive impact on financial performance of firms.
· H4. Volume flexibility has a higher positive impact on performance in large firms
than it does in small firms.
· H5. Volume flexibility has a higher positive impact on performance when the
importance placed on a volume flexibility strategy is high than when it is low.
· H6. Short-terms sources of volume flexibility have a positive impact on a firm’s
Volume flexibility capability.
· H9. Long-terms sources of volume flexibility have a positive impact on a firm’s
Volume flexibility capability.
To test H2, we evaluated the following model:
PERF = a + b*VF + e
Where · PERF is the average of the scaled items (P2, P3 and P4)
· VF is the average of the scaled items (VF1, VF2, VF3 and VF4)
· e is the error term in the regression model
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Regression Equation Section
Independent Reg. Standard T-Value Prob Decision Power Variable Coeff. Error (Ho: B=0) Level (5%) (5%)
Intercept 2.77 0.24 11.66 0.000 Reject Ho 1.00
VF (VF1- VF4) 0.22 0.07 3.00 0.003 Reject Ho 0.85 R-Squared 0.06 Analysis of Variance Section
Sum of Mean Prob Power Source DF Squares Square F-Ratio Level -5% Intercept 1.00 1608.56 1608.56 Model 1.00 4.95 4.95 9.02 0.003 0.85 Error 133.00 72.94 0.55 Total (Adjusted) 134.00 77.88 0.58
Root Mean Square Error 0.74 R-Squared 0.06 Mean of Dependent 3.45 Adj R-Squared 0.06 Coefficient of Variation 0.21 Press Value 75.62 Press R- Sum |Press Residuals| 79.17 Squared 0.03 Table 5.14 Regression Analysis Results for H2
Since the regression coefficient in table 5.14 is positive and significant, these results support hypothesis H2 that volume flexibility has a positive impact on financial performance. We note that his finding is consistent with results from our secondary data analysis in chapter 3, where we found that volume flexibility had a positive influence on the risk-adjusted return on sales. This result is also consistent with the findings of other researchers, for example Vickery et al (1999) analyzed a sample of firms in the highly cyclical furniture industry and their results show that volume flexibility has a positive impact on performance.
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To test for the moderating influence of both firm size (H4) and importance (H5)
on the strength of the relationship between volume flexibility and performance, we also
used regression analysis. These hypotheses required us to focus on the interaction terms
in the full regression model. Our hypotheses will be supported if the coefficients (e and
f) of the interaction terms are significant. We evaluated these hypotheses by using the
following model:
PERF = a + b*VF + c*DD2 + d*IMP3 + e*(DD2*VF) + f*(IMP3*VF) + e
Where · PERF is the average of the scaled items (P2, P3 and P4)
· VF is the average of the scaled items (VF1, VF2, VF3 and VF4)
· DD2 represents the size of the firm as measured by average annual sales
and placed into three categories (1, 2, and 3)
· IMP3 represents the importance the firms place on a volume flexibility
strategy as reported on the Likert 5-point scale
· (DD2*VF) is an interaction term that measures the moderating impact of
size of firm
· (IMP3*VF) is an interaction term that measures the moderating impact of
the importance of a volume flexibility strategy
· e is the error term in the regression model
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Power Independent Regression Standard T-Value Prob Decision (5% Variable Coefficient Error (Ho: B=0) Level (5% level) level) Intercept 2.580 0.851 3.030 0.003 Reject Ho 0.853 IMP3 -0.121 0.149 -0.810 0.420 Accept Ho 0.127 DD2 Size($M) 0.323 0.269 1.203 0.231 Accept Ho 0.223 VFLEX_A (VF1- VF4) 0.144 0.293 0.492 0.624 Accept Ho 0.078 s_b_VF (Size_x_VF) -0.071 0.085 -0.834 0.406 Accept Ho 0.131 IMP_b_VF (IMP_x_VF) 0.054 0.050 1.089 0.278 Accept Ho 0.191 R-Squared 0.081
Analysis of Variance Section
Power Sum of Mean Prob (5% Source DF Squares Square F-Ratio Level level) Intercept 1 1673.257 1673.257 Model 5 6.388 1.278 2.366 0.043 0.742 Error 134 72.355 0.540 Total(Adjusted) 139 78.743 0.566
Root Mean Square Error 0.735 R-Squared 0.081 Mean of Dependent 3.457 Adj R-Squared 0.047 Coefficient of Variation 0.213 Press Value 80.903 Sum |Press Residuals| 84.817 Press R-Squared -0.027 Table 5.15 Regression Analysis Results for H4 and H5
These results do not support H4 and H5. The regression coefficient “e” (for the interaction term for size and volume flexibility) is negative while “f” is positive.
However, no strong conclusions can be made because these results are not significant. It is interesting that while the overall model is significant (p-value = 0.043), none of these independent variables and interaction terms are significant. Recall that when we tested
H2, we found that volume flexibility had a positive impact on performance. But, when we include the moderating variables of size and importance, the relationship between volume flexibility and performace also becomes non-significant. Therefore, from this analysis, we conclude that size and importance have no significant moderating impact on the relationship between volume flexibility capability and performance.
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Using the results from the previous item analysis, factor analysis, and canonical
correlation, we now use regression analysis to evaluate hypotheses H6, and H9. To test
these hypotheses we evaluated the following model:
VF = a + b*ST3 + c*ST_A + d*ST_B + e*LT_A + f*LT_B + e
Where for each firm in the sample:
· VF is calculated as the average of (VF1, VF2, VF3 and VF4)
· ST3 is the observed values of ST3
· ST_A is the average of ST4 and ST5
· ST_B is the average of ST_2 and ST3
· LT_A is the average of LT4, LT5, LT6 and ST6
· LT_B is the average of LT1, LT2, and LT3
The results from this regression analysis are shown at Table 5.16. The results for hypothesis H6 are interesting. In this regression model, the short-term sources were
represented by three variables (overtime, internal buffers, and cross-training and
overtime). The result that coefficient “b” was negative and significant is not surprising.
This result indicates that ST3 (overtime use) has a perceived negative impact on the
volume flexibility capability of firms in this sample. Therefore, firms perceive that a
high reliance on the use of overtime is not an effective source of volume flexibility.
Also, the regression coefficient is positive and significant for coefficients “c” and “d” in
our model; which indicate that the short-term sources (inventory and capacity buffers or the combined mix of overtime and cross-training) have a positive impact on volume flexibility. Therefore, this result validates hypothesis H6. These results suggest that
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firms that have a high volume flexibility capability also rely on their inventory buffers, slack capacity, and cross-training as sources of volume flexibility. This result is also consistent with the findings of Fine and Freund (1990) and Upton (1994).
Hypothesis H9(a) and H9(b) also give mixed results. For example, H9(b), the regression coefficient “e” is negative, which suggest that external long-term sources
(such as outsourcing and supply networks) do not increase a firm’s volume flexibility capability. However, the regression coefficient “f “ is also positive and significant which validates our hypothesis H9(a) that internal long-term sources (such as internal planning and control systems, number of shifts and workforce flexibility) have a positive impact on a firm’s volume flexibility capability.
Regression Equation Table Independent Reg. Standard T-Value Prob Decision Power Variable Coef. Error (Ho: B=0) Level -5% -5% Intercept 2.18 0.52 4.2 0 Reject Ho 0.99 ST3 -0.35 0.1 -3.49 0.001 Reject Ho 0.93 ST_A (ST4, ST5) 0.2 0.08 2.57 0.011 Reject Ho 0.72 ST_B (ST2, ST3) 0.37 0.13 2.89 0.005 Reject Ho 0.82 LT_A (LT4 - LT6, ST6) -0.11 0.07 -1.55 0.124 Accept Ho 0.34 LT_B (LT1, LT2, LT3) 0.23 0.08 2.78 0.006 Reject Ho 0.79 R-Squared 0.21
Analysis of Variance Table Sum of Mean Prob Power Squares Square F-Ratio Level -5% Source DF Intercept 1 1308.22 1308.22 Model 5 21.16 4.23 6.77 0.000 0.47 Error 129 80.68 0.63 Total (Adjusted) 134 101.84 0.76
Root Mean Square Error 0.79 R-Squared 0.21 Mean of Dependent 3.11 Adj R-Squared 0.18 Coefficient of Variation 0.25 Press Value 90.45 Sum |Press Residuals| 86.28 Press R-Squared 0.11 Table 5.16 Regression Analysis Results for H6 and H9
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5.5.5. Analysis of Variance (ANOVA)
To study the effects of a single factor (independent variable) on a response
(dependent) variable, we use the one-way or single factor analysis of variance
(Montgomery, 1996). Therefore, to test the mediating role that firm size and importance have on several response variables in our model, we use the single factor ANOVA to independently test each effect on each response variable. Using a completely randomized block design, we used the Bonferroni test to see if there is a significant difference between mean of the response variables for all pairwise levels of each factor.
The results from this analysis are listed at Table 5.17. For example, for
hypotheses H1, and H10(a) suggest that size has no impact on performance, volume
flexibility capability, or internal long-term sources. However, size has a positive impact
for hypothesis H7 and H10(b). These results indicate that large firms rely more heavily
than small firms do on the use of inventory buffers, slack capacity and external supply
networks as sources of volume flexibility.
To evaluate H3, we measured response variable (volume flexibility capability) as
the average of the scaled responses to items VF1-VF4. We then used the scaled item for delivery performance (P1) as our fixed factor with five levels. Detailed results are shown in Appendix C3. These results are significant (p-value = 0.002) and the Bonferroni pairwise comparisons show that higher usage of volume flexibility gives higher levels of delivery performance (delivery performance level 5 is significantly different from levels
2 and 3). Therefore, these results suggest that volume flexibility has a positive impact on delivery performance [Wacker (1996), Blackburn (1991), and Vickery et al (1999)].
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Summary of ANOVA Results (Testing the treatment effect of Firm Size and Importance of Volume Flexibility) Bonferroni (all Response Variable Hypotheses Treatment (t P-value pairwise) Multiple (Y ) i) ij Comparison Tests Performance Size of Firm ($M) as measured (measured by the by DD2 at three (1, 2, 3) levels H1 0.223 No difference average of P2, P3, and (<$50M; $50
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5.5.6. Structural Equation Modeling
In the previous sections, we showed how we evaluated our hypotheses using
factor analysis, regression analysis and ANOVA. However, we note the relative
shortcomings of each methodology. Multiple regression seeks to identify and estimate
the amount of variance in the dependent variable attributed to one or more independent
variables. Path analysis seeks to identify relationships among a set of variables
(explanation). Factor analysis seeks to identify subsets of variables with common shared
variance from a much larger set (exploratory factor analysis), or to confirm a
measurement model where variables are hypothesized to define a construct (confirmatory
factor analysis). Structural equation modeling builds on these methods by incorporating
a confirmatory factor analysis approach into the theoretical relationships among the latent
variables.
The structural model is written as: h = Bh + Gx + z
Measurement model for the latent dependent (endogenous) variables is formulated as
Y = L yh + e
Measurement model for the latent independent (exogenous) variables is formulated as:
X = L xx + d
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Symbol Description h (eta) Vector of latent dependent (endogenous) concepts x (xi) Vector of latent independent (exogenous) concepts B (beta) Matrix of the relationships (structure coefficients) between the latent dependent variables G (gamma) Matrix of the relationships (structure coefficients) between the latent independent variables Y (psi) Matrix contains the variances and covariances among the latent dependent variables. F (phi) Matrix contains the variances and covariances among the latent independent variables. z (zeta) Vector that contains the equation prediction errors Y Vector of observed variables (indicators) for the measures of the latent dependent variables
Ly Matrix of the relationships (factor loadings) between the observed dependent variables and the latent dependent variables (individual loadings denoted by ly) e Vector of the measurement errors of the Y’s.
qe Matrix of the variances and covariances among the errors of the observed dependent variables X Vector of observed variables (indicators) for the measures of the latent dependent variables
Lx Matrix of the relationships (factor loadings) between the observed independent variables and the latent independent variables (individual loadings denoted by lx) d Vector of the measurement errors of the Y’s.
qd Matrix of the variances and covariances among the errors of the observed independent variables Table 5.18 List of SEM variables and parameters
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5.5.6.1. The Hypothesized Model
In this analysis, we use structural equation modeling to test the relationship between the sources of volume flexibility, volume flexibility capability and performance as shown in Figure 5.2. The list of observed and latent variables is shown at Table 5.18.
We conducted the analysis in two phases. First, we evaluate the measurement model by using confirmatory factor analysis to evaluate how well the latent variables are measured by the observed variables. Once we confirm that we have convergent validity, item reliability and discriminant validity, we proceed to the second step by evaluating the structural model. The structural model specifies the hypothesized relationships among the latent variables. It is employed to describe the causal effects and the amount of unexplained variance (Anderson and Gerbing, 1982).
Perceived Value of Volume Flexibility
Short-Term Sources (+)
VF (+) Performance Capability
(+) Long-Term Sources
Figure 5.2 The Hypothesized Model
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List of Variables in the Structural Equation Model
Variables Type Description (Scaled Item) ST4 Observed In the short-term (< 3 mths) we use internal inventory buffers to address fluctuations in demand. ST5 Observed In the short-term (< 3 mths) we use slack production capacity buffers to address fluctuations in demand. LT2 Observed In the long-term (> 6 mths) we can significantly adjust the number of shifts to address fluctuations in demand. LT3 Observed In the long-term (> 6 mths) we can significantly (> +/- 25%) adjust our workforce to address fluctuations in demand. VF1 Observed Our production processes and equipment give us the capability to produce high volume levels. VF2 Observed We can significantly increase (or decrease) volume levels to support fluctuations in demand. VF3 Observed When we increase (or decrease) volume levels we do not experience more than proportionally higher (or lower) production costs. VF4 Observed When we increase (or decrease) volume levels we do not experience more than proportionally higher (or lower) product quality problems. P2 Observed Relative to our competitors, our firm’s financial performance is: P3 Observed Relative to our competitors, our firm’s sales growth performance is: P4 Observed Relative to our competitors, our firm’s market share growth performance is: S_Term Latent Short-term sources of volume flexibility L_Term Latent Long-term internal sources of volume flexibility V_Flex Latent Volume flexibility capability Perf Latent Performance (financial and growth) Table 5.19 List of Variables in the Lisrel Model
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5.5.6.2. The Measurement Model
Much of the recent literature on Confirmatory Factor Analysis (CFA) has been based on Joreskog and Sorbom’ s LISREL terminology and modeling (Bentler, 1986;
Bagozzi et al., 1991). The covariance structure model consists of two parts: the measure-
ment model and the structural model. In this section we evaluate the measurement
model, which specifies how hypothetical constructs (latent) are measured in terms of the
observed variables. In this phase, we use CFA to assess the measurement properties.
Confirmatory Factor Analysis
CFA involves the specification and estimation of on or more hypothesized models
of factor structure, each of which proposes a set of latent variables (factors) to account for
covariances among a set of observed variables. Linear structural equation modeling can
be used to test the fit of a hypothesized model against the sample data. Model
specification is accomplished by fixing or constraining elements in three matrices that are
analogous to the factor pattern matrix, factor correlation matrix, and communalities from
a common factor analysis.
CFA is performed on the entire set of items simultaneously. Anderson et al.
(1987) suggest that assessments of unidimensionality for sets of measures should be
made in the same context (i.e., model) as the one that the researcher is interested in
making statements about the unidimensionality of those measures. They further state that
in substantive applications, most often, the context of interest will be the measurement
model for the overall set of measures under study.
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Figure 5.3 Measurement Model showing Standardized Solution
Convergent Validity and Item Reliability
The concepts of reliability and validity are somewhat different in confirmatory studies than in exploratory studies. Convergent validity is assessed by analyzing the standardized factor loadings of the observed variables on the latent variables. Significant t-values suggest that the measured variables represent the underlying construct. The standardized factor loadings and the t-values for our measurement model are shown in figures 5.3 and 5.4. Item reliability is assessed by analyzing the squared factor loadings of the observed variables on the latent variables. Item reliability is measured by the proportion of variance (R2) in the observed variables accounted for by the latent variables influencing them.
205 5. Measurement Model showing t- t Values
ctor loadings can be viewed as regression coefficients in the regression
ables on latent variables. On the first- models, the standard factor loadings of observed variables (items) on latent variables
(fac are estimates of the validity of the observed variables. The larger the factor loadings or coeffi corresponding t- ors represent the underlying constructs (Bollen, 1989). There is no universally accepted cut off value for factor loadings but convergent validity can be assessed by examining the
if these t- greater than |2| or |2.576| then they are considered significant at the 0.05 level and 0.01 level, respectively.
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Our examination of the t values (Figure 5.4) associated with each of the loadings
ator they exceed the critical values at the 0.01 significant level. Thus, all indicators are significantly related to their specified constructs
fying the posited relationships among indicators and constructs (latent variables).
R2 Values
On the first-order level of measurement models, the proportion of variance (R2) in the observed variables that is accounted for by the latent variables influencing them can be used to estimate the reliability of a particular observed variable (item). R2 values above 0.50 provide evidence of acceptable reliability (Bollen, 1989). In light of the results based on item performance (i.e., convergent validity and R2 a decision can be made whether to proceed with analysis of unidimensionality and composite scales as opposed to item analysis. The squared correlations for the 11 items are listed below:
An examination of the table reveals that several items do not meet the 0.50 criterion. However, despite the low reliability of some of these items, there are several other aspects of this model that are worth evaluating.
Squared Multiple Correlations for X - Variables
VF1 VF2 VF3 VF4 ST4 ST5 ------0.38 0.30 0.33 0.51 0.34 0.35
Squared Multiple Correlations for X - Variables
LT2 LT3 P2 P3 P4 ------0.48 0.32 0.37 0.78 0.82
Fit Statistics for the Measurement Model
The overall fit of a hypothesized model can be tested by using the maximum
c2 statistic provided in the LISREL output. This 2 a function
c is a function of internal and external consistency. The p-value associated with this x2 is the probability of obtaining a c2 value larger than the value actually obtained under the hypothesis that the model specified is a true reflection of reality. Small p-values indicate that the hypothesized structure is not confirmed by the sample data. Although the c2 statistic is a global test of model’s ability to reproduce the sample variance/covariance matrix, its significance levels are sensitive to sample size and departures from multivariate normality; thus, the c2 statistic must be interpreted with caution in most applications. Therefore, other measures of model fit should also be considered in assessing model adequacy (Joreskog and Sorbom, 1989). Such indices include the ratio of c2 to degrees of freedom, the root mean square residual (RMR), the Bentler and Bonnet normed fit index (NFl), the Bentler and Bonnet non-normed fit index (NNFI), and the
Bentler comparative fit index (CFI).
The ratio of c2 to the degrees of freedom (Joreskog, 1969) provides information on the relative efficiency of competing models in accounting for the data. Most current research suggests the use of ratios less than 2 as indication of a good fit. Models exhibiting CFI and NNFI indices greater than 0.90 have adequate fit. These critical values indicate that one expects any model that adequately explains the variances and
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covariances in the observed data to reflect at least a 90% improvement over the null model.
With respect to fit indices, the LISREL program using as input 140 observations
2 demonstrates strong fit for the measurement model (Table 5.20). The c estimate is non-
significant (c2= 46.21, p = 0.17, df =38 ), which indicates good fit. The CFI and NNFI
indices are both 0.98 and 0.97 respectively while the c2 per degree of freedom is 1.22. All
fit indices are well within acceptable limits providing strong evidence of model fit, and
consequently, internal and external consistency.
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Goodness of Fit Statistics
Degrees of Freedom = 38 Minimum Fit Function Chi-Square = 46.21 (P = 0.17) Normal Theory Weighted Least Squares Chi-Square = 45.82 (P = 0.18) Estimated Non-centrality Parameter (NCP) = 7.82 90 Percent Confidence Interval for NCP = (0.0 ; 29.05)
Minimum Fit Function Value = 0.33 Population Discrepancy Function Value (F0) = 0.056 90 Percent Confidence Interval for F0 = (0.0 ; 0.21) Root Mean Square Error of Approximation (RMSEA) = 0.038 90 Percent Confidence Interval for RMSEA = (0.0 ; 0.074) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.66
Expected Cross-Validation Index (ECVI) = 0.73 90 Percent Confidence Interval for ECVI = (0.68 ; 0.89) ECVI for Saturated Model = 0.95 ECVI for Independence Model = 3.11
Chi-Square for Independence Model with 55 Degrees of Freedom = 409.79 Independence AIC = 431.79 Model AIC = 101.82 Saturated AIC = 132.00 Independence CAIC = 475.15 Model CAIC = 212.19 Saturated CAIC = 392.15
Normed Fit Index (NFI) = 0.89 Non-Normed Fit Index (NNFI) = 0.97 Parsimony Normed Fit Index (PNFI) = 0.61 Comparative Fit Index (CFI) = 0.98 Incremental Fit Index (IFI) = 0.98 Relative Fit Index (RFI) = 0.84
Critical N (CN) = 184.96
Root Mean Square Residual (RMR) = 0.068 Standardized RMR = 0.054 Goodness of Fit Index (GFI) = 0.94 Adjusted Goodness of Fit Index (AGFI) = 0.90 Parsimony Goodness of Fit Index (PGFI) = 0.54
Table 5.20 Fit Statistics for the Measurement Model
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Standardized Residuals
LISREL also provides diagnostics that can provide useful information in the assessment of measurement models and unidimensionality in particular. The first diagnostic comes from the examination of the residuals of the predicted covariance or correlation matrix. Residuals represent differences between elements in the observed and fitted moment matrices of covariances. Small fitted residuals indicate good fit, though their size depends on the units of measures of the observed variables (Medsker et al.,
1994). To ease interpretation, residuals are standardized by dividing them by their asymptotic standard errors (Joreskog and Sorbom, 1989). Standardized residuals (also called normalized residuals) are considered large if they are greater than |2.58| (Joreskog and Sorbom, 1989). Large residuals indicate a substantial prediction error for a pair of
indicators (i.e., one of the correlations or covariances in the original input data). The
magnitude of a standardized residual is influenced by the sample size (with larger sample
sizes producing larger standardized residuals). Thus, this information must be factored in
to the interpretation of standardized residuals (Joreskog and Sorbom, 1993). Individual
residuals can be used to search for model mispecifications (Anderson and Gerbing, 1988;
Joreskog and Sorbom, 1989).
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Only on ceeded the value of |2.58|. As shown in the following table, the residual value was 2.60 for items ST4 and VF2.
However, looking at the items from a substantive point of view (e.g. we have strong
e case studies and the secondary data analysis that inventory buffers is related to volume flexibility), there is no need for respecification. In addition, an array of
tics include Q plots, modification indices, and expected change in Lx
esiduals
------VF1 - - VF3 0.88 0.10 - - VF4 0.34 1.12 1.02 - - - - ST5 0.44 0.23 1.15 0.38 - - - 2.29 1.49 LT3 0.23 1.24 0.49 0.26 1.68 0.65 P2 1.31 1.76 1.01 0.61 0.54 1.01 P3 0.35 1.35 0.17 1.79 0.51 1.71 - -
Standardized Residuals
------LT2 - - - - P2 0.66 0.51 - - - - P4 0.37 0.20 0.52 1.20
Table . Standardized Residuals
Measurement Model Q-Plots
Q-plots are plots of standardized residuals against normal quantiles. Joreskog and
Sorbom (1996) point out that points falling approximately on a straight line characterize a good model. Deviations from this pattern are indicative of specification errors in the model, non-normality in the variables or of non-linear relationships among variables.
Standardized residuals that appear as outliers in the Q-plot are indicative of a specification error in the model. Using the Q-plot developed from plotting standardized residuals for the trimmed model, it is shown that the slope is linear and approximately equal to one and there are no apparent outliers. This provides additional evidence of model fit and no apparent mispecifications.
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......
.x ...... x . .
.xx .
. a . . xxx .
. Q . xx. x x .
x . n . i . .x* . l . x x . e . x.x . . . * .
. . . . . x ......
- ...... 3.5 3.5 Standardized Residuals Figure . Q Plot of Standardized Residuals
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Modification Indices
The modification index for a fixed parameter tells us how much the overall c2
would decrease if one re-estimated the model with that, and only that, parameter free
(i.e., included or estimated), while all other parameters are held fixed at the same
estimated values (Joreskog and Sorbom, 1989). Typically, small modification indices
(i.e., approximately 4.0, p <0.05) provide an insignificant improvement in fit relative to
the loss of 1 df from estimating the additional parameter (Kaplan, 1990). What is also
important is to accompany the modification index statistic with the completely standard-
ized expected change in the loading with other latent variables (Lx). Items exhibiting change in Lx greater than 0.3 should be investigated for their lack of unidimensionality.
Modification Indices for LAMBDA-X
S_Term L_Term V_Flex Perf ------VF1 0.55 1.06 - - 0.06 VF2 2.95 0.04 - - 3.76 VF3 0.04 0.19 - - 0.04 VF4 0.27 1.25 - - 3.46 ST4 - - 0.87 1.28 0.56 ST5 - - 0.87 1.28 0.56 LT2 0.55 - - 0.00 0.17 LT3 0.55 - - 0.00 0.17 P2 1.71 0.04 2.72 - - P3 1.78 0.18 0.99 - - P4 0.42 0.27 0.02 - -
Table 5.22 Modification Indices
Considering the modification indices for these 11 items, none of these indices is
highly significant. The highest modification index was 3.76, between VF2 and Perf.
Looking at the relationship from a theoretical point of view, there does not appear to be a
reason for respecification. The highest completely standardized expected change in Lx
was -0.27 for item VF2 and S_Term.
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Discriminant Va
Discriminant validity can be assessed by developing a confidence interval of
±2s (Marcoulides, 1998) for each pair of constructs and examining whether one is
between two constructs plus or minus the standard error. If 1 is not confidence interval, you have evidence of discriminant validity.
PHI MATRIX
S_Term L_Term V_Flex Perf ------S_Term 1.00
L_Term 0.27 1.00 (0.15) 1.77
V_Flex 0.42 0.48 1.00 (0.13) (0.12) 3.27 4.04
Perf 0.13 0.22 0.23 1.00 (0.12) (0.11) (0.10) 1.04 1.90 2.32
5. The Phi Matrix
Discriminant Validity Calculations (F± 2se) S_Term L_Term V_Flex Perf S_Term 1
L_Term 0.27 1 U-Limit 0.57 L-Limit -0.03
V_Flex 0.42 0.48 1 U-Limit 0.68 0.72 L-Limit 0.16 0.24
Perf 0.13 0.22 0.23 1 U-Limit 0.37 0.44 0.43 L-Limit -0.11 0 0.03 Table 5.24 Discriminant Validity Calculations
Therefore, since 1 in not included in these intervals, we have evidence of
discriminant validity.
5.5.6.3. The Structural Model
Once an acceptable measurement model is available, the structural model evaluation may begin. To be congruent with the hypothesized model (Figure 5.6), short-
term sources (x1) and long-term sources (x2) are treated as the exogenous variables. The endogenous variables include volume flexibility capability (hl) and performance (h2).
The terms exogenous variables and endogenous variables are synonymous with in-
dependent and dependent variables, respectively.
Perceived Value of Volume Flexibility
Short-Term Sources (x1)
VF Performance Capability (h2) (h1)
Long-Term Sources (x2)
Figure 5.6 The Structural Model
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In LISREL Notation:
éh1 ù é 0 0ù éh1 ù ég 11 g 12 ù éx1 ù éz 1 ù ê ú = ê ú ê ú + ê ú ê ú + ê ú ëh2 û ëb 21 0û ëh2 û ë 0 0 û ëx 2 û ëz 2 û
y él y 0 ù e é 1 ù 11 é 1 ù ê ú êl 0 ú ê ú y2 y éh1 ù e 2 ê ú = ê 21 ú + ê ú ê 0 l ú ê ú ê y3 ú y ëh2 û êe 3 ú ê ú ê 32 ú ê ú y 0 l y e ë 4 û ëê 42 ûú ë 4 û
x él 0 ù d é 1 ù x 11 é 1 ù ê ú ê ú ê ú x l x 0 éx ù d ê 2 ú = ê 21 ú 1 + ê 2 ú êx ú ê 0 l ú êx ú êd ú 3 x 32 ë 2 û 3 ê ú ê ú ê ú x 0 l x d ë 4 û ëê 42 ûú ë 4 û
We analyzed the structural model in Figure 5.6 using the student’s version of
Lisrel software version 8.3. If the model fits the data adequately, the t-values of the g and b coefficients will be evaluated to test the research hypotheses. A t-value is the ratio of an estimated parameter to its standard error. To assess the fit of the model to the data, c2/df, GFI, AGFI, and NNFI were computed. The relative measure of fit for each endogenous variable is examined through an evaluation of R2. This is similar to the coefficient of determination measure found in multiple regression analysis. The results of this analysis are shown in the following two figures:
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Figure 5.7 Structural Model showing the Standardized Solution
Figure 5.8 Structural Model showing t-Values2
2 Although we allowed the errors of S_Term and L_Term to correlate in the measurement model, they are not shown in this structural model because this path is non-significant (t-value = 1.72).
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Goodness of Fit Statistics
Degrees of Freedom = 40 Minimum Fit Function Chi-Square = 47.13 (P = 0.20) Normal Theory Weighted Least Squares Chi-Square = 46.51 (P = 0.22) Estimated Non-centrality Parameter (NCP) = 6.51 90 Percent Confidence Interval for NCP = (0.0 ; 27.66)
Minimum Fit Function Value = 0.34 Population Discrepancy Function Value (F0) = 0.047 90 Percent Confidence Interval for F0 = (0.0 ; 0.20) Root Mean Square Error of Approximation (RMSEA) = 0.034 90 Percent Confidence Interval for RMSEA = (0.0 ; 0.071) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.72
Expected Cross-Validation Index (ECVI) = 0.71 90 Percent Confidence Interval for ECVI = (0.66 ; 0.86) ECVI for Saturated Model = 0.95 ECVI for Independence Model = 3.11
Chi-Square for Independence Model with 55 Degrees of Freedom = 409.79 Independence AIC = 431.79 Model AIC = 98.51 Saturated AIC = 132.00 Independence CAIC = 475.15 Model CAIC = 200.99 Saturated CAIC = 392.15
Normed Fit Index (NFI) = 0.88 Non-Normed Fit Index (NNFI) = 0.97 Parsimony Normed Fit Index (PNFI) = 0.64 Comparative Fit Index (CFI) = 0.98 Incremental Fit Index (IFI) = 0.98 Relative Fit Index (RFI) = 0.84
Critical N (CN) = 188.84
Root Mean Square Residual (RMR) = 0.069 Standardized RMR = 0.056 Goodness of Fit Index (GFI) = 0.94 Adjusted Goodness of Fit Index (AGFI) = 0.91 Parsimony Goodness of Fit Index (PGFI) = 0.57
Table 5.25 Fit Statistics for Structural Model
The results of fitting the structural model to the data (Table 5.25) indicate that the model had a good fit as indicated by c2 /df (1.17), GFI (0.94), AGFI (0.91) and NNFI
220
(0.97). The specified relationship between volume flexibility capability and performance was supported by the data as indicated by a significant t-value (t = 2.26). A higher level in volume flexibility capability may be conducive in reaching a higher level of performance. Short-term sources (inventory buffers and slack capacity) were found to have significant (t = 2.16) effects on the level of volume flexibility capability. Also, long-term sources (number of shifts and number of workers) were found to have significant (t = 2.78) effects on the level of volume flexibility capability.
An overall coefficient of determination (R2) is calculated for each endogenous
2 variable. For volume flexibility capability, R is 0.33, while for performance, it is 0.06.
In other words, 33% of variation in volume flexibility capability can be explained by the short-term and long-term sources. On the other hand, the proposed model can explain only 6% of the variation in performance.
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Chapter 6. Results and Conclusions
In this chapter, we summarize the results and conclusions of this research. First, we restate why we used a triangulated approach (secondary data analysis, case studies, and field survey). Second, we show how the data and analytical methods of each of the three methods are related. Third, we restate the results from each of the three approaches.
Fourth, we triangulate our findings by showing how these results were supported in each of the three research methodologies. Fifth, we amplify our conclusions using some of the resource-based view arguments [Wernefelt (1984), Barney (1991) and Peteraf (1993)].
Finally, we identify some of the shortcomings of this research and suggest some directions for future research.
6.1. Triangulated Objectives
In this research, we use three approaches to triangulate our understanding of volume flexibility (secondary data analysis, case studies, and field survey). Since the overall objective of the research is to build and validate theory in operations strategy, we have focused on three basic questions about the volume flexibility construct: What?
How? and Why? Figure 6.1 summarizes the objectives of the three approaches. First, the field survey addresses the (What?) question and it assesses the manager’s view of volume flexibility in response to demand uncertainty. For example, in the field survey a typical question that we focused on was: What is the relationship between volume flexibility and performance? Second, the case studies were designed to address the questions (How? and Why?) by focusing on the drivers and sources of volume flexibility
222
within the firms. For example, a typical question that we addressed was: Why are firms
driven to adopt a volume flexibility strategy and how do they achieve it? Finally, the
empirical validation study is designed to provide answers to the theory testing question
(Who?, Where? and When?). For example, a basic question that we investigated was: are small firms more volume flexible than large firms are? And if so, When? and
Where?
Triangulated Objectives
Field Survey
Theory Building: What?
Empirical Case Study Validation Theory Testing: Who? Where? When? Theory Building: How? Why?
Figure 6.1 Triangulated Objectives
6.2. Triangulated Data Sources and Analytical Methods
To accomplish our triangulated objectives, we acquired our data from three sources in the following manner. We began the research with a secondary data source
(Standard and Poors Compustat Business Database) by extracting 20 years of financial
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performance data that the publicly-traded firms report on their SEC 10-K reports. As summarized in Figure 6.2, we analyzed two samples from this Compustat database (N1 =
550 firms from 29 industries; and N2 = 2100 firms from 93 industries). To analyze this
secondary data, we used regression analysis as the main statistical technique for
conducting the empirical validation of the relationship between volume flexibility, firm
size and performance. We used two data samples in order to (1) retest F&K’s existing
hypotheses and propose new measures in a subset of manufacturing industries with
sample, N1, and (2) show how our measures can be extended and validated in a broader
set of industries with sample, N2.
In the second approach (case study), we used primary data collected from three
manufacturing firms. We acquired this data by conducting three case studies using
structured interviews with key decision makers in each of the three firms in order to
determine the drivers and sources of volume flexibility. We document much of this
qualitative data in a systematic format and derive several propositions from it.
In the third approach, we use primary data from a field survey to test and validate
some of the propositions we developed in the case studies as well as to retest some of the
findings from the regression analysis of the secondary data. The main objective of the
field survey is to test and validate the perceived value of volume flexibility in small and
large firms. The data from the field survey consisted of responses from managers from
140 companies in the Greater Cincinnati Area. We then analyzed this data using a
variety of methods including: Item Analysis, Exploratory Factor Analysis, Canonical
Correlation, Regression Analysis, ANOVA, and Structural Equation Modeling.
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Triangulated Data Sources and Analytical Methods
Survey (N=140 Firms / 55 Industries)
Factor Analysis, Canonical Correlation, Regression, ANOVA, SEM
Case Studies Compustat Data (N = 550 Firms / 28 SICs) (N=3 Firms / Capital Goods Industry) 1 (N2 = 2100 Firms / 93 SICs) Qualitative Analysis Regression Analysis
Figure 6.2 Triangulation of Data Sources and Analytical Methods
6.3. Results
In this section, we summarize the results from each of the three approaches. First, we simply restate the results from chapters 3, 4 and 5. Then, we show how these results are related and how they reinforce our findings using the triangulated approach.
6.3.1. Results from the Secondary Data Analysis
In Chapter 3 we retested 7 existing hypotheses that were first published by
Fiegenbaum and Karnani (1991). Table 6.1 summarizes the results of our revalidation of
F&K’s hypotheses. Notice that the major differences in our findings are for hypotheses
H1, H2 and H3.
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Hypothesis Formulations Results
H1 SVij = aj + bj*Sij + eij
· SVij = standard error from the regression of sales data for firm i in industry j. · Sij = size of firm i in industry j, measured by the natural log of average sales .
H1 suggest bj < 0 bj > 0
H2, H3, H4 RISKADJij = cj + dj*Sij + ej*SVij + fj*Sij*SVij + eij
· RISKADJij = the risk adjusted return on assets (or return on sales) which is defined as the ROS divided by the variance of ROS for firm i in industry j over the 20-yr period. · Sij = size of firm i in industry j as measured by the average sales for firm i in industry j over period. · SVij = measure of output flexibility for firm i in industry j. This is the same measure used to evaluate H1.
H2: dj > 0, size has a positive impact on performance dj = 0
H3: ej = 0, output flexibility has no impact on performance ej > 0
H4: fj < 0, size has a negative mediating impact on output flexibility and performance fj < 0
H5, H6, H7 bj = cj + fj*ISVj + gj*CIj + hj*ROSj + + eij
· bj = coefficient of size for industry j (from H1) · ISVj = output flexibility for industry j. Calculated as the Std. Dev. of sales for all firms in industry j. · CIj = capital intensity of the industry (assets divided by sales) · ROSj = return on sales for industry j
H5: fj < 0, measures the impact of industry volatility fj < 0
H6: gj < 0, measures the impact of capital intensity gj < 0
H7: hj > 0, measures the impact of industry profitability hj > 0
Table 6.1 Revalidation of F&K's Hypotheses
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We extended F&K’s work by making a distinction between volume fluctuations and volume flexibility. In this effort, we developed four new process-based measures of volume flexibility. Using Sample N1 (550 firms in 29 industries), we have the following results:
Positive Negative and and Positive Negative Significant Significant Model Measures bj bj bj bj
OF1 sales s 27 2 25 0 (Note 1)
OF 2 Log (s sales) 24 4 7 0 (Note 2)
æ s sales ö VF1 Log ç ÷ 1 27 0 15 ç ÷ ès inventory ø
æ s sales ö VF2 Log ç ÷ 2 26 0 15 ç ÷ è s CGS ø æ ö ç s sales ÷ VF3 Log 0 28 0 17 ç 2 2 ÷ è (s inventory) + (s CGS ) ø
æ s ö ROS*Logç sales ÷ VF4 ç 2 2 ÷ 22 6 0 1 è (s inventory) + (s CGS) ø
Table 6.2 Measures of Volume Flexibility Using Sample N1
· Note 1: measures output fluctuations using std. deviation of sales (F&K, 1991). · Note 2: measures output fluctuations using Mills and Schumann (1985) procedure. · Note 3: models VF1-VF4 measures volume flexibility by using the Mills and Schumann (1985) procedure to measure the simultaneous impact of environmental uncertainty and production technology.
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The key findings of our process-based measures are as follows:
1. The output of large firms fluctuates more than that of small firms.
2. Small firms are more volume flexible (VF1) than large firms are when measured
by their use of inventory buffers to support output fluctuations.
3. Small firms are more volume flexible (VF2) than large firms are when measured
by their cost efficiency in responding to output fluctuations.
4. Small firms are more volume flexible (VF3) than large firms are when measured
by their combined use of inventory buffers and their cost efficiency in responding
to sales fluctuations.
5. When we measure the combined impact of output fluctuations, return on sales,
inventory, and cost-of-goods sold we find that on this measure (VF4), large firms
are more volume flexibile than small firms are.
6. Large firms derive their volume flexibility competitive advantage from their
ability to fluctuate their output more profitably than small firms can.
7. Small firms derive their volume flexibility competitive advantage from their
ability to use inventory buffers and production technology more efficiently than
large firms can.
Since the previous results were based on a sample of primarily manufacturing firms in the capital goods industries, we attempted to improve the generalizability of the results by analyzing a broader sample of firms. Therefore, using sample N2, we analyzed
2100 firms in 93 industries and the results are summarized in Table 6.3. Notice that the results (based on a larger sample, N2) are quite similar to those of the previous sample,
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N1. Therefore, having found similar results in two different samples, we have some evidence that these results are generalizable to a larger sample of firms.
Positive Negative and and Positive Negative Significant Significant Model Measures bj bj bj bj
OF1 sales s 89 1 82 0 (Note 1)
OF 2 Log (s sales) 82 8 22 0 (Note 2)
æ s sales ö VF1 Log ç ÷ 3 87 0 43 ç ÷ ès inventory ø
æ s sales ö VF2 Log ç ÷ 17 73 2 16 ç ÷ è s CGS ø æ ö ç s sales ÷ VF3 Log 7 83 0 33 ç 2 2 ÷ è (s inventory) + (s CGS ) ø
æ s ö ROS*Log ç sales ÷ VF4 ç 2 2 ÷ 54 36 44 20 è (s inventory) + (s CGS) ø
Table 6.3 Measures of Volume Flexibility Using Sample N2
· Note 1: measures output fluctuations using std. deviation of sales (F&K, 1991). · Note 2: measures output fluctuations using Mills and Schumann (1985) procedure. · Note 3: models VF1-VF4 measures volume flexibility by using the Mills and Schumann (1985) procedure to measure the simultaneous impact of environmental uncertainty and production technology.
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6.3.2. Results from the Case Studies
In chapter 4, we focused on documenting the rich context and background of the drivers and sources of volume flexibility within the firm. We found support for these
volume flexibility drivers in the OM literature and we also found evidence in each of the
three firms. We then generated the following propositions:
Drivers of Propositions volume Firms (Supported by OM literature and References flexibility evidence in the case studies)
The greater the inaccuracy of the sales Raturi et al (1990), Forecasting forecasts, the more the company will be Aldrige and Betts inaccuracy all driven to adopt a volume flexibility (1995) manufacturing strategy. The greater the number of market segments Miller and Roth (1994), Customer served, the more the company will be driven Bozart and Edwards all segmentation to adopt a volume flexibility manufacturing (1997), Safizadeh and strategy. Ritzman (1997) Hayes and Wheelwright (1984), The greater the variability in order volume, Variability in Skinner (1974), and all the more the company will be driven to adopt order volume. Hill (1989), Cox a volume flexibility manufacturing strategy. (1989), Fiegenbaum and Karnani (1991) The shorter the delivery lead-time, the more Stalk and Hout (1990), Delivery lead time all the company will be driven to adopt a volume Blackburn (1991), flexibility manufacturing strategy. Wacker (1996) Delivery The lower the actual delivery reliability, the Blackburn (1991), Reliability B, C more the company will be driven to adopt a Vickery (1993) (Responsiveness) volume flexibility manufacturing strategy. The greater the extent of product McCutcheon et al Product customizations, the more the company will be (1994), Gilmore and B, C customization driven to adopt a volume flexibility Pine (1997), Safizadeh manufacturing strategy. and Ritzman (1997) The wider the charter for the core Core competency competency of the company, the less the Prahalad and Hammel B, C strategy company will be driven to adopt a volume (1994), Witney (1995) flexibility manufacturing strategy.
Table 6.4 Drivers of Volume Flexibility
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We also found support in each of the three firms for several propositions that address the sources of volume flexibility as follows:
Sources of Propositions Volume Firms (Supported by OM literature and evidence References Flexibility in the case studies) The lower the actual operating capacity at the Cox (1989), Fine and plant, the greater the source of volume flexibility Freund (1990) Slack that is available to this company. The corollary is A, C Capacity that the higher the slack production capacity, the greater the source of volume flexibility available to the plant. The larger the size of inventory buffers at the plant, Cox (1989), Safizadeh Inventory B, C the greater the source of volume flexibility that is and Ritzman (1997) Buffers available to this plant. Overtime, The greater the ability to use overtime, seasonal Cox (1989), Upton Seasonal labor or flexible shift schedules at the plant, the (1994), and Suarez et Labor, and greater the source of volume flexibility that is al (1995) all Flexible available to this plant. Shifts Schedules. The greater the deployment and use of HRM “best Arthur (1994) and practices”(such as cross-training and employee MacDuffie (1995) HRM Best all empowerment) within the company, the greater the Practices source of volume flexibility that is available to this company. The greater the deployment and use of supply Bowersox (1997), chain management practices (such as outsourcing, Cooper et al (1997), Supply Chain supply and distribution networks, and strategic Vickery et al (1999) Management all alliances) within the company, the greater the Practices source of volume flexibility that is available to this company. In the short-term, companies will rely on their Cox (1989), Fine and Short-Term internal buffers (capacity and inventory) and their Freund (1990), all Sources labor flexibility as key sources of volume Safizadeh and flexibility. Ritzman (1997) In the long-run, the key sources of volume Jordan and Graves flexibility are embedded in four capabilities (1) (1995), Vickery et al Long-Term network of plants; (2) outsourcing; (3) the ability (1999) A, C Sources to increase/decrease existing plant capacity; (4) the ability to increase/decrease the labor force or number of shift.
Table 6.5 Sources of Volume Flexibility
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6.3.3. Results from the Field Survey
In the field survey, we validated the linkages between the sources of volume flexibility, volume flexibility capability, and performance using both regression analysis and structural equation modeling. For example, all of the linkages shown in the structural equation model (figures 6.3 and 6.4) are positive and significant.
Figure 6.3 SEM Results showing the Standardized Solution
Figure 6.4 Structural Model showing t-Values
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In chapter 5 (sections 5.5.4 and 5.5.5), we tested 11 hypotheses and analyzed the results using data from the field survey. These results are summarized as follows:
Analytical P- Hypotheses Description Findings Method value Large firms are more profitable than H1 ANOVA 0.223 No difference small firms are Volume flexibility has a positive impact Regression Positive relationship H2 on financial performance of firms Analysis and 0.003 confirmed SEM Volume flexibility has a positive impact Group 5 (M=3.59) is on delivery performance different from Group 2 H3 ANOVA 0.002 (M=2.58) and Group 3 (M=2.81) Volume flexibility has a higher positive Slope is negative but H4 impact on performance in large firms Regression 0.406 Non-significant than it does in small firms. Volume flexibility has a higher positive impact on performance when the Slope is positive but H5 importance placed on a volume Regression 0.278 Non-significant flexibility strategy is high than when this importance in low. Short-term sources of volume flexibility Regression Positive relationship H6 have a positive impact on a firm’s Analysis and 0.005 confirmed volume flexibility capability SEM Short-term sources of volume flexibility Size 1 (M =2.59) is have a higher positive impact on a firm’s H7 ANOVA 0.031 different from Size 3 volume flexibility capability in large (M=3.13) firms than it does in small firms. Short-term sources of volume flexibility have a higher positive impact on a firm’s Group 5 (M=3.23) is volume flexibility capability when the H8 ANOVA 0.021 different from Group 1 importance placed on a volume (M=2.21) flexibility strategy is high than when this importance in low. Internal long-term sources of volume Regression Positive relationship H9(a) flexibility have a positive impact on a Analysis and 0.006 confirmed firm’s volume flexibility capability SEM External long-term sources of volume Size 1 (M =2.60) is flexibility have a higher positive impact H10(b) ANOVA 0.022 different from Size 3 on a firm’s volume flexibility capability (M=3.16) in large firms than it does in small firms. Long-term sources of volume flexibility have a higher positive impact on a firm’s volume flexibility capability when the H11 ANOVA 0.549 No difference importance placed on a volume flexibility strategy is high than when this importance in low. Table 6.6 Field Survey Results
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6.3.4. Triangulated Results
In this section we show how the results from the three different approaches help us to build operations management theory. First, we used two secondary data sources to revalidate seven existing hypotheses as well as to test four new hypotheses on process- based measures of volume flexibility. Since the results from these tests suggest that small firms are more efficient in using their resources to respond to demand fluctuations, we then used the case studies to develop a much deeper understanding of the drivers and sources of volume flexibility within the firm. We used the rich context of the case studies to generate 13 propositions that identify the drivers and sources of volume flexibility. Finally, we used the field survey to test 11 hypotheses that were based on the
propositions from the case study and also on some of the results from the secondary data
analysis. Figure 6.5 summarizes these relationships as follows:
Triangulated Hypotheses and Propositions
Survey (N=140 Firms / 55 Industries)
11 Hypotheses (Sources, VF & Performance)
Case Studies Compustat Data (N = 550 Firms / 28 SICs) (N=3 Firms / Capital Goods Industry) 1 (N2 = 2100 Firms / 93 SICs) 13 Propositions 7 Hypotheses Retested (Drivers & Sources of VF) 4 New Hypotheses Tested
Figure 6.5 Triangulated hypotheses and Propositions
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6.3.4.1. Sources of Volume Flexibility
When we look closely at the sources of volume flexibility, we notice that our results are strongly supported in each of the three triangulated approaches. For example, in our analysis of the secondary Compustat data, our process-based measures suggest that small firms are more efficient in using their inventory buffers and slack production capacity as sources of volume flexibility. Three different regression models supported this result: models VF1, VF2, and VF3.
These results were also supported by evidence in each of the three cases. For example, the CTG company has grown steadily over the last 10 years and the Operations manager attributes much of their success to their ability to respond flexibly to their customer’s needs. The key sources of volume flexibility identified at this company were slack capacity, cross-training, reliance on a network of plants and suppliers, and outsourcing arrangements. At the Rotex company, we documented evidence of the sources of volume flexibility using overtime, slack capacity, and outsourcing. At the
UAS company, we documented evidence of overtime as a key sources of volume flexibility.
These sources of volume flexibility were also strongly supported in the field survey by hypotheses H6 thru H11. For example, hypothesis H7 of the field survey show that small firms rely less on the use of inventory buffers and slack capacity resources than the larger firm do. This finding is also corroborated by the results from the process-based measures [models VF1, VF2, and VF3] of volume flexibility that show that small firms
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use less inventory and slack capacity to support fluctuations in demand than the larger
firms do. These triangulated results are summarized in Figure 6.6.
Triangulated Sources of Volume Flexibility
Survey (N=140 Firms / 55 Industries)
Inventory Buffers, Slack Capacity, Overtime, Cross-Training, Supply Networks, Outsourcing
Case Studies Compustat Data
(N=3 Firms / Capital Goods Industry) (N1 = 550 Firms / 28 SICs) (N2 = 2100 Firms / 93 SICs) CTG (Slack, Training, Networks, Outsourcing) Rotex (Slack, OT, Outsourcing) Small Firm (Efficient Buffers/Processes) UAS (Slack, Inventory, OT) Large Firm (Slack, Networks, Profits)
Figure 6.6 Sources of Volume Flexibility
6.3.4.2. Drivers of Volume Flexibility
Unlike our success in triangulating the results on the sources of volume flexibility, we were unable to triangulate the results on the drivers of volume flexibility.
First, we did not have reliable surrogate variables in our secondary data set to measure
the drivers of volume flexibility. Second, in the case study, we identified several factors
that drive these three firms to adopt a volume flexibility strategy. Then, using
propositions that were developed in the case studies, we tested the drivers of volume
flexibility in the field study. However, we were unable to get a unidimensional set of
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scaled items and we also did not find any support that the drivers have a positive impact on volume flexibility. Therefore, more research needs to be accomplished in this area.
Triangulated Drivers of Volume Flexibility
Survey (N=140 Firms / 55 Industries)
4 Hypotheses Tested (no hypotheses supported)
Case Studies Compustat Data (N = 550 Firms / 28 SICs) (N=3 Firms / Capital Goods Industry) 1 (N2 = 2100 Firms / 93 SICs) 7 Propositions supported Drivers not tested
Figure 6.7 Drivers of Volume Flexibility
6.3.4.3. Volume Flexibility and Performance
The link between volume flexibility and performance has also been triangulated in this study. First, in the secondary data analysis, the results from hypothesis H3 show that output flexibility (as defined by F&K) has a positive impact on the risk-adjusted measure of performance. Other regression analysis results suggest that large firms are more profitable (when measured by the average annual return on sales). Also, the results from our process-based measure, [model VF4] suggest that large firms derive their
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volume flexibility competitive advantage from their ability to fluctuate their output levels more profitably than small firms can.
Second, this finding was also corroborated by H2 in the field survey where the results from the regression analysis suggest that volume flexibility has a positive impact on financial performance. Furthermore, hypothesis H3 of the field study also suggests that volume flexibility has a positive impact on delivery performance.
Third, the rich context and background that was documented in the case study also support the findings that volume flexibility has a positive impact on performance.
For example, at the CTG company, they have provided ample evidence of the use of several short and long-term sources of volume flexibility. Correspondingly, CTG’s sales have increased significantly over the past 10 years (from $10M to over $40M).
Similarly, anecdotal evidence at Rotex suggests that the company places a lot of emphasis on volume flexibility by maintaining low inventory buffers, operating at 60 percent of plant capacity, and maintaining outsourcing arrangements with suppliers and vendors. Correspondingly, the company has performed well for over 150 years!
However, when we look at the UAS company, we see evidence of a decline in sales and a loss of profitability. Both the new CEO and the Operations manager gave anecdotal evidence that the company needs to become more volume flexible. There is an over- reliance on overtime and more emphasis needs to be given to the use of cross-trained workers, slack capacity buffers and outsourcing arrangements. These triangulated linkages are summarized in the figure 6.8.
.
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Triangulated Results for Volume Flexibility and Performance
Survey (N=140 Firms / 55 Industries)
(H2 and H3) Volume flexibility has a positive impact on performance
Case Studies Compustat Data
(N=3 Firms / Capital Goods Industry) (N1 = 550 Firms / 28 SICs) (N2 = 2100 Firms / 93 SICs) CTG (VF contributes to performance)
Rotex (VF contributes to performance) (H3) Volume flexibility has a positive UAS (no clear evidence) impact on performance
Figure 6.8 Volume Flexibility and Performance
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6.4. Conclusions
In this research, we have presented several arguments for the use of process-based
measures of volume flexibility. We have also used a triangulated research methodology
to identify and validate some of the drivers and sources of volume flexibility. Our work
addresses a significant gap in the literature and brings new insight to the debate question:
Are small firms more volume flexible than large firms are?
6.4.1. Output Fluctuations and Volume Flexibility
Any attempt to measure volume flexibility must first make a distinction between volume fluctuations and volume flexibility. When we measure volume fluctuations, our results suggest that the output of large manufacturing firms fluctuate more than that of small firms. This result was validated using both F&K’s approach (OF1) and Mills and
Schumann’s (1985) measure of output fluctuations (OF2). Characterizing this result, we coined the phrase the “Wal-Mart supplier effect,” which helps us understand why major customers will choose large firms as their suppliers because these firms are able to significantly adjust their output levels to meet fluctuations in customer demand. One explanation for this difference between large and small firms is that large firms are more adept at using their network of plants, overseas suppliers and network of vendors and distributors to respond to fluctuations in output. When viewed from a purely resource dependence perspective, one might surmise that small firms have less bargaining power in the competitive arena and survive primarily by accommodating the environment: if a large customer demands small shipments one day and large shipments the next, they have no choice but to adjust. The question is, can they do this profitably? Process-based
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measures account for the cost implications of doing this. The result that large firms are
able to fluctuate their output more profitably than small firms can is also intuitive if one
argues that while small firms can adjust to small variations in output quickly, they really
do not have the capability to move to second and third shift operation quickly. Nor do
they have the financial and resource leverage to accommodate dramatic highs and lows in
volume fluctuation.
Second, when we use deGroote’s (1994b) framework to develop process-based
measures of volume flexibility by accounting simultaneously for how firms use their
resources in response to environmental uncertainty, we provide invaluable insights into
the sources of volume flexibility within small and large firms. For example, model VF1
shows that small firms are more effective in using their inventory buffers to support
output fluctuations. Also models VF2 and VF3 show that small firms are also more cost efficient and effective in using their production technology (measured by the cost-of- goods sold) and their inventory buffers to support output fluctuations. However, model
VF4 suggests that large firms are more volume flexible than small firms are when we measure the combined impact of output fluctuations, return on sales, inventory, and cost- of-goods sold. These results suggest that large firms derive their volume flexibility competitive advantage from their ability to significantly fluctuate their output and to do so more profitably than small firms can. Small firms derive their volume flexibility competitive advantage from their ability to use inventory buffers and production technology more efficiently than large firms can.
In Figure 6.9, we summarize the differences between small and large firms in their use of output fluctuations and volume flexibility.
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Volume Flexibility v.s. Firm Size
High VF1 VF2 VF3 OF1 OF2
VF4 VF4 VF Measures Low OF OF 1 2 VF1 VF2 VF3
Small Large Size of Firms
Figure 6.9 Output Fluctuations, Volume Flexibility and Firm Size
6.4.2. Short-Term Sources of Volume Flexibility
Firms rely heavily on the use of short-term sources of volume flexibility to sustain their competitive advantage. These findings are consistent with the resource-based view
(RBV) of the firm (Wernefelt, 1984 and Barney, 1991). This theory suggests that firm- specific attributes drive both strategies and performance outcomes. Our results show that the key short-term sources of volume flexibility are: (1) overtime; (2) inventory buffers; and (3) capacity buffers. For example, the higher the use of overtime, temporary workers or multi-skilled craftsmen, the higher the level of volume flexibility that the firm demonstrates. Our results show that small and large firms rely heavily on overtime as a
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key source of volume flexibility. Firms may also choose to use their inventory buffers as
a key source of volume flexibility. When we measure how effectively firms use their
inventory buffers to respond to demand, we find that the competitive advantage goes to
the small firms, in that small firms can respond more efficiently to changes in demand by
using lower inventory levels than large firms can. Another source of volume flexibility is
the actual operating capacity of the firm’ production plants. Plants that operate at less
than full capacity can use their slack capacity buffers to respond to changes in demand.
Therefore, one plant can demonstrate more volume flexible capability if it can respond to changes in demand by using less of its capacity buffers than a similar plant can. On this measure our results show that large firms rely more heavily on their inventory and capacity buffers than small firms do. The strategic implication of this finding is that as firms grow in size and begin to loose their efficiency, if they desire to become more volume flexible, then firms should try to respond to demand fluctuations by using lower inventory and capacity buffers in order to sustain their competitive advantage.
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Short-Term Sources v.s. Firm Size
Slack Inv Cap. High OT Temps OT Cross Train Cross
Term Sources Term Inv
- Train
Low Short Slack Temps Cap.
Small Large Size of Firms
Figure 6.10 Short-Term Sources v.s. Firm Size
6.4.3. Long-Term Sources of Volume Flexibility
The resource-based view (Wernefelt, 1984 and Barney, 1991) of the firm can also be used to explain our findings on the long-term sources of volume flexibility. In this
RBV perspective, unique resources and processes drive heterogeneity among firms and provide competitive advantage when protected from imitation. Our findings suggest that in the long-term, firms can make significant improvements to their planning and control systems and also adjust their workforce levels in order to maintain their competitiveness.
Our findings also suggest that there is a significant difference in the options that large and small firms use as their long-term sources of volume flexibility. For example, from our field survey, we find that large firms are more reliant on outsourcing arrangements,
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supply networks, and multi-plant networks than small firms are. The strategic
implications of this result are obvious. Large firms have the resources necessary for
long-term improvements to their outsourcing and networking alliances. Therefore,
although the large firms may be inefficient in the short-term, they can sustain their
competitive advantage by reliance on the long-term sources on volume flexibility.
We summarize the differences between small and large firms in the following chart:
Long-Term Sources v.s. Firm Size
P/C # Workers Sys Shifts High Supply Outsource Networks
P/C #
Term Sources Term - Workers Sys Shifts
Long Low Supply Outsource Networks
Small Large Size of Firms
Figure 6.11 Long-Term Sources v.s. Firm Size
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6.4.4. Perceived Value of Volume Flexibility
Our triangulated results provide strong support for the hypotheses that volume flexibility has a positive impact on both financial and delivery performance. Again, this result is consistent with the resource-based view that there are firm effects on strategies and performance outcomes within the same industry. Clearly, the managers from our sample of firms perceive that volume flexibility has a positive impact on performance.
But, although we were able to show from our secondary data that large firms are more profitable, the firm’s self-reported data in the survey showed no difference in the financial performance of small and large firms. However, the result that volume flexibility leads to higher delivery performance is worth emphasizing. This result provides strong ammunition for the argument that volume flexibility is not just an academic issue. Many small firms hesitate to follow the path of rapid expansion fearing a loss of flexibility. At the same time, many large firms want to clone the processes of small firms and rid themselves of non-value added and bureaucratic hurdles in order to become more responsive to customer needs. A volume flexibility strategy provides options that allow a firm to respond efficiently to demand fluctuations while maintaining high service levels. The strategic implications of this result provide ample rationale for why firms should adopt a volume flexible operations strategy.
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6.4.5. Contributions to Operations Management Theory
Perhaps the most significant contributions that we make in this research are to develop and test theory that extends existing knowledge about volume flexibility. Other researchers in this area have measured a variety of different dimensions of manufacturing flexibility [Kekre and Srinivasan (1990); Dixon (1992); Gupta and Somers (1992); Ettlie and Penner-Hahn (1994); Suarez et al (1995 and 1996); Upton (1997); Vickery et al
(1999); and Narasimhan and Das (1999)]. However, since the work of Fiegenbaum and
Karnani (1991), researchers have not focused specifically on using empirical methods to measure volume flexibility. Consequently, much of the existing knowledge on empirical measures of volume flexibility is based on work that has been published over 10 years ago. Our research extends the existing knowledge on the measurement, deployment, and viability of a volume flexibility strategy and we use empirical research methodologies to test and validate our hypotheses. The use of empirical methods is significant because it addresses a gap in the literature that was documented by Gerwin (1993) and Scudder and
Hill (1998). In addition, this research represents the first effort to use a triangulated approach (secondary data, case studies, and field survey) to measure volume flexibility.
This research also breaks new ground by showing how operations management
theory on volume flexibility can be developed and supported by importing theories from
the operations strategy field. Amundson (1998) provides strong rationale for importing
theories from other disciplines “because formal theorizing in other fields in generally
more mature than in OM, . . . it is prudent to examine the history of other disciplines for the lessons they have learned.” For example, we utilize the theory of the resource-based
view (RBV) of the firm [Barney (1991), Peteraf (1993), and Wernerfelt (1984)] to
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develop more intuitive understandings of relationships between the sources of volume flexibility, volume flexibility capability and performance. The RBV holds that firms are bundles of resources, and that resources possessing certain specific characteristics have the potential to provide competitive advantage, and in some cases sustainable competitive advantage. A resource is defined as “all assets, capabilities, organizational processes, firm attributes, information, knowledge, etc., controlled by a firm that enable the firm to conceive of and implement strategies that improve its efficiency and effectiveness” (Bar- ney, 1991). For example, Peteraf (1993) indicates that resources contribute to competitive advantage when they are heterogeneous (different across firms), imperfectly mobile (not easily transferred from firm to firm), and cannot be duplicated either prior to or after establishment of the firm’s competitive position (ex ante and ex post limits to competition). Our research shows that there are differences in the resource options that small and large firms use to implement a volume flexibility strategy. Small firms are more efficient at using short-term sources (such as inventory and capacity buffers) of volume flexibility to respond to fluctuations in demand while large firms are better positioned to derive competitive advantage through the long-term sources of volume flexibility (such as supply chain networks and outsourcing arrangements).
In this research, we document the rich context and background of key factors that are the drivers and sources of volume flexibility. We also show how firms can use secondary data to measure their own volume flexibility as well as to benchmark themselves against the competition. In addition, the corroborated results from the field survey and the secondary data analyses provide compelling reasons for firms to adopt a volume flexibility operations strategy because it has a positive impact on both delivery
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and financial performance. The links between the drivers and sources of volume
flexibility, volume flexibility capability, and performance can be summarized in Figure
6.12. Notice that the link between the drivers and sources of volume flexibility are dashed lines because they represent propositions that were supported in the case studies but they were not validated in our field survey. However, the other linkages were both supported in the case studies and validated in the field survey as we described earlier.
Volume Flexibility Linkages
Drivers Sources Volume Flexibility
Short-Term Measures Internal - Current Tech/Process - Range (volume changes) - Forecast Inaccuracy - Workforce - Costs (financial & quality) - Competitive strategy -- Overtime - Time (<3mths, >6mths) - Core competency -- Cross-training -- Temp workers - Capacity buffers - Inventory buffers External - Market segments - Number of customers Long-Term - Order volume variations - Planning & Control Sys - Delivery Lead-time - Workforce/Shifts Expansion - Network of plants Performance - Network of suppliers - Delivery - Distribution network - Financial - Growth
Figure 6.12 Volume Flexibility Linkages
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6.4.6. Shortcomings and Directions for Future Research
Despite the triangulated approach to measuring the volume flexibility construct,
there are several shortcomings that limit the generalizability of this research. First, the
secondary data was limited to publicly-traded firms. To the extent that our two samples of 550 and 2100 firms were drawn from a population of publicly traded firms, our results may be limited to this population of firms. Future research should extend this research by using a wider random sample of public and private firms that consist of small and large firms from a variety of industries.
Second, we were unable to triangulate our results on the drivers of volume flexibility. Clearly more research needs to done in this area. Special efforts should to be made to develop and validate scaled items to measure the drivers of volume flexibility.
In addition, surrogate variables that measure the drivers of volume flexibility should be extracted from secondary data in order to triangulate the relationships between the drivers and sources of volume flexibility.
Third, our case studies were limited to an opportunity sample of three relatively small manufacturing firms. Future research should evaluate a wider random sample of both small and large firms in several industries. Also, the research design should be carefully done within each industry in order to effectively triangulate the case study results with the findings from the other research methods.
Fourth, the field survey was also based on an opportunity sample of APICS members of the Greater Cincinnati Area chapter. While the response rate (approximately
19 percent) for this type of research was within the 10 to 30 percent of the historical average (Flynn et al, 1990), more work needs to be done to study a wider sample of
250
firms. Future research should address a much broader random sample of firms in order to
improve the generalizability of this study.
Finally, we acknowledge that volume flexibility represents one of several options that firms can use to respond to demand uncertainty. Two other highly related options are mix flexibility and new product flexibility. Future research needs to be done on these two constructs in order to (1) develop measures for these constructs and (2) to identify
the drivers and sources of mix and new product flexibility within the firm.
Despite these shortcomings, our results are interesting and they add valuable
insights into the development of operations strategy theory on the development and use
of volume flexibility.
251
Bibliography
Adams E. and Swamidass, P. (1989). Assessing operations management from a strategic perspective. Journal of Management, 15(2): 181-203.
Aggarwal, S. (1997). Flexibility management: the ultimate strategy. Industrial Management, Nov./Dec.: 20-26.
Aldrige, D. and Betts, J. (1995). Flexibility and responsiveness in relation to the use of MRPII. Logistics Information Management, 8(6):
Amundson, S. (1998). Relationships between theory-driven empirical research in operations management and other disciplines. Journal of Operations Management, 16: 341-359.
Anderson, J. and Gerbing, D. (1988). Structural equation modeling in practice: a review and recommended two-step approach. Psychological Bulletin, 103(3): 453-460.
Anderson, J., Gerbing, D., and Hunter, J. (1987). On the assessment of unidimensional measurement: internal and external consistency, and overall consistency criteria. Journal of Marketing Research, XXIV, 432-437.
Allen, D.(1999). Seasonal production smoothing. Review, Sept./Oct. 1999: 21-30.
Arthur, J. (1994). Effects of human resource systems on manufacturing performance and turnover. Academy of Management Journal, 37: 670-687.
Bagozzi, R. Youjae, Y. and Phillips, L. (1991). Assessing construct validity in organizational research. Administrative Science Quarterly, 36: 421-458.
Barney, J. B. (1991). Firm resources and sustained competitive advantage, Journal of Management, 17(1): 121-154.
Barney, J. B. (1997). Gaining and Sustaining Competitive Advantage. Addison-Wesley, Reading, MA.
Bentler, P. (1986). Structural modeling and Psychometrika: a shitorical perspective on growth and achievements. Psychometrika, 51(1): 35-51.
Birch, D. (1988). The hidden economy. Wall Street Journal, :23R-24R
Blackburn, J. (1990). The time factor. National Productivity Review, 9(4): 395-408.
Blackburn, J. (1991). Time-based Competition. Business One Irwin, Homewood, IL
252
Bogozzi, R., (1980). Causal Models in Marketing, Wiley, New York, NY.
Bollen, K. (1989). Structural Equations with Latent Variables, Wiley, New York, NY.
Boynton, A., Victor, B., and Pine, J. (1993). New competitive strategies: Challenges to organizations and information technology. IBM Systems Journal, 32(1): 40-65.
Bowersox, D.J. and Morash, E.A. (1989). The Integration of Marketing Flows in Channels of Distribution. European Journal of Marketing 23: 54-67.
Bowersox, D. J. (1997). Integrated Supply Chain Management: A Strategic Imperative, proceedings of Council of Logistics Management 1997 Annual Conference, 5-8 Oct. Chicago, IL.
Bozart, C. and Edwards, S. (1997). The impact of market requirements focus and manufacturing characteristics focus on plant performance. Journal of Operations Management, 15: 161-180.
Boyer, K., and Leong, G. (1996). Manufacturing flexibility at the plant level. Omega International Journal of Management Science, 1996, 24(5): 495-510.
Brill, P.H. and Mandelbaum, M. (1987), On measures of flexibility in manufacturing, Internatioanl Journal of production Research, 27(5): 747-756
Campbell, D. (1969). Variation and Selective Retention in Socio-Cultural Evolution. General Systems: Yearbook of the Society for General Systems Research, 16: 69- 85.
Carroll, G. R. (1984). Organizational Ecology. Annual Review of Psychology, 32: 71- 93.
Choi, T.Y. and Hartley, J. (1996). An exploration of supplier selection practices across the suppy chain. Journal of Operations Management,14:333-343.
Churchill, G. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16: 64-73.
Clark, K. (1996). Competing through Manufacturing and the New Manufacturing Paradigm: Is Manufacturing Strategy Passé?. Production and Operations Management, 5(1): 42-58.
Cooper, M., Ellram, L., Gardner, J., Hanks, J. (1997), Meshing multiple alliances. Journal of Business Logistics, 18(1): 67-89.
253
Cox, Jr., T., (1989). Toward the measurement of manufacturing flexibility, Production and Inventory Management Journal, First Quarter: 68-72.
D’Aveni, A. (1995). Coping with hyper competition: Utilizing the new 7S’s framework. Academy of Management Executive, 9(3): 45-60.
Dean T., Brown, R., and Bamford, C. (1998). Differences in large and small firm responses to environmental context: strategic implications from a comparative analysis of business formations. Strategic Management Journal, 19: 709-728.
de Groote, Xavier (1994b). The flexibility of production processes: A general framework, Management Science, 40(7): 933-945.
DeMeyer, A., Nakane, J., Miller, J. G., and Ferdows, K., (1989). Flexibility, the next competitive battle. Strategic Management Journal, 10: 135-144.
Dixon, J. R., (1992). Measuring manufacturing flexibility: an empirical investigation. European Journal of Operational Research, 60: 131-143.
Eisenhardt, K. (1989). Building theories from case study research. Academy of Management Review, 14(4): 532-550.
Ellram, L. (1996). The use of the case study method in logistics research. Journal of Business Logistics, 17(2): 93-135.
Ettlie, J.E. and Penner-Hahn, J.D. (1994). Flexibility ratios and manufacturing strategy, Management Science, 40(11): 1444-1454.
Evans, J. and Lindsay, W. (1996). The Management and Control of Quality, 3rd Edition, West Publishing Company.
Ferdows, K. (1997). Making the most of foreign factories. Harvard Business Review, 75(2) Mar/Apr:
Ferdows, K. and DeMeyer, A. (1990). Lasting improvements in manufacturing peformance: in search of a new theory. Journal of Operations Management, 16(1): 168-184.
Fiegenbaum, A. and Karnani, A. (1991). Output Flexibility—A competitive Advantage for Small Firms. Strategic Management Journal, 12: 101-114.
Fine, C. and Freund R. (1990). Optimal Investment in Product-flexible Manufacturing Capacity. Management Science, 1990, 36(4): 449-446.
254
Flynn, B., Sakakibara, s., Schroeder, R., Bates, K., and Flynn, J. (1990). Empirical research methods in operations management. Journal of Operations Management, 1990, 9(2): 250-284.
Gentry, J. (1996). The role of carriers in buyer-supplier strategic partnerships : A supply chain management approach. Journal of Business Logistics, 1996, 17:-55.
Gerwin, D. (1993). Manufacturing Flexibility: A Strategic Perspective, Management Science, 39(4): 395-410.
Gilmore, J. and Pine, B. J. (1997). The four faces of mass customization. Harvard Business Review, 75(1) Jan/Feb.
Gupta, Y.P. and Goyal, S., (1989). Flexibility of manufacturing systems: Concepts and measurements, European Journal of Operational Research, 43: 119-135.
Gupta Y.P. and Somers, T.M., (1992). The measurement of manufacturing flexibility. European Journal of Operational Research, 60: 166-182.
Gupta Y.P. and Somers, T.M., (1996). Business strategy, manufacturing flexibility, and organizational performance relationships. Production and Operations Management, 5(3): 204-232.
Hair, J., Anderson, R., Tatham, R., and Black, W. (1998). Multivariate Data Analysis, 5th Edition, Prentice Hall, c1998, Upper Saddle River, N.J.
Handfield, R. and Melnyk, S. (1998). The scientific theory-building process: a primer using the case of TQM. Journal of Operations Management, 16: 321-339.
Hayes, R. and Pisano, G. (1996). Manufacturing strategy at the intersection of two paradigm shifts. Production Operations Management, 5(1): 25-41.
Hayes, R. and Wheelwright, S. (1979). Link manufacturing process and product life cycles. Harvard Business Review, Jan./Feb. 1979: 133-140.
Hayes, R. and Wheelwright, S. (1984). Restoring Our Competitive Edge. John Wiley and Sons, New York, 1984.
Hill, T. (1989). Manufacturing Strategy: Text and Cases, R. D. Irwin, Homewood, IL, 1989.
Holt, C., Modigliani, F., Muth, J., and Simon, H. (1960). Planning Production, Inventories, and Work Force. Prentice Hall Inc., Englewood Cliffs, NJ.
Huchzermeier, A. and Cohen, M. (1996), Valuing operational flexibility under exchange rate risk, Operations Research, 44(1): 100-113
255
Jaikumar, R. (1986). "Postindustrial Manufacturing", Harvard Business Review, November-December: 69-76.
Jayaram, J., Droge, C., and Vickery, S. (1999). The impact of human resource management practices on manufacturing performance. Journal of Operations Management, 18: 1-20.
Jolliffe, I. (1972). Discarding variables in principal component analysis, I: Artificial data. Applied Statistics, 21: 160-173.
Joreskog, K. and Sorbom, D. (1986). LISREL VI: Analysis of Linear Structural Relationships by Maximum Likelihood, Instrumental Variables and Least Squares Methods. Moorsville, IL.
Joreskog, K. and Sorbom, D. (1993). LISREL VIII. Scientific Software, Chicago, IL.
Jordan, W. and Graves S. (1995). Principles on the Benefits of Manufacturing Process Flexibility, Management Science, April 1995, 41(4): 577-594.
Kaiser, H. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20: 141-151.
Keats, B. and Bracker, J. (1988). Toward A Theory Of Small Firm Performance: A Conceptual Model. Entrepreneurship Theory and Practice, 12(4) Spring 1988.
Kekre, S. and Srinivasan, K., (1990). Broader Product Line: A Necessity to Achieve Success? Management Science, 36(10): 1216-1231.
Kogut, B. and Kulatilaka, N. (1994), Operating flexibility, global manufacturing and the option value of a multinational network, Management Science, Jan. 1994, 40(1): 123-140.
Koste, L. and Malhotra, M. (1999). Theoretical development of the dimensions of manufacturing flexibility. Journal of Operations Management, 18: 75-93.
Lawrence, P. and Lorsch, J. (1967). Organization and Environment: Managing Differentiation and Integration. Boston: Graduate School of Business Administration, Harvard University.
McCutcheon, D. M. and Meredith, J. (1993). Conducting case study research in operations management. Journal of Operations Management, 11(3): 239-256.
McCutcheon, D. Raturi, A. and Meredith, J. (1994). Customization responsiveness squeeze. Sloan Management Review, 35(2): 89-99
256
MacDuffie (1995). Human resource bundles and manufacturing performance: organizational logic and flexible production systems in the world auto industry. Industry and Labor Relations Review, 48: 197-221.
Marcoulides, G. (1998). Modern Methods for Business Research. Lawrence Erlbaum Associates, Hillsdale, NJ.
Medsker, G., Williams, L., Holahan, P. (1994). A review of current practices for evaluating casual models in organizational behavior and human resources management research. Journal of Management, 20(2): 439-464.
Meredith, J. (1998). Building operations management theory through case and field research, Journal of Operations Management, 16: 441-454.
Miles R. and Snow, C. (1984). Fit, failure and the hall of fame. California Management Review, 26(3): 10-28.
Miller, D. and Droge, C. (1986). Psychological and traditional determinants of structure. Administrative Science Quarterly, 31: 539-560.
Miller, J.G. and Roth, A.V. (1994). Taxonomy of manufacturing strategies. Management Science, 40(3): 285-304.
Mills, David E. and Schumann, Laurence (1985), Industry structure with fluctuating demand. The American Economic Review, 75(4): 758-767
Montgomery, D. (1996). Design and Analysis of Experiments, 4th Edition, John Wiley and Sons, New York.
Nahmias, Steven (1997), Production and Operations Analysis, 3rd edition, Irwin publishers. p347.
Nunnally, J. and Bernstein, I. (1994). Psychometric Theory, 3rd Edition, McGraw-Hill, New York.
Narasimhan, R. and Das, A. (1999). An empirical investigation of the contribution of strategic sourcing to manufacturing flexibilities and performance. Decision Sciences, 30(4): 683-718.
Neilson, E. H. (1974). Contingency theory applied to small business organizations. Human Relations, 24: 357-379.
O’Leary-Kelly, Scott E. and Vokurka, Robert J. (1998). The empirical assessment of construct validity, Journal of Operations Management, 16: 387-405.
Penrose, E. (1959). The Theory of the Growth of the Firm. New York, John Wiley.
257
Peteraf, J. (1993). The cornerstone of competitive advantage: a resource-based view, Strategic Management Journal, 14: 179-191.
Pine, J. (1993). Mass Customization: The New frontier in Business Competition, Harvard Business School Press.
Porter M. (1980). Competitive Strategy, Free Press. New York, NY.
Pfeffer, J. and Salancik, G. (1978). The External Control of Organizations. New York: Harper and Row.
Prahalad and Hammel (1990). The core competence of the corporation. Harvard Business Review, 90(3): 79-91.
Raturi, A., Meredith, J., McCutheon, D., and Camm, J. (1990). Coping with the build-to- forecast environment. Journal of Operations Management, 9(2): 230-249.
Ramasesh, R. V. and Jayakumar, M. D. (1991), Measurement of Manufacturing Flexibility: A Value Based Approach, Journal of Operations Management, 10(4): 446-468.
Robinson, R., and Pearce, J. (1984). Research Thrusts in Small Firm Strategic Planning. Academy of Management. The Academy of Management Review, 9(1) Jan 1984.
Safizadeh, M. H. and Ritzman, L. P. (1997). Linking performance drivers in production planning and inventory control to process choice. Journal of Operations Management, 15: 389-403.
SAS® User’s Guide: Statistics, Version 6 Edition, SAS Institute, Cary, NC, 1990.
Scott, R.W. (1992). Organizations: Rational, natural, and Open Systems, 3rd ed. Prentice Hall.
Scudder, G. D. and Hill, C. A. (1998). A review and classification of empirical research in operations management. Journal of Operations Management, 16: 91-101.
Schumacher, R. and Lomax, R. (1996). A Beginner’s Guide to Structural Equation Modeling, Lawrence Erlbaum Associates, Mahway, NJ.
Sethi. A. K. and S. P. Sethi (1990), Flexibility in Manufacturing: A Survey. International Journal of Flexible Manufacturing Systems, 2(4): 289-328.
Shepherd, W. G. (1972). The elements of market structure. Review of Economics and Statistics, 54: 25-37.
258
Skinner, W. (1969), Manufacturing the missing link in corporate strategy. Harvard Business Review, : 136-145.
Skinner, W. (1974). The focused factory. Harvard Business Review, 52(3): 113-121.
Skinner, W. (1985). Manufacturing: the Formidable Competitive Weapon, John Wiley and Sons, New York, 1985.
Skinner, W. (1996). Manufacturing Strategy on the "S" Curve. Production and Operations Management, 5(1): 3-15
Slack, Nigel (1983). Flexibility as a manufacturing objective. International Journal of Operations and Production Management, 1983, 3(3): 4-13.
Slack, Nigel (1987). The flexibility of manufacturing systems. International Journal of Operations and Production Management, 7: 35-45..
Stalk, G. and Hout, T.M. (1990). Competing Against Time, New York: The Free Press.
Stigler, G. (1939). Production and distribution in the short run. The Journal of Political Economy, 47(3): 305-327.
Suarez, F. F., Cusumano, and C. H. Fine (1995). An empirical study of flexibility in manufacturing systems, Sloan Management Review, 37 (1), pp. 25-32.
Suarez, F. F., Cusumano, and C. H. Fine (1996). An empirical study of flexibility in printed circuit board assembly, Operations Research, 44(1).
Swamidass, Paul M. and Suresh, Kotha (1998). Explaining manufacturing technology use, firm size and performance using multidimensional view of technology, Journal of Operations Management, 17: 23-37.
Swamidass, P. M. and Newell, W. T. (1987). Manufacturing strategy, environmental uncertainty, and performance: A path analytic model. Management Science, 33(4): 509-524.
Upton, D.M., 1994. The management of manufacturing flexibility. California Management Review, Winter:.72-89.
Upton, D.M., (1995). Flexibility as process mobility: The management of plant capabilities for quick response manufacturing. Journal of Operations Management, 12: 205-224.
Upton. D.M., (1995). What really makes factories flexible? Harvard Business Review, July-August: 74-84.
259
Upton, David M. (1997). Process Range in Manufacturing: An empirical Study of flexibility, Institute of Operations Research, 43(8): 1079-1092.
Venkatraman, N. and Ramnugam, V. (1987). Measurement of business economic performance: An examination of method convergence. Journal of Management, 1987, 13(1): 109-122.
Venkatraman, N. (1989). The concept of fit in strategy research: toward verbal and statistical correspondence. Academy of Management Review, 14: 423-444.
Vickery, S. (1993). A theory of Production Competence Revisited. Decision Sciences, 22(3): 635-643.
Vickery, S., Calantone, R., and Droge, C. (1999). Supply chain flexibility: an empirical study. The Journal of Supply Chain Management. Summer: 16-24.
Vickery, S. Droge, C. and Germain R.(1999). The relationship between product customization and organizational structure. Journal of Operations Management, 17: 377-391.
Voss, C. (1992). Manufacturing Strategy: Process and Content. London, Chapman & Hall.
Wacker, J. (1996). A theoretical model of manufacturing lead times and their relationship to a manufacturing goal hierarchy. Decision Sciences, 27(3) Summer 1996.
Ward, P., Duray, R., Leong, G., and Sum, C. (1995). Business environment, operations strategy, and performance: An empirical study of Singapore manufacturers. Journal of Operations Management. 13: 99-115.
Ward, P., Leong, K., and Boyer, K. (1996). Manufacturing proactiveness and performance. Decision Sciences, 25(3): 337-358.
Wernerfelt, B. (1984). A resourced-based view of the firm. Strategic Management Journal, 5: 171-180.
Whetten, David A. (1989). What constitutes a theoretical contribution. Academy of Management Review, 14(4): 490-495.
Wheelwright, S. and Bowen, H. (1996). The challenge of manufacturing advantage. Production and Operations Management, 5(1): 42-58.
Wheelwright, S. and Hayes, R. (1985). Competing through manufacturing. Harvard Business Review, 63(1): 99-109.
260
White, G. (1996). A survey and taxonomy of strategy-related performance measures for manufacturing. International Journal of Operations and Production Management, 16(3): 42-61.
White, G. (1996). A meta-analysis model of manufacturing capabilities. Journal of Operations Management, 14: 315-331.
Yin, R. (1994). Case Study Research, 2nd Edition. Thousand Oaks, California: Sage Pubilcations, (1994).
261
Appendices
Appendix A. Supporting Information for Chapter 3
A1. Regression Diagnostics and Remedial Procedures (Sample #1)
1. OF2 ------SIC=3564 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
16 DONALDSON CO INC 257651109 3.94115 -1.88552 -2.22816 12.5335
------SIC=3571 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
17 DYNATEM INC 268145109 0.017 -2.67394 -2.94518 0.69946 18 MIKROS SYSTEMS CORP 598626307 0.019 -2.62208 -2.87441 0.71284
------SIC=3576 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
127 CABLETRON SYSTEMS 126920107 3.50210 0.09097 0.09033 1.09586 128 3COM CORP 885535104 2.84567 -0.20286 -0.20148 1.17212 129 CISCO SYSTEMS INC 17275R102 4.93367 -2.72553 -2.86002 2.78759
2. VF1 ------SIC=3559 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
95 CRYO-CELL INTERNATIONAL INC 228895108 0.01133 3.72294 4.22008 0.61916 96 MOTORVAC TECHNOLOGIES INC 620105106 0.04200 3.65398 4.11876 0.63287
------SIC=3560 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
157 TB WOODS 872226105 1.024 2.76332 3.70056 0.3307 158 COHESANT TECHNOLOGIES INC 192480101 0.077 2.03265 2.28514 0.6662
------SIC=3572 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
302 SEAGATE TECHNOLOGY 811804103 36.8992 1.04544 1.04738 2.13259
------SIC=3575 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
303 IMAGE SYSTEMS CORP 45244J109 0.04133 -2.60542 -4.36140 0.141 304 PIXTECH INC 72583K109 0.12000 1.83871 2.14404 0.598
------SIC=3585 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
512 HUSSMANN INTERNATIONAL INC 448110106 8.7000 -2.86627 -3.97876 0.28519
262
3. VF2 ------SIC=3531 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
29 GRADALL INDUSTRIES INC 38411P107 0.6313 -2.45339 -6.06792 0.03108
37 CATERPILLAR INC 149123101 61.1778 0.73655 0.70998 5.56456
------SIC=3533 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
39 IRI INTERNATIONAL CORP 45004F107 1.4890 . . . 40 UNIFAB INTERNATIONAL INC 90467L100 0.4370 -2.52823 -3.99302 0.17869 41 DAILEY INTL INC -CL A 23380G106 0.4850 1.38143 1.45691 0.89786 42 BOLT TECHNOLOGY CORP 097698104 0.1014 1.35160 1.41832 0.92113
------SIC=3537 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX 57 NACCO INDUSTRIES -CL A 629579103 8.12645 -0.28758 -0.2381 27.2869
------SIC=3559 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
96 TOOLEX INTERNATIONAL NV N8715N103 0.07400 . . . 97 ICOS VISION SYSTEMS CORP NV B49233107 . 6.68622 13.1323 0.06851 98 CRYO-CELL INTERNATIONAL INC 228895108 0.01133 2.14397 2.2125 0.90100 99 TRIKON TECHNOLOGIES INC 896187101 0.41567 -1.82586 -1.8631 0.94002
157 APPLIED MATERIALS INC 038222105 4.2429 0.35837 0.35575 1.58930 158 MILACRON INC 598709103 10.0922 0.66305 0.65993 1.79719
------SIC=3560 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
160 SI DIAMOND TECHNOLOGY INC 784249104 0.070 2.54369 3.19132 0.4295 161 HELIX TECHNOLOGY CORP 423319102 0.402 1.95856 2.17493 0.6996
168 INGERSOLL-RAND CO 456866102 37.287 -0.65698 -0.64487 1.1495 177 ABB-ASEA BROWN BOV GRP -ADR 00299Y931 212.529 0.86926 0.86227 50.5815
------SIC=3567 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
205 RESEARCH INC 760898106 0.21035 2.11982 5.95799 0.01875 ------SIC=3569 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
215 UNOVA INC 91529B106 7.0600 -3.77010 -15.9081 0.00370 216 TYCO INTERNATIONAL LTD 902124106 22.7762 3.23655 5.6931 0.58224
------SIC=3570 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
232 HEWLETT-PACKARD CO 428236103 87.715 -1.99569 -26.3291 0.00004
------SIC=3571 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
238 DAUPHIN TECHNOLOGY INC 238326102 0.012 -2.29749 -2.45225 0.79315 239 NEOWARE SYSTEMS INC 64065P102 0.066 2.26439 2.41101 0.80092 240 NEXAR TECHNOLOGIES INC 65332P106 0.073 1.86472 1.93443 0.88884 241 DELL COMPUTER CORP 247025109 5.465 -1.80651 -1.86792 0.90411
------SIC=3572 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
278 OVERLAND DATA INC 690213103 0.2205 -2.89071 -3.44095 0.52178 279 HMT TECHNOLOGY CORP 403917107 1.3700 2.44135 2.72679 0.66829 280 CAMBEX CORP 132008103 0.1080 2.19113 2.37948 0.75491 281 MTI TECHNOLOGY CORP 553903105 0.5622 -1.54507 -1.58981 0.93044
263
------SIC=3575 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX 306 IMAGE SYSTEMS CORP 45244J109 0.04133 -3.13939 -24.8018 0.000
------SIC=3576 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX 320 ECHELON CORP 27874N105 . 3.91026 4.38317 0.64312 321 INTERLINK CMPTR SCIENCES INC 458747102 0.16933 2.92332 3.09488 0.80812 322 VIDEOSERVER INC 926918103 0.16925 2.68428 2.81180 0.84304 323 KOFAX IMAGE PRODUCTS INC 500200100 0.15900 -2.29494 -2.36825 0.89534 324 ELECTRONICS FOR IMAGING INC 286082102 0.26800 -2.16229 -2.22139 0.91084 325 ADAPTIVE SOLUTIONS INC 00650P305 0.04400 -1.81163 -1.84190 0.95094 326 CABLETRON SYSTEMS 126920107 3.50210 2.06843 2.11863 0.96800
------SIC=3577 ------
OBS CONAME CUSIP M_SALES STUDX RSTUDX COVRATX
1 WAVE SYSTEMS CORP -CL A 943526103 0.00004 . . . 2 ACCENT COLOR SCIENCES INC 004305108 0.00983 . . . 3 TELEPANEL SYSTEMS INC 87943U209 0.00986 -1.71325 -1.73934 0.95608 4 TOP IMAGE SYSTEMS LTD M87896102 0.01493 . . . 5 COMMUN INTELLIGENCE 20338K106 0.01926 -1.33720 -1.34538 0.99118 6 ION NETWORKS INC 46205P100 0.02250 -2.29645 -2.37588 0.88649 7 VOICE CONTROL SYSTEMS INC 92861B100 0.02455 -1.73805 -1.76571 0.95348 8 SEDONA CORP 815677109 0.02532 -1.99169 -2.03876 0.92505 9 TRINITECH SYSTEMS INC 896406105 0.02590 -1.06463 -1.06572 1.01149 10 SC&T INTERNATIONAL INC 783975105 0.03096 -0.07302 -0.07246 1.04697 11 MICROFIELD GRAPHICS INC 59506W104 0.03337 -0.96502 -0.96451 1.01780 12 DIGITAL BIOMETRICS INC 253833107 0.04183 -1.56160 -1.57917 0.97119 13 ISOMET CORP 464893106 0.04953 -2.00726 -2.05573 0.92319 14 LABTEC INC 505450106 0.04970 -0.68319 -0.68040 1.03238 15 MEDPLUS INC/OH 58504P103 0.05036 -0.51794 -0.51505 1.03864 16 ACRES GAMING INC 004936100 0.05461 -0.16312 -0.16191 1.04629 17 INTERLINK ELECTRONICS 458751104 0.06336 -1.25337 -1.25891 0.99787 18 BULL RUN CORP 120182100 0.06691 -0.09548 -0.09476 1.04684 19 SOFTNET SYSTEMS INC 833964109 0.08593 -0.41968 -0.41705 1.04154 20 MITEK SYSTEMS INC 606710200 0.09452 -0.46617 -0.46339 1.04023
64 GENICOM CORP 372282103 1.88768 0.51504 0.51215 1.03816 65 LOGITECH INTL S A -ADR 541419107 2.30101 1.12203 1.12428 1.00694 66 DIAMOND MULTIMEDIA SYS INC 252714100 2.94400 1.97285 2.01826 0.92663 67 BELL & HOWELL COMPANY 077852101 5.36300 0.69306 0.69030 1.03143 68 CREATIVE TECHNOLOGY LTD Y1775U107 5.49900 1.93214 1.97409 0.93159 69 LEXMARK INTL GRP INC -CL A 529771107 15.14900 0.46791 0.46512 1.04796 70 CANON INC -ADR 138006309 75.70500 -0.12362 -0.12270 1.51851 71 XEROX CORP 984121103 108.54900 -0.46208 -0.45931 3.04613
------SIC=3578 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
464 AFFINITY TECHNOLOGY GRP INC 00826M103 0.1078 -3.55229 -5.96614 0.1325
484 NCR CORP 62886E108 55.5941 0.46539 0.45558 97.2465
------SIC=3580 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
20 PREMARK INTERNATIONAL INC 740459102 21.9357 -2.05905 -2.28864 32.8950
------SIC=3585 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
515 RTI INC 749739207 0.0552 3.03376 4.50702 0.22364 74 RARE MEDIUM GROUP INC 75382N109 0.0205 -1.89095 -2.07773 0.74749
264
4. VF3
------SIC=3531 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
29 GRADALL INDUSTRIES INC 38411P107 0.6313 -2.33550 -4.60181 0.07715
------SIC=3537 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
53 OMNIQUIP INTERNATIONAL INC 681969101 1.31667 -1.73013 -30.0032 0.0000
------SIC=3540 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
58 PRODUCTIVITY TECHNOLOGIES CP 743088106 0.2000 -2.87719 -4.33684 0.21089 ------SIC=3559 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
96 TOOLEX INTERNATIONAL NV N8715N103 0.07400 . . . 97 CRYO-CELL INTERNATIONAL INC 228895108 0.01133 4.18256 4.92767 0.53034 98 TRIKON TECHNOLOGIES INC 896187101 0.41567 -3.85587 -4.40864 0.59630
------SIC=3560 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
159 PFEIFFER VACUUM TECH -ADR 717067102 0.719 . . . 160 SI DIAMOND TECHNOLOGY INC 784249104 0.070 2.44599 2.99316 0.4746 161 HELIX TECHNOLOGY CORP 423319102 0.402 2.36628 2.84172 0.5115
------SIC=3569 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
212 CECO FILTERS INC 150034106 0.0470 . . . 213 COMMODORE SEPARATION TCHNLGY 202909107 0.0180 . . . 214 MANSUR INDUSTRIES INC 564491108 0.0660 . . . 215 UNOVA INC 91529B106 7.0600 -3.62388 -9.92228 0.02087 216 TYCO INTERNATIONAL LTD 902124106 22.7762 3.37266 6.62789 0.37373
------SIC=3575 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
306 IMAGE SYSTEMS CORP 45244J109 0.04133 -3.13799 -24.0664 0.000
317 MEMOREX TELEX NV -SPON ADR 586014102 6.95000 -0.80166 -0.7862 195.559
------SIC=3576 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
318 AUGMENT SYSTEMS INC 051058105 0.03100 . . . 319 EXTENDED SYSTEMS INC 301973103 0.27700 . . . 320 ECHELON CORP 27874N105 . 4.19592 4.80429 0.59077 321 INTERLINK CMPTR SCIENCES INC 458747102 0.16933 3.43049 3.72931 0.72688 322 KOFAX IMAGE PRODUCTS INC 500200100 0.15900 -2.70023 -2.83041 0.84104 323 VIDEOSERVER INC 926918103 0.16925 2.61049 2.72617 0.85340
------SIC=3578 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
464 AFFINITY TECHNOLOGY GRP INC 00826M103 0.1078 -2.91757 -3.82223 0.3578 465 ELECTRONIC RETAILNG SYS INTL 285825105 0.0632 -1.43783 -1.48246 0.9327
------SIC=3585 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
515 HUSSMANN INTERNATIONAL INC 448110106 8.7000 -3.67785 -9.05711 0.02879
265
5. VF4
------SIC=3523 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
8 CASE CORP 14743R103 17.2000 2.92951 4.82810 0.15339
------SIC=3531 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
29 GRADALL INDUSTRIES INC 38411P107 0.6313 -2.29271 -4.25318 0.09819
------SIC=3537 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
53 OMNIQUIP INTERNATIONAL INC 681969101 1.31667 -1.72406 -14.6733 0.0002
------SIC=3540 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
58 DYNAMOTIVE TECHNOLOGIES CORP 267924108 0.0330 3.19035 5.88415 0.09432 59 PRODUCTIVITY TECHNOLOGIES CP 743088106 0.2000 -1.71596 -1.86075 0.78730
73 BLACK & DECKER CORP 091797100 26.9950 1.14506 1.15901 2.61743
------SIC=3559 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
96 TOOLEX INTERNATIONAL NV N8715N103 0.07400 . . . 97 CRYO-CELL INTERNATIONAL INC 228895108 0.01133 4.42769 5.35101 0.47898 98 EMCORE CORP 290846104 0.21950 2.68714 2.84109 0.81575 99 SONO-TEK CORP 835483108 0.02664 1.90786 1.95203 0.93229 100 MICRION CORP 59479P102 0.19500 -1.84610 -1.88496 0.93778 101 DT INDUSTRIES INC 23333J108 2.18000 -1.81754 -1.85410 0.94238 102 QC OPTICS INC 746934108 0.05900 1.60403 1.62585 0.96724 103 MATTSON TECHNOLOGY INC 577223100 0.25975 1.30939 1.31739 0.99404
------SIC=3560 ------
OBS CONAME CUSIP M_SALES M_EMP STUDX RSTUDX COVRATX
68 SI DIAMOND TECHNOLOGY INC 784249104 0.018 0.070 3.37095 6.06307 0.1017 69 TECHNOLOGY GENERAL CP 878695105 0.026 0.052 -0.02635 -0.02552 1.2107
------SIC=3564 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
193 IONIC FUEL TECHNOLOGY INC 462211103 0.01867 2.50239 3.88290 0.19330
------SIC=3569 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
215 UNOVA INC 91529B106 7.0600 -3.30407 -6.11814 0.09976 216 TYCO INTERNATIONAL LTD 902124106 22.7762 3.18061 5.38516 0.67828
------SIC=3570 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
232 HEWLETT-PACKARD CO 428236103 87.715 -1.91530 -5.76049 0.01524
------SIC=3571 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
238 PARAVANT INC 699376109 0.098 -2.25280 -2.39664 0.80366 239 DAUPHIN TECHNOLOGY INC 238326102 0.012 -2.07448 -2.17991 0.84429 240 VITECH AMERICA INC 928489103 0.500 -1.97481 -2.06208 0.86574 241 NEXAR TECHNOLOGIES INC 65332P106 0.073 - 1.95373 -2.03744 0.87036
------SIC=3575 ------
266
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
306 IMAGE SYSTEMS CORP 45244J109 0.04133 -2.38408 -3.44265 0.254 307 MEGATECH CORP 585160104 0.02263 1.96712 2.38345 0.513
317 MEMOREX TELEX NV -SPON ADR 586014102 6.95000 0.77025 0.75341 197.628
------SIC=3576 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
318 AUGMENT SYSTEMS INC 051058105 0.03100 . . . 319 EXTENDED SYSTEMS INC 301973103 0.27700 . . . 320 ECHELON CORP 27874N105 . 4.16822 4.76225 0.59591 321 INTERLINK CMPTR SCIENCES INC 458747102 0.16933 3.31091 3.57506 0.74680 322 VIDEOSERVER INC 926918103 0.16925 2.80009 2.94782 0.82634 323 RIT TECHNOLOGIES LTD M8215N109 . -1.92317 -1.96135 0.93889 324 ADAPTIVE SOLUTIONS INC 00650P305 0.04400 -1.86202 -1.89573 0.94575 325 FASTCOMM COMMUNICATIONS CORP 311871107 0.04445 1.79756 1.82692 0.95234 326 FRANKLIN TELECOMMUNICATIONS 354727208 0.04860 -1.52617 -1.54087 0.97774 327 CABLETRON SYSTEMS 126920107 3.50210 1.90920 1.94633 0.98646
------SIC=3577 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
393 ACCENT COLOR SCIENCES INC 004305108 0.09700 . . . 394 WAVE SYSTEMS CORP -CL A 943526103 0.04500 . . . 395 TOP IMAGE SYSTEMS LTD M87896102 0.03100 . . . 396 SECURE COMPUTING CORP 813705100 0.34533 2.92017 3.10552 0.79398 397 VOICE CONTROL SYSTEMS INC 92861B100 0.03247 2.59505 2.71770 0.84436 398 MEDPLUS INC/OH 58504P103 0.07300 2.53955 2.65320 0.85249 399 NEWCOM INC 651093106 0.11200 -1.84776 -1.88306 0.94148 400 SECURITY DYNAMICS TECH INC 814208104 0.30850 1.81081 1.84341 0.94556 401 TRANSACT TECHNOLOGIES INC 892918103 0.21750 1.79408 1.82551 0.94741
------SIC=3578 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
464 INTL AUTOMATED SYSTEMS INC 459039103 0.0235 3.37232 5.18053 0.1893 465 AFFINITY TECHNOLOGY GRP INC 00826M103 0.1078 -1.74502 -1.85349 0.8281
------SIC=3580 ------
OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
495 INNOVATIVE MEDICAL SERVICES 45766R109 0.01775 2.65260 3.30306 0.44032 496 TURBOCHEF TECHNOLOGIES INC 900006107 0.02600 1.24841 1.26944 0.99019
514 PREMARK INTERNATIONAL INC 740459102 21.9357 2.36055 2.76083 26.8327
------SIC=3585 ------OBS CONAME CUSIP M_EMP STUDX RSTUDX COVRATX
515 AMERICAN STANDARD INC 029717006 46.1000 3.08923 4.70859 0.38277 516 RTI INC 749739207 0.0552 2.23498 2.60932 0.58637 517 AMERN STANDARD CO INC 029712106 41.0250 -2.20241 -2.55456 0.83407 518 UNITED DOMINION INDUSTRIES 909914103 12.5195 -1.58117 -1.66670 0.87204 519 TECUMSEH PRODUCTS CO -CL A 878895200 12.5086 -0.68054 -0.66868 1.14006
527 RARE MEDIUM GROUP INC 75382N109 0.0205 0.28506 0.27671 1.22710
267
A2. List of 2100 Firms and Years of Data in Sample #2
# Yrs 42 2820 PROTEIN POLYMER TECHNOLOGIES 9 # SIC Company Name of 43 2820 WELLMAN INC 13 Data 44 2820 ZOLTEK COS INC 7 1 2800 AKZO NOBEL NV -ADR 14 45 2821 ADVANCED POLYMER SYSTEMS 13 2 2800 ARCH CHEMICALS INC 3 46 2821 AT PLASTICS INC 6 3 2800 BALCHEM CORP -CL B 19 47 2821 BAIRNCO CORP 19 4 2800 BAYER A G -SPON ADR 5 48 2821 BORDEN CHEM&PLAST -LP COM 11 5 2800 CATALYTICA INC 7 49 2821 CHURCHILL TECHNOLOGY INC 11 6 2800 COURTAULDS PLC -ADR 18 50 2821 DEXTER CORP 20 7 2800 DESC S A DE C V -SPON ADR 4 51 2821 DOW CHEMICAL 20 8 2800 DETREX CORP 20 52 2821 DOW CORNING CORP 19 9 2800 FERRO CORP 20 53 2821 EASTMAN CHEMICAL CO 7 10 2800 FMC CORP 20 54 2821 ELECTROCHEMICAL INDUS FRUTAR 13 11 2800 HARRIS CHEMICAL NTH AMER INC 5 55 2821 GEON COMPANY 7 12 2800 HOECHST AG -SPON ADR 4 56 2821 ICO INC 19 13 2800 IMPERIAL CHEM INDS PLC -ADR 19 57 2821 LANDEC CORP 4 14 2800 MONSANTO CO 20 58 2821 LAWTER INTERNATIONAL INC 20 15 2800 RHONE-POULENC SA -ADR 1/4 12 59 2821 MCWHORTER TECHNOLOGIES INC 5 16 2810 AIR PRODUCTS & CHEMICALS INC 20 60 2821 MILLENNIUM CHEMICALS INC 4 17 2810 AMERICAN PACIFIC CORP 20 61 2821 PLANET POLYMER TECHNOLGS INC 4 18 2810 ATMI INC 6 62 2821 PT TRI POLYTA INDONSIA -ADR 4 19 2810 BOC GROUP PLC -SP ADR 16 63 2821 ROGERS CORP 19 20 2810 CALGON CARBON CORP 13 64 2821 ROHM & HAAS CO 20 21 2810 GENERAL CHEMICAL CORP 9 65 2821 SCHULMAN (A.) INC 20 22 2810 GENERAL CHEMICAL GRP INC 4 66 2821 SHANGHAI PETROCHEM LTD -ADR 6 23 2810 GEORGIA GULF CORP 14 67 2821 SOLUTIA INC 4 24 2810 HITOX CORP OF AMERICA 12 68 2833 BIO TECHNOLOGY GENERAL CORP 16 25 2810 KERR-MCGEE CORP 20 69 2833 CHIREX INC 3 26 2810 LSB INDUSTRIES INC 19 70 2833 CYANOTECH CORP 13 27 2810 MINERALS TECHNOLOGIES INC 8 71 2833 HAUSER INC 12 28 2810 NL INDUSTRIES 20 72 2833 MARTEK BIOSCIENCES CORP 7 29 2810 NORTH AMERN SCIENTIFIC INC 3 73 2833 NAPRO BIOTHERAPEUTICS 5 30 2810 OM GROUP INC 7 74 2833 NUTRACEUTICAL INTL CP 3 31 2810 PIONEER COS INC -CL A 7 75 2833 OXIS INTERNATIONAL INC 19 32 2810 PRAXAIR INC 8 76 2834 ABBOTT LABORATORIES 20 33 2810 TETRA TECHNOLOGIES INC/DE 10 77 2834 AGOURON PHARMACEUTICALS INC 13 34 2810 TRANS RESOURCES INC 12 78 2834 AKORN INC 13 35 2810 USEC INC 3 79 2834 ALLERGAN INC 11 36 2810 VALHI INC 12 80 2834 ALPHA 1 BIOMEDICALS INC 13 37 2820 CROMPTON & KNOWLES CORP 20 81 2834 ALPHARMA INC -CL A 16 38 2820 DU PONT (E I) DE NEMOURS 20 82 2834 ALZA CORP 20 39 2820 GLASSMASTER CO 16 83 2834 AMBI INC 12 40 2820 HOECHST CELANESE CORP 10 84 2834 AMERICAN HOME PRODUCTS CORP 20 41 2820 MARTIN COLOR-FI INC 6 85 2834 ANESTA CORP 6
268
86 2834 ASTRA AB -SPON ADR A 5 134 2834 INTERNEURON PHARMACEUTICALS 9 87 2834 ASTRAZENECA PLC -SPON ADR 6 135 2834 IVAX CORP 12 88 2834 ATRIX LABS INC 9 136 2834 IVC INDUSTRIES INC 6 89 2834 BARR LABORATORIES INC 13 137 2834 JOHNSON & JOHNSON 20 90 2834 BAUSCH & LOMB INC 20 138 2834 JONES PHARMA INC 14 91 2834 BENTLEY PHARMACEUTICALS 9 139 2834 K V PHARMACEUTICAL -CL A 19 92 2834 BIGMAR INC 3 140 2834 KING PHARMACEUTICALS INC 3 93 2834 BIOSPECIFICS TECHNOLOGIES CP 8 141 2834 LABORATORIO CHILE -SPON ADR 3 94 2834 BIOVAIL CORP INTERNATIONAL 6 142 2834 LESCARDEN INC 10 95 2834 BONE CARE INTERNATIONAL INC 3 143 2834 LILLY (ELI) & CO 20 96 2834 BOSTON LIFE SCIENCES INC 8 144 2834 MACROCHEM CORP/DE 14 97 2834 BRADLEY PHARMACEUTICL -CL A 8 145 2834 MEDEVA PLC -SPON ADR 9 98 2834 BRISTOL MYERS SQUIBB 20 146 2834 MEDICIS PHARMACEUT CP -CL A 8 99 2834 BRIT BIO-TECH GRP PLC -ADR 8 147 2834 MENLEY & JAMES INC 7 100 2834 CARACO PHARMACEUTICAL LABS 6 148 2834 MERCK & CO 20 101 2834 CARRINGTON LABS 16 149 2834 MGI PHARMA INC 17 102 2834 CHANTAL PHARMACEUTICAL CORP 14 150 2834 MYLAN LABORATORIES 19 103 2834 CHATTEM INC 19 151 2834 NASTECH PHARMACEUTICAL 13 104 2834 CHEM INTERNATIONAL INC 3 152 2834 NATURADE INC 5 105 2834 CHESAPEAKE BIOLOGICAL -CL A 12 153 2834 NATURAL ALTERNATIVES 12 106 2834 CHIRON CORP 16 154 2834 NATURES SUNSHINE PRODS INC 20 107 2834 CIMA LABS INC 5 155 2834 NBTY INC 17 108 2834 COLUMBIA LABORATORIES INC 10 156 2834 NEXSTAR PHARMACEUTICALS 7 109 2834 COPLEY PHARMACEUTICAL INC 8 157 2834 NM HOLDINGS INC 3 110 2834 CORTECS PLC -SPON ADR 7 158 2834 NOVEN PHARMACEUTICALS INC 11 111 2834 CYGNUS INC 10 159 2834 NOVO-NORDISK A/S -ADR 19 112 2834 CYPROS PHARMACEUTICAL CORP 6 160 2834 NSA INTERNATIONAL INC 7 113 2834 DALTEX MED SCIENCES 13 161 2834 PDK LABS INC 10 114 2834 DERMA SCIENCES INC 5 162 2834 PERRIGO COMPANY 8 115 2834 DRAXIS HEALTH INC 11 163 2834 PFIZER INC 20 116 2834 DURA PHARMACEUTICALS INC 9 164 2834 PHARMACEUTICAL FORMULA INC 17 117 2834 DURAMED PHARMACEUTICALS INC 13 165 2834 PHARMACEUTICAL RES INC 15 118 2834 DYNAGEN INC 8 166 2834 PHARMACIA & UPJOHN INC 20 119 2834 ELAN CORP PLC -ADR 16 167 2834 PHARMOS CORP 10 120 2834 EMISPHERE TECHNOLOGIES INC 11 168 2834 POLYDEX PHARMACEUTICALS LTD 15 121 2834 ESCALON MEDICAL CORP 7 169 2834 PROCYTE CORP 10 122 2834 ETHICAL HLDGS LTD -SPON ADR 5 170 2834 QLT PHOTOTHERAPEUTICS 10 123 2834 FLAMEL TECHNOLOGIES SA -ADR 3 171 2834 RELIV INTERNATIONAL INC 7 124 2834 FOREST LABORATORIES -CL A 19 172 2834 REXALL SUNDOWN INC 7 125 2834 FUISZ TECHNOLOGIES LTD 4 173 2834 ROBERTS PHARMACEUTICAL CORP 10 126 2834 GENENTECH INC 19 174 2834 ROCHE HOLDINGS LTD -SP ADR 5 127 2834 GENSIA SICOR INC 8 175 2834 SCHERING-PLOUGH 20 128 2834 GLAXO WELLCOME PLC -SP ADR 19 176 2834 SCICLONE PHARMACEUTICALS INC 4 129 2834 GLIATECH INC 5 177 2834 SEQUESTER HOLDINGS INC 4 130 2834 GUILFORD PHARMACEUTICAL INC 4 178 2834 SEQUUS PHARMACEUTICALS INC 12 131 2834 HALSEY DRUG CO INC 13 179 2834 SMITHKLINE BEECHAM PLC -ADR 12 132 2834 HI TECH PHARMACAL CO INC 7 180 2834 SUMMA RX LABORATORIES INC 13 133 2834 ICN PHARMACEUTICALS INC 17 181 2834 TARO PHARMACEUTICAL INDS LTD 10
269
182 2834 TEVA PHARM INDS -ADR 14 230 2835 KAIRE HOLDINGS INC 8 183 2834 THERAGENICS CORP 13 231 2835 MALLINCKRODT INC 20 184 2834 TRANSDERM LABORATORIES CORP 4 232 2835 MATRITECH INC 7 185 2834 TWINLAB CORP 3 233 2835 MERIDIAN DIAGNOSTICS INC 14 186 2834 U S BIOSCIENCE INC 10 234 2835 METRA BIOSYSTEMS INC 5 187 2834 UNIMED PHARMACEUTICALS INC 19 235 2835 MOLECULAR BIOSYSTEMS INC 16 188 2834 UNITED GUARDIAN INC 19 236 2835 NEO RX CORPORATION 11 189 2834 USANA INC 4 237 2835 NEOGEN CORP 10 190 2834 VEREX LABORATORIES INC 15 238 2835 NEOPROBE CORP 7 191 2834 VIRBAC CORP 5 239 2835 ONCOR INC 13 192 2834 WARNER CHILCOTT PLC -ADR 3 240 2835 OSI PHARMACEUTICALS INC 12 193 2834 WARNER-LAMBERT CO 20 241 2835 OSTEX INTERNATIONAL INC 5 194 2834 WATSON PHARMACEUTICALS INC 7 242 2835 PACIFIC PHARMACEUTICALS INC 13 195 2834 WEIDER NUTRITION INTL -CL A 3 243 2835 PARACELSIAN INC 6 196 2834 ZONAGEN INC 6 244 2835 QUIDEL CORP 18 197 2835 ACCUMED INTERNATIONAL INC 6 245 2835 SALIVA DIAGNOSTIC SYS INC 5 198 2835 ADVANCED MAGNETICS INC 14 246 2835 SELFCARE INC 3 199 2835 ALLIANCE PHARMACEUTICAL CP 14 247 2835 SPECTRAL DIAGNOSTICS INC 5 200 2835 AMER BIOGENETIC SCI -CL A 7 248 2835 SYNBIOTICS CORP 16 201 2835 AMERICAN BIO MEDICA CORP 3 249 2835 TECHNICAL CHEMICALS & PRODS 5 202 2835 ARQULE INC 3 250 2835 TECHNICLONE CORP 16 203 2835 AVANT IMMUNOTHERAPEUTICS INC 13 251 2835 TRINITY BIOTECH -SPON ADR 7 204 2835 BIOCHEM PHARMA INC 9 252 2835 VENTANA MEDICAL SYSTEM INC 4 205 2835 BIOMIRA INC 11 253 2835 XENOMETRIX INC 4 206 2835 BIOPOOL INTERNATIONAL INC 11 254 2835 ZAXIS INTL INC 7 207 2835 BIOSITE DIAGNOSTICS INC 4 255 2836 ADVANCED TISSUE SCI -CL A 10 208 2835 BIOSOURCE INTERNATIONAL INC 15 256 2836 ALFACELL CORP 5 209 2835 BOSTON BIOMEDICA INC 3 257 2836 AMGEN INC 17 210 2835 CHEMTRAK INC 7 258 2836 ANIKA THERAPEUTICS INC 6 211 2835 CISTRON BIOTECHNOLOGY INC 13 259 2836 AQUILA BIOPHARM INC 15 212 2835 COLLEGIATE PACIFIC INC 11 260 2836 BIOGEN INC 17 213 2835 CYTOGEN CORP 14 261 2836 BIOMATRIX INC 9 214 2835 DIAGNOSTIC PRODUCTS CORP 18 262 2836 BIOMUNE SYSTEMS INC 9 215 2835 DIGENE CORP 4 263 2836 BIORA AB -SP ADR 3 216 2835 ENDOGEN INC 6 264 2836 BIORELEASE CORP 7 217 2835 E-Z-EM-INC -CL A 16 265 2836 BIOTIME INC 7 218 2835 GENELABS TECHNOLOGIES INC 8 266 2836 CAMBREX CORP 13 219 2835 GENOME THERAPTCS 18 267 2836 CELOX LABORATORIES INC 8 220 2835 GOLDEN PHARMACEUTICALS INC 19 268 2836 CENTOCOR INC 17 221 2835 HEALTHCARE TECHNOLOGIES LTD 8 269 2836 CORVAS INTERNATIONAL INC 8 222 2835 HEMAGEN DIAGNOSTICS INC 7 270 2836 CREATIVE BIOMOLECULES INC 7 223 2835 HESKA CORP 3 271 2836 ENZON INC 15 224 2835 HYCOR BIOMEDICAL INC 16 272 2836 EPOCH PHARMACEUTICALS 6 225 2835 IDEXX LABS INC 9 273 2836 GENSET SA -ADR 3 226 2835 IMMUCELL CORP 12 274 2836 GENTA INC 7 227 2835 IMMUCOR INC 14 275 2836 GENZYME CORP-CONSOLIDATED 11 228 2835 IMMUNOMEDICS INC 14 276 2836 GENZYME GENERAL 3 229 2835 INVITRO INTL 6 277 2836 IDEC PHARMACEUTICALS CORP 9
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278 2836 IGI INC 17 326 2844 CARSON INC -CL A 4 279 2836 IMMUNEX CORP 17 327 2844 CARTER-WALLACE INC 19 280 2836 INTEGRA LIFESCIENCES CORP 4 328 2844 CCA INDUSTRIES INC 14 281 2836 INTERFERON SCIENCES 17 329 2844 COLGATE-PALMOLIVE CO 20 282 2836 LIFE MED SCIENCES INC 6 330 2844 DEL LABORATORIES INC 20 283 2836 LIFE TECHNOLOGIES INC 15 331 2844 FOUNTAIN PHARMACEUTICALS INC 8 284 2836 LIPOSOME COMPANY INC 14 332 2844 FRENCH FRAGRANCES INC 3 285 2836 MEDAREX INC 8 333 2844 GUEST SUPPLY INC 17 286 2836 MEDIMMUNE INC 9 334 2844 HUMAN PHEROMONE SCIENCES INC 6 287 2836 NABI INC 19 335 2844 HYAL PHARMACEUTICAL CP 16 288 2836 NORTH AMERICAN VACCINE INC 9 336 2844 HYDRON TECHNOLOGIES INC 18 289 2836 ORGANOGENESIS INC 12 337 2844 HYMEDIX INC 5 290 2836 REPLIGEN CORP 13 338 2844 JEAN PHILIPPE FRAGRANC 12 291 2836 RIBI IMMUNOCHEM RESEARCH INC 17 339 2844 LAMAUR CORP 3 292 2836 SANGSTAT MEDICAL CORP 6 340 2844 LAUDER ESTEE COS INC -CL A 4 293 2836 SEROLOGICALS CORP 4 341 2844 LEE PHARMACEUTICALS 20 294 2836 SIGMA-ALDRICH 20 342 2844 NUTRAMAX PRODUCTS INC 9 295 2836 SUPERGEN INC 3 343 2844 ORALABS HOLDING CORP 6 296 2836 SYMBOLLON CORP -CL A 6 344 2844 PARLUX FRAGRANCES INC 13 297 2836 TECHNE CORP 10 345 2844 REVLON CONSUMER PRODUCTS CP 7 298 2836 VAXCEL INC 6 346 2844 REVLON INC -CL A 4 299 2836 VI TECHNOLOGIES INC 3 347 2844 SCOTT'S LIQUID GOLD 19 300 2836 VIRAGEN INC 14 348 2844 SENETEK PLC -ADR 10 301 2836 VITRO DIAGNOSTICS INC 11 349 2844 STEPHAN CO 19 302 2836 XECHEM INTERNATIONAL INC 5 350 2844 THERMOLASE CORP 6 303 2836 XOMA LTD 14 351 2844 TRISTAR CORP 16 304 2840 BENCKISER N V 3 352 2844 ZEGARELLI GROUP INTL INC 4 305 2840 CHURCH & DWIGHT INC 20 353 2851 ARROW MAGNOLIA INTL INC 5 306 2840 DIAL CORPORATION 4 354 2851 CFC INTERNATIONAL INC 4 307 2840 ECOLAB INC 20 355 2851 CORIMON CA -SPON ADR 5 308 2840 PROCTER & GAMBLE CO 20 356 2851 LILLY INDS INC -CL A 20 309 2840 STEPAN CO 20 357 2851 MOORE (BENJAMIN) & CO 20 310 2840 SYBRON CHEMICALS INC 8 358 2851 PPG INDUSTRIES INC 20 311 2840 USA DETERGENTS INC 5 359 2851 RENTECH INC 9 312 2842 CLOROX CO/DE 20 360 2851 RPM INC-OHIO 19 313 2842 CYCLOPSS CORP 4 361 2851 SHERWIN-WILLIAMS CO 20 314 2842 H E R C PRODUCTS INC 5 362 2851 THERMACELL TECHNOLOGIES INC 4 315 2842 KYZEN CORP 5 363 2851 VALSPAR CORP 20 316 2842 NCH CORP 19 364 2860 BORDEN INC 19 317 2842 OCEAN BIO-CHEM INC 18 365 2860 BUSH BOAKE ALLEN INC 6 318 2842 SPECIALTY CHEM RES 17 366 2860 CHEMFIRST INC 20 319 2842 WILSHIRE TECHNOLOGIES INC 8 367 2860 ETHYL CORP 20 320 2844 ADRIEN ARPEL INC 16 368 2860 FAIRMOUNT CHEMICAL CO INC 19 321 2844 ALBERTO-CULVER CO -CL B 20 369 2860 HIGH PLAINS CORP 18 322 2844 AVON PRODUCTS 20 370 2860 INTL FLAVORS & FRAGRANCES 20 323 2844 BEAUTICONTROL COSMETICS INC 13 371 2860 INTL SPECIALTY PRODS INC 9 324 2844 BLOCK DRUG -CL A 19 372 2860 JILIN CHEM INDL LTD -ADR 4 325 2844 BODY SHOP INTL PLC -ADR 4 373 2860 KMG CHEMICALS INC 3
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374 2860 LUBRIZOL CORP 20 422 3411 U S CAN CORP 6 375 2860 LYONDELL CHEMICAL CO 11 423 3420 ACME UNITED CORP 19 376 2860 METHANEX CORP 12 424 3420 ACORN PRODUCTS INC 3 377 2860 NOVA CHEMICALS CORP 19 425 3420 AMERICAN SAFETY RAZOR 6 378 2860 STERLING CHEMICALS HLDGS INC 12 426 3420 BEST LOCK CORP 18 379 2860 SYNTHETECH INC 14 427 3420 BLOUNT INC 19 380 2860 UNION CARBIDE CORP 20 428 3420 DANAHER CORP 20 381 2860 UNIVERSAL FOODS CORP 20 429 3420 EASTERN CO 20 382 2860 WITCO CORP 20 430 3420 GILLETTE CO 20 383 2870 AGRIUM INC 6 431 3420 INTL KNIFE & SAW INC 3 384 2870 ALCIDE CORP 15 432 3420 LIFETIME HOAN CORP 9 385 2870 AMERICAN VANGUARD CORP 19 433 3420 SAF T LOK INC 6 386 2870 ECOGEN INC 13 434 3420 SIMPSON MANUFACTURING INC 6 387 2870 ECOSCIENCE CORP/DE 8 435 3420 STANLEY WORKS 20 388 2870 IGENE BIOTECHNOLOGY INC 13 436 3420 STARRETT (L.S.) CO -CL A 20 389 2870 IMC GLOBAL INC 11 437 3420 TRANSTECHNOLOGY CORP 19 390 2870 LAROCHE INDUSTRIES INC 3 438 3430 FORTUNE BRANDS INC 20 391 2870 LESCO INC 16 439 3433 FAFCO INC 18 392 2870 MISSISSIPPI CHEMICAL CORP 20 440 3433 MARTIN INDUSTRIES INC/DE 4 393 2870 NORSK HYDRO AS -ADR 13 441 3433 TEMTEX INDUSTRIES INC 20 394 2870 PHOSPHATE RES PARTNERS -LP 12 442 3440 CHATWINS GROUP INC 5 395 2870 POTASH CORP SASK INC 11 443 3440 COVENTRY INDUSTRIES CORP 3 396 2870 SCOTTS COMPANY 10 444 3440 CPT HOLDING CORP 20 397 2870 TERRA NITROGEN CO -LP 8 445 3440 DAYTON SUPERIOR CORP -CL A 4 398 2870 VERDANT BRANDS INC 9 446 3440 JANNOCK LTD 12 399 2890 ALBEMARLE CORP 7 447 3440 LBP INC 4 400 2890 BIONUTRICS INC 3 448 3440 MAXCO INC 19 401 2890 BIOSEPRA INC 6 449 3440 ROHN INDUSTRIES INC 20 402 2890 CABOT CORP 20 450 3440 SCHUFF STEEL CO 4 403 2890 CYTEC INDUSTRIES INC 6 451 3440 VALMONT INDUSTRIES 20 404 2890 GRACE (W R) & CO 20 452 3442 DREW INDUSTRIES INC 14 405 2890 GREAT LAKES CHEMICAL CORP 20 453 3442 GRIFFON CORP 20 406 2890 HERCULES INC 20 454 3442 INTL ALUMINUM 20 407 2890 LEARONAL INC 19 455 3442 MILLENNIA INC 12 408 2890 MACDERMID INC 19 456 3443 AAVID THERMAL TECHNOLOGIES 5 409 2890 MINING SERVICES INTL CORP 15 457 3443 ALPHA TECHNOLOGIES GROUP INC 11 410 2890 MORTON INTERNATIONAL INC 11 458 3443 AMERICAN PRECISION INDS 20 411 2890 NALCO CHEMICAL CO 20 459 3443 BRYAN STEAM CORP 20 412 2890 PYROCAP INTERNATIONAL CORP 3 460 3443 CHART INDUSTRIES INC 8 413 2890 RONSON CORP 19 461 3443 DENALI INC 3 414 2890 SMITH INTERNATIONAL INC 20 462 3443 ITEQ INC 8 415 2890 TECHNICAL VENTURES INC 4 463 3443 JASON INC 12 416 2890 WD-40 CO 20 464 3443 MCCLAIN INDUSTRIES INC 20 417 3411 BALL CORP 20 465 3443 MCDERMOTT INTL INC 19 418 3411 BWAY CORP 5 466 3443 MOBILE MINI INC 6 419 3411 CROWN CORK & SEAL CO INC 20 467 3443 MUELLER (PAUL) CO 19 420 3411 PEERLESS TUBE CO 19 468 3443 PITT-DES MOINES INC 19 421 3411 SILGAN HOLDINGS INC 3 469 3443 POWERSOFT TECHNOLOGIES INC 17
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470 3443 ROBBINS & MYERS INC 20 518 3490 ACME METALS INC 14 471 3444 BERGER HOLDINGS LTD 16 519 3490 ADVANCED DEPOSITION TECH INC 6 472 3444 NORTEK INC 19 520 3490 AEROQUIP-VICKERS INC 20 473 3448 AMERICAN BUILDINGS COMPANY 5 521 3490 BARNES GROUP INC 20 474 3448 BUTLER MFG CO 20 522 3490 CENTRAL SPRINKLER CORP 14 475 3448 MARK SOLUTIONS INC 13 523 3490 EDISON CONTROL CORP 13 476 3448 MILLER BUILDING SYSTEMS INC 14 524 3490 FOILMARK INC 5 477 3448 NCI BUILDING SYSTEMS INC 8 525 3490 GENERAL KINETICS INC 19 478 3448 ROBERTSON CECO CORP 19 526 3490 GODDARD INDUSTRIES INC 20 479 3451 ATHANOR GROUP INC 13 527 3490 GS TECHNOLOGIES CORP 3 480 3452 ALLIED DEVICES CORP 5 528 3490 HARSCO CORP 20 481 3452 CHICAGO RIVET & MACHINE CO 19 529 3490 INTERMAGNETICS GENERAL CORP 19 482 3452 FAIRCHILD CORP -CL A 20 530 3490 PARKER-HANNIFIN CORP 20 483 3452 FEDERAL SCREW WORKS 20 531 3490 RAYTECH CORP/DE 20 484 3452 INDUSTRIAL HOLDINGS INC 8 532 3490 SHAW GROUP INC 6 485 3452 KAYNAR TECHNOLOGIES INC 3 533 3490 SUN HYDRAULICS CORP 3 486 3452 MICHIGAN RIVET CORP 20 534 3490 THERMODYNETICS INC 18 487 3452 PENN ENGR & MFG CORP -CL A 20 535 3490 UNITED CAPITAL CORP 17 488 3452 SPS TECHNOLOGIES INC 20 536 3490 WATTS INDUSTRIES -CL A 13 489 3460 ABC-NACO INC 6 537 3490 WHITTAKER CORP 20 490 3460 AETNA INDUSTRIES INC 3 538 3510 BRIGGS & STRATTON 20 491 3460 AMPCO-PITTSBURGH CORP 20 539 3510 BRUNSWICK CORP 20 492 3460 EKCO GROUP INC 19 540 3510 CHINA YUCHAI INTERNATIONAL 5 493 3460 FANSTEEL INC/DE 20 541 3510 CUMMINS ENGINE 20 494 3460 HIGHWAY HOLDINGS LTD 3 542 3510 DETROIT DIESEL CORP 7 495 3460 JPE INC 5 543 3510 KUHLMAN CORP 19 496 3460 PARK OHIO HOLDINGS CORP 20 544 3510 MCDERMOTT INC 11 497 3460 ROBROY INDUSTRIES INC -CL A 3 545 3523 AG CHEM EQUIPMENT INC 5 498 3460 SEMX CORP 8 546 3523 AG-BAG INTL LTD 9 499 3460 SHILOH INDUSTRIES INC 7 547 3523 AGCO CORP 8 500 3460 TMCI ELECTRONICS INC 3 548 3523 ALAMO GROUP INC 6 501 3460 TOWER AUTOMOTIVE INC 6 549 3523 ALLIED PRODUCTS 19 502 3460 WYMAN-GORDON CO 19 550 3523 ARTS WAY MFG INC 20 503 3460 ZERO CORP/DE 19 551 3523 CASE CORP 6 504 3470 BMC INDUSTRIES INC/MN 20 552 3523 CTB INTERNATIONAL CORP 3 505 3470 GENERAL MAGNAPLATE CORP 20 553 3523 DEERE & CO 20 506 3470 KINARK CORP 20 554 3523 DEERE & CO-PRE FASB 20 507 3470 MARGATE INDUSTRIES INC 13 555 3523 KUBOTA CORP -ADR 19 508 3470 MATERIAL SCIENCES CORP 15 556 3523 LINDSAY MANUFACTURING CO 11 509 3470 METAL ARTS CO INC 19 557 3523 TOP AIR MANUFACTURING INC 16 510 3470 ORYX TECHNOLOGY CORP 5 558 3523 TORO CO 20 511 3470 REXAM PLC -ADR 17 559 3524 ARMATRON INTERNATIONAL INC 20 512 3480 ALLIANT TECHSYSTEMS INC 9 560 3524 METROMEDIA INTERNATIONAL GRP 19 513 3480 ALLIED RESEARCH CORP 15 561 3530 COLUMBUS MCKINNON CORP 4 514 3480 BLOUNT INTL INC -CL A 20 562 3530 DOVER CORP 20 515 3480 ESTERLINE TECHNOLOGIES 20 563 3530 RAUMA OY -ADR 5 516 3480 PRIMEX TECHNOLOGIES INC 4 564 3530 SI HANDLING SYSTEMS INC 19 517 3480 STURM RUGER & CO INC 20 565 3531 A S V INC 6
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566 3531 ASTEC INDUSTRIES INC 14 614 3550 TAPISTRON INTERNATIONAL INC 7 567 3531 CATERPILLAR INC 20 615 3550 THERMO FIBERTEK INC 7 568 3531 CMI CORP 20 616 3550 THERMWOOD CORP 15 569 3531 GEHL CO 11 617 3550 VALMET CORP -SP ADR 3 570 3531 GRADALL INDUSTRIES INC 4 618 3555 AUTOLOGIC INFORMATION INTL 3 571 3531 JLG INDUSTRIES INC 20 619 3555 BALDWIN TECHNOLOGY -CL A 13 572 3531 KOMATSU LTD -ADR 19 620 3555 CHECK TECHNOLOGY CORP 14 573 3531 MANITOWOC CO 20 621 3555 DIVERSINET CORP 3 574 3532 HARNISCHFEGER INDUSTRIES INC 20 622 3555 INDIGO N V 6 575 3533 BAKER-HUGHES INC 20 623 3555 NUR MACROPRINTERS LTD 2 576 3533 BOLT TECHNOLOGY CORP 18 624 3555 PRESSTEK INC 7 577 3533 COOPER CAMERON CORP 5 625 3555 PUBLISHERS EQUIPMENT CORP 17 578 3533 DRECO ENERGY SERVICES LTD 13 626 3555 SCITEX CORP LTD -ORD 19 579 3533 ERC INDUSTRIES INC/DE 12 627 3555 STEVENS INTL INC -SER A COM 12 580 3533 FRIEDE GOLDMAN INTL INC 3 628 3555 WEB PRESS CORP 20 581 3533 GULF ISLAND FABRICATION INC 4 629 3559 3D SYS CORP/DE 11 582 3533 TESCO CORP 5 630 3559 ABC DISPENSING TECHNOLOGIES 15 583 3533 UNIFAB INTERNATIONAL INC 3 631 3559 AG ASSOCIATES INC 3 584 3533 VARCO INTERNATIONAL 20 632 3559 AMISTAR CORP 15 585 3533 WEATHERFORD INTL INC 18 633 3559 AMTECH SYSTEMS INC 17 586 3537 CASCADE CORP 19 634 3559 APPLIED MATERIALS INC 20 587 3537 NACCO INDUSTRIES -CL A 20 635 3559 APPLIED SCI & TECH 6 588 3537 OMNIQUIP INTERNATIONAL INC 3 636 3559 ASM INTERNATIONAL N V 17 589 3537 TEREX CORP 19 637 3559 ASM LITHOGRAPHY HOLDING NV 4 590 3537 TRANSACT INTL INC 19 638 3559 ASYST TECHNOLOGIES INC 6 591 3540 BLACK & DECKER CORP 20 639 3559 BE SEMICONDUCTOR INDUSTRIES 4 592 3540 BROWN & SHARPE MFG CO 19 640 3559 BROOKS AUTOMATION INC 5 593 3540 DEVLIEG-BULLARD INC 10 641 3559 BTU INTERNATIONAL INC 11 594 3540 DYNAMOTIVE TECHNOLOGIES CORP 3 642 3559 CFM TECHNOLOGIES INC 4 595 3540 ILLINOIS TOOL WORKS 20 643 3559 CPAC INC 19 596 3540 KENNAMETAL INC 20 644 3559 CRYO-CELL INTERNATIONAL INC 3 597 3540 LINCOLN ELECTRIC HLDGS INC 20 645 3559 CVD EQUIPMENT CORP 14 598 3540 MAKITA CORP -ADR 19 646 3559 CYMER INC 4 599 3540 MET-COIL SYSTEMS CORP 14 647 3559 DT INDUSTRIES INC 6 600 3540 MONARCH MACHINE TOOL CO 19 648 3559 DTM CORP 4 601 3540 P & F INDUSTRIES -CL A 19 649 3559 ELECTROGLAS INC 7 602 3540 PH GROUP INC 4 650 3559 EMCORE CORP 4 603 3540 PRODUCTIVITY TECHNOLOGIES CP 3 651 3559 ETEC SYSTEMS INC 4 604 3540 QUALITY PRODUCTS INC 9 652 3559 FARREL CORP 8 605 3540 RIVIERA TOOL CO 4 653 3559 FSI INTL INC 11 606 3540 WSI INDUSTRIES INC 20 654 3559 GASONICS INTERNATIONAL CORP 6 607 3541 BRIDGEPORT MACHINES INC 5 655 3559 GENERAL SCANNING INC 4 608 3541 GLEASON CORP 19 656 3559 GENUS INC 12 609 3541 HARDINGE BROTHERS INC 5 657 3559 GERBER SCIENTIFIC INC 19 610 3541 THERMADYNE HOLDINGS CORP 6 658 3559 HELISYS INC 4 611 3550 KEY TECHNOLOGY INC 7 659 3559 HI-RISE RECYCLING SYS INC 6 612 3550 PENTAIR INC 20 660 3559 ICOS VISION SYSTEMS CORP NV 3 613 3550 SHOPSMITH INC 19 661 3559 INTEGRATED PROCESS EQ 8
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662 3559 INTEVAC INC 5 710 3561 GRACO INC 20 663 3559 IONICS INC 20 711 3561 IDEX CORP 11 664 3559 KULICKE & SOFFA INDUSTRIES 20 712 3562 GENERAL BEARING CORP 4 665 3559 LAM RESEARCH CORP 16 713 3562 KAYDON CORP 16 666 3559 MATTSON TECHNOLOGY INC 6 714 3562 NETWORKS ELECTRONICS CORP 20 667 3559 MICRION CORP 6 715 3562 NN BALL & ROLLER INC 6 668 3559 MILACRON INC 20 716 3562 SKF AB -ADR 14 669 3559 MOTORVAC TECHNOLOGIES INC 3 717 3562 SUNBASE ASIA INC 4 670 3559 MRS TECHNOLOGY INC 6 718 3562 TIMKEN CO 20 671 3559 PHOTRONICS INC 13 719 3564 ALANCO ENVIRON RESOURCES CP 17 672 3559 PLASMA-THERM INC 18 720 3564 BHA GROUP HOLDINGS INC 14 673 3559 PRAB INC 19 721 3564 CECO ENVIRONMENTAL CORP 13 674 3559 PRI AUTOMATION INC 6 722 3564 CORE TECHNOLOGIES INC/PA 11 675 3559 QC OPTICS INC 3 723 3564 CROWN ANDERSEN INC 20 676 3559 QUAD SYSTEMS CORP 7 724 3564 DONALDSON CO INC 20 677 3559 QUIPP INC 13 725 3564 EARTH SCIENCES INC 19 678 3559 RIMAGE CORP 8 726 3564 ENGINEERED SUPPORT SYSTEMS 15 679 3559 SEMITOOL INC 5 727 3564 ENVIRONMENTAL ELEMENTS CORP 9 680 3559 SILICON VALLEY GROUP INC 17 728 3564 IONIC FUEL TECHNOLOGY INC 5 681 3559 SONO-TEK CORP 13 729 3564 MET-PRO CORP 19 682 3559 SPEEDFAM-IPEC INC 4 730 3564 NOXSO CORP 1 683 3559 SUBMICRON SYSTEMS CORP 6 731 3564 TRION INC 19 684 3559 TEGAL CORP 4 732 3567 BETHLEHEM CORP 19 685 3559 TOOLEX INTERNATIONAL NV 2 733 3567 CONSUMAT ENVIRONMENTAL 19 686 3559 TRIKON TECHNOLOGIES INC 4 734 3567 GENCOR INDUSTRIES INC 20 687 3559 VEECO INSTRUMENTS INC 17 735 3567 RADIANT TECHNOLOGY CORP 18 688 3559 WAVEMAT INC 11 736 3567 RESEARCH INC 20 689 3559 YIELDUP INTL CORP 5 737 3567 SELAS CORP OF AMERICA 20 690 3560 ABB-ASEA BROWN BOV GRP -ADR 3 738 3567 THERMATRIX INC 3 691 3560 BINKS SAMES CORP 20 739 3569 CUNO INC 4 692 3560 COHESANT TECHNOLOGIES INC 5 740 3569 ESCO ELECTRONICS CORP 10 693 3560 GARDNER DENVER INC 5 741 3569 FARR CO 19 694 3560 GRAHAM CORP 19 742 3569 FLOW INTL CORP 17 695 3560 HELIX TECHNOLOGY CORP 20 743 3569 HELPMATE ROBOTICS INC 4 696 3560 INGERSOLL-RAND CO 20 744 3569 INTERSYSTEMS INC/DE 14 697 3560 MEDICAL TECHNOLOGY SYS 12 745 3569 NORDSON CORP 20 698 3560 OMNI USA INC 10 746 3569 OSMONICS INC 19 699 3560 PAXAR CORP 20 747 3569 PALL CORP 20 700 3560 PFEIFFER VACUUM TECH -ADR 2 748 3569 PEERLESS MFG CO 20 701 3560 REGAL BELOIT 20 749 3569 PUROFLOW INC 19 702 3560 SI DIAMOND TECHNOLOGY INC 6 750 3569 SGI INTERNATIONAL 10 703 3560 TB WOODS 4 751 3569 TAYLOR DEVICES INC 17 704 3560 TECHNOLOGY GENERAL CP 10 752 3569 TENNEY ENGINEERING -CL B 19 705 3560 THOMAS INDUSTRIES INC 20 753 3569 TYCO INTERNATIONAL LTD 19 706 3560 TWIN DISC INC 20 754 3569 UNOVA INC 4 707 3560 WELDOTRON CORP 19 755 3569 WASTE TECHNOLOGY CORP 12 708 3560 ZEBRA TECHNOLOGIES CP -CL A 9 756 3570 DATA GENERAL CORP 20 709 3561 GORMAN-RUPP CO 20 757 3570 HEWLETT-PACKARD CO 20
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758 3570 HITACHI LTD -ADR 19 806 3572 DREXLER TECHNOLOGY CORP 19 759 3570 INTL BUSINESS MACHINES CORP 20 807 3572 ECCS INC 6 760 3570 NBS TECHNOLOGIES INC 15 808 3572 EMC CORP/MA 14 761 3570 TOSHIBA CORP 19 809 3572 EXABYTE CORP 10 762 3571 ALPHA MICROSYSTEMS 18 810 3572 HMT TECHNOLOGY CORP 4 763 3571 APPLE COMPUTER INC 19 811 3572 IOMEGA CORP 17 764 3571 BITWISE DESIGNS INC 8 812 3572 JTS CORP 13 765 3571 COMPAQ COMPUTER CORP 16 813 3572 MAXTOR CORP 14 766 3571 CONCURRENT COMPUTER CP 15 814 3572 MTI TECHNOLOGY CORP 6 767 3571 CYCOMM INTERNATIONAL INC 7 815 3572 NETWORK APPLIANCE INC 4 768 3571 DATAPOINT CORP 20 816 3572 NSTOR TECHNOLOGIES INC 7 769 3571 DAUPHIN TECHNOLOGY INC 3 817 3572 OVERLAND DATA INC 3 770 3571 DELL COMPUTER CORP 12 818 3572 PINNACLE MICRO INC 6 771 3571 DIGITAL LIGHTWAVE INC 3 819 3572 PROCOM TECHNOLOGY INC 2 772 3571 DUNN COMPUTER CORP/VA 3 820 3572 QUANTUM CORP 17 773 3571 DYNATEM INC 14 821 3572 SANDISK CORP 5 774 3571 ENCORE COMPUTER CORP 14 822 3572 SEAGATE TECHNOLOGY 18 775 3571 EQUITRAC CP 7 823 3572 STORAGE COMPUTER CORP 4 776 3571 FIELDWORKS INC 4 824 3572 STORAGE TECHNOLOGY CP 20 777 3571 FUJITSU LTD -SPON ADR 6 825 3572 SYQUEST TECHNOLOGY INC 7 778 3571 GATEWAY 2000 INC 7 826 3572 WESTERN DIGITAL CORP 20 779 3571 GLOBAL MAINTECH CORP 7 827 3575 BOUNDLESS CORP 9 780 3571 IAT MULTIMEDIA INC 3 828 3575 DOTRONIX INC 17 781 3571 MAXWELL TECHNOLOGIES INC 18 829 3575 IIS INTELLIGENT INFO -ORD 15 782 3571 MICRON ELECTRONICS INC 10 830 3575 IMAGE SYSTEMS CORP 3 783 3571 MIKROS SYSTEMS CORP 19 831 3575 LTI TECHNOLOGIES INC 14 784 3571 MMC NETWORKS INC 4 832 3575 MEGATECH CORP 20 785 3571 NATIONAL DATACOMPUTER INC 9 833 3575 NETWORK COMPUTING DEVICES 8 786 3571 NEMATRON CORP 4 834 3575 PIXTECH INC 4 787 3571 NEOWARE SYSTEMS INC 4 835 3575 TELEVIDEO INC 17 788 3571 NEXAR TECHNOLOGIES INC 3 836 3575 TRIDEX CORP 19 789 3571 PARAVANT INC 4 837 3575 WELLS-GARDNER ELECTRONICS 20 790 3571 SEQUENT COMPUTER SYSTEMS INC 13 838 3576 3COM CORP 16 791 3571 SILICON GRAPHICS INC 13 839 3576 ACT NETWORKS INC 5 792 3571 SULCUS HOSPITALITY TECH CP 15 840 3576 ADAPTEC INC 13 793 3571 SUN MICROSYSTEMS INC 14 841 3576 ADAPTIVE SOLUTIONS INC 6 794 3571 TELEPAD CORP -CL A 6 842 3576 ADVANCED ELECTR SUPPORT PDS 3 795 3571 VITECH AMERICA INC 3 843 3576 ANCOR COMMUNICATIONS INC 6 796 3571 WPI GROUP INC 8 844 3576 APEX PC SOLUTIONS INC 3 797 3571 XATA CORP 6 845 3576 ASANTE TECHNOLOGIES INC 6 798 3571 XYBERNAUT CORP 3 846 3576 ASCEND COMMUNICATIONS INC 6 799 3572 ADVANCED DIGITAL INFO CORP 4 847 3576 ASTROCOM CORP 19 800 3572 ANDATCO INC -CL A 18 848 3576 AUSPEX SYSTEMS INC 7 801 3572 ARTECON INC 3 849 3576 BOS BETTER ONLINE SOLUTIONS 3 802 3572 BOXHILL SYSTEMS CORP 4 850 3576 CABLETRON SYSTEMS 11 803 3572 CAMBEX CORP 19 851 3576 CASTELLE 4 804 3572 DATARAM CORP 19 852 3576 CHATCOM INC 6 805 3572 DISC INC 7 853 3576 CIPRICO INC 17
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854 3576 CIRRUS LOGIC INC 9 902 3576 TELEBYTE TECHNOLOGY INC 15 855 3576 CISCO SYSTEMS INC 10 903 3576 UNIVIEW TECHNOLOGIES CORP 6 856 3576 COMPUTER NETWORK TECH CORP 13 904 3576 VERILINK CORP 4 857 3576 COMPUTONE CORP 11 905 3576 VIDEOSERVER INC 5 858 3576 CYBEX COMPUTER PRODS 4 906 3576 XIRCOM INC 8 859 3576 DEVELCON ELECTRONICS LTD 16 907 3576 XYLAN CORP 4 860 3576 DIALOGIC CORP 6 908 3577 ACRES GAMING INC 6 861 3576 DIGI INTERNATIONAL INC 10 909 3577 ASTRO-MED INC 18 862 3576 DIGITAL LINK CORP 6 910 3577 BANCTEC INC 20 863 3576 ECHELON CORP 3 911 3577 BELL & HOWELL COMPANY 5 864 3576 ELECTRONICS FOR IMAGING INC 8 912 3577 BULL RUN CORP 10 865 3576 EMULEX CORP 19 913 3577 CALCOMP TECHNOLOGY INC 12 866 3576 EQUINOX SYSTEMS INC 6 914 3577 CANON INC -ADR 19 867 3576 FASTCOMM COMMUNICATIONS CORP 12 915 3577 CASINO DATA SYSTEMS 7 868 3576 FOCUS ENHANCEMENTS INC 6 916 3577 CENTENNIAL TECHNOLOGIES INC 5 869 3576 FORE SYSTEMS INC 5 917 3577 COMMUN INTELLIGENCE 8 870 3576 FRANKLIN TELECOMMUNICATIONS 9 918 3577 CYLINK CORP 3 871 3576 FVC.COM INC 3 919 3577 DATA TRANSLATION INC 5 872 3576 IMAGING TECHNOLOGIES CORP 16 920 3577 DATAMETRICS CORP 20 873 3576 INTERLINK CMPTR SCIENCES INC 3 921 3577 DIAMOND MULTIMEDIA SYS INC 5 874 3576 INTERPHASE CORP 15 922 3577 DIGITAL BIOMETRICS INC 9 875 3576 KOFAX IMAGE PRODUCTS INC 3 923 3577 DIGITAL ORIGIN INC 10 876 3576 LANOPTICS LTD 8 924 3577 ENCAD INC 7 877 3576 LARSCOM INC -CL A 4 925 3577 FRANKLIN ELECTRONIC PUBLISH 13 878 3576 MADGE NETWORKS NV 6 926 3577 GENICOM CORP 13 879 3576 MEGADATA CORP 20 927 3577 HAUPPAUGE DIGITAL INC 5 880 3576 MRV COMMUNICATIONS INC 8 928 3577 HOWTEK INC 12 881 3576 MYLEX CORP 15 929 3577 INPUT SOFTWARE INC 6 882 3576 NETOPIA INC 4 930 3577 INTERLINK ELECTRONICS 6 883 3576 NETRIX CORP 7 931 3577 ION NETWORKS INC 15 884 3576 NETWORK CONNECTION INC 4 932 3577 ISOMET CORP 20 885 3576 NETWORK EQUIPMENT TECH INC 13 933 3577 KENTEK INFORMATION SYS INC 4 886 3576 NETWORK PERIPHERALS INC 6 934 3577 KEY TRONIC CORP 17 887 3576 ODS NETWORKS INC 8 935 3577 LABTEC INC 4 888 3576 OLICOM A/S 7 936 3577 LEXMARK INTL GRP INC -CL A 5 889 3576 OPENROUTE NETWORKS INC 8 937 3577 LOGITECH INTL S A -ADR 4 890 3576 PERFORMANCE TECHNOLOGIES INC 4 938 3577 MEDIA 100 INC 15 891 3576 PHOTONICS CORP 6 939 3577 MEDPLUS INC/OH 5 892 3576 PLAINTREE SYSTEMS INC 5 940 3577 METROLOGIC INSTRUMENTS INC 5 893 3576 PROXIM INC 7 941 3577 MICROFIELD GRAPHICS INC 5 894 3576 RADCOM LTD 3 942 3577 MICROTOUCH SYSTEMS INC 8 895 3576 RIT TECHNOLOGIES LTD 4 943 3577 MILTOPE GROUP INC 14 896 3576 S3 INCORPORATED 7 944 3577 MITEK SYSTEMS INC 12 897 3576 SBE INC 20 945 3577 NEWCOM INC 3 898 3576 SEEQ TECHNOLOGY INC 17 946 3577 NUMBER NINE VISUAL TECH CORP 4 899 3576 SHIVA CORP 5 947 3577 PERCON INC 5 900 3576 SPLASH TECHNOLOGY HLDGS INC 4 948 3577 PHOTOMATRIX INC 11 901 3576 SYNC RESEARCH INC 5 949 3577 PSC INC 18
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950 3577 QMS INC 17 998 3579 GRADCO SYSTEMS INC 16 951 3577 RADISYS CORP 5 999 3579 GUNTHER INTERNATIONAL LTD 6 952 3577 RAINBOW TECHNOLOGIES INC 12 1000 3579 KRONOS INC 8 953 3577 RASTER GRAPHICS INC 3 1001 3579 PITNEY BOWES INC 20 954 3577 SC&T INTERNATIONAL INC 3 1002 3579 SANYO ELECTRIC CO LTD -ADR 8 955 3577 SCAN OPTICS INC 19 1003 3579 SMITH CORONA CORP 11 956 3577 SCANSOFT INC 5 1004 3579 ULTRADATA SYSTEMS INC 5 957 3577 SCM MICROSYSTEMS INC 4 1005 3580 AQUA CARE SYSTEMS INC 6 958 3577 SECURE COMPUTING CORP 4 1006 3580 ATRIX INTERNATIONAL INC 8 959 3577 SECURITY DYNAMICS TECH INC 5 1007 3580 BION ENVIRONMENTAL TECH INC 4 960 3577 SEDONA CORP 12 1008 3580 ESSEF CORP 13 961 3577 SOFTNET SYSTEMS INC 20 1009 3580 EV ENVIRONMENTAL INC 5 962 3577 STB SYSTEMS INC 5 1010 3580 INNOVATIVE MEDICAL SERVICES 4 963 3577 STORM TECHNOLOGY INC 3 1011 3580 INTERLOTT TECHNOLOGIES INC 5 964 3577 STRATASYS INC 6 1012 3580 MIDDLEBY CORP 13 965 3577 SYMBOL TECHNOLOGIES 20 1013 3580 MINUTEMAN INTERNATIONAL INC 12 966 3577 TELEPANEL SYSTEMS INC 11 1014 3580 ON POINT TECH SYSTEMS INC 5 967 3577 THRUSTMASTER INC 4 1015 3580 PREMARK INTERNATIONAL INC 14 968 3577 TOP IMAGE SYSTEMS LTD 2 1016 3580 RECOVERY ENGINEERING INC 6 969 3577 TRANSACT TECHNOLOGIES INC 3 1017 3580 SWWT INC 4 970 3577 TRINITECH SYSTEMS INC 6 1018 3580 TENNANT CO 19 971 3577 TRUEVISION INC 9 1019 3580 TOKHEIM CORP 19 972 3577 VOICE CONTROL SYSTEMS INC 19 1020 3580 TURBOCHEF TECHNOLOGIES INC 5 973 3577 WAVE SYSTEMS CORP -CL A 2 1021 3580 U S FILTER CORP 19 974 3577 XEIKON N V -ADR 4 1022 3580 WATER CHEF INC 8 975 3577 XEROX CORP 20 1023 3580 WTC INDUSTRIES INC 8 976 3578 AFFINITY TECHNOLOGY GRP INC 4 1024 3585 AAON INC 9 977 3578 ATS MONEY SYSTEMS INC 10 1025 3585 AMERICAN STANDARD INC 3 978 3578 AUTOTOTE CORP 16 1026 3585 AMERN STANDARD CO INC 20 979 3578 COMTREX SYSTEMS CORP 14 1027 3585 CONTINENTAL MATERIALS CORP 19 980 3578 DIEBOLD INC 20 1028 3585 FEDDERS CORP 20 981 3578 ELECTRONIC RETAILNG SYS INTL 6 1029 3585 HUSSMANN INTERNATIONAL INC 4 982 3578 GLOBAL PAYMENT TECHNOLOGIES 5 1030 3585 INDUSTRIAL DATA SYS CORP 3 983 3578 HYPERCOM CORP 3 1031 3585 INTL COMFORT PRODUCTS CORP 20 984 3578 INTL AUTOMATED SYSTEMS INC 3 1032 3585 INTL COMFORT PRODUCTS CP USA 4 985 3578 INTL LOTTERY & TOTALIZATOR 18 1033 3585 LANCER CORP/TX 14 986 3578 JAVELIN SYSTEMS INC 3 1034 3585 MESTEK INC 12 987 3578 MICROS SYSTEMS INC 19 1035 3585 RARE MEDIUM GROUP INC 11 988 3578 MOBINETIX SYSTEMS INC 6 1036 3585 RTI INC 19 989 3578 NAM TAI ELECTRONICS 12 1037 3585 SCOTSMAN INDUSTRIES INC 10 990 3578 NCR CORP 17 1038 3585 SPECIALTY EQUIPMENT COS INC 9 991 3578 OPTIMAL ROBOTICS CORP 5 1039 3585 TECUMSEH PRODUCTS CO -CL A 20 992 3578 PAR TECHNOLOGY CORP 17 1040 3585 UNITED DOMINION INDUSTRIES 20 993 3578 TELXON CORP 16 1041 3585 YORK INTL 14 994 3578 TIDEL TECHNOLOGIES INC 10 1042 3590 BONSO ELECTRONIC INTL INC 11 995 3578 US WIRELESS DATA INC -CL A 6 1043 3590 COMMERCIAL INTERTECH 20 996 3579 DAWN TECHNOLOGIES INC 7 1044 3590 DENISON INTL PLC -SP ADR 3 997 3579 GENERAL BINDING CORP 19 1045 3590 HASKEL INTL INC -CL A 5
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1046 3590 HASTINGS MFG CO 19 1094 3621 UNIQUE MOBILITY INC 16 1047 3590 MEASUREMENT SPECIALTIES INC 13 1095 3621 WESTERBEKE CORP 14 1048 3590 METTLER-TOLEDO INTL INC 2 1096 3630 AB ELECTROLUX -ADR 14 1049 3590 MICRO GENERAL CORP 19 1097 3630 FANTOM TECHNOLOGIES INC 4 1050 3590 MOOG INC -CL A 20 1098 3630 HMI INDUSTRIES INC 20 1051 3590 OILGEAR CO 19 1099 3630 MAYTAG CORP 20 1052 3590 SAUER INC 3 1100 3630 ROYAL APPLIANCE MFG CO 9 1053 3590 SI TECHNOLOGIES INC 17 1101 3630 SEMI-TECH CORP -CL A 11 1054 3590 THERMEDICS INC 17 1102 3630 SINGER CO N V 8 1055 3590 THERMO SENTRON INC 3 1103 3630 U S INDUSTRIES INC 5 1056 3590 VERTEX INDUSTRIES INC 14 1104 3630 WHIRLPOOL CORP 20 1057 3612 MAGNETEK INC 14 1105 3634 GLOBAL-TECH APPLIANCES INC 3 1058 3612 WATERS INSTRUMENT INC 19 1106 3634 HELEN OF TROY CORP LTD 19 1059 3613 CONTROL DEVICES INC 4 1107 3634 NATIONAL PRESTO INDS INC 20 1060 3613 LITTELFUSE INC 7 1108 3634 REMINGTON PRODUCTS CO LLC 4 1061 3613 POWELL INDUSTRIES INC 19 1109 3634 SALTON INC 8 1062 3613 TECHNOLOGY RESEARCH CORP 15 1110 3634 SUNBEAM CORPORATION 19 1063 3613 TII INDUSTRIES INC 20 1111 3634 WINDMERE-DURABLE HOLDINGS 19 1064 3613 WESTWOOD CORP 8 1112 3640 ADVANCED LIGHTING TECH INC 3 1065 3620 ACME ELECTRIC CORP 20 1113 3640 ANDERSEN GROUP INC 19 1066 3620 AEROVOX INC 9 1114 3640 ASTRO COMMUNICATIONS INC 18 1067 3620 AMERICAN PWR CNVRSION 12 1115 3640 AZTEC MANUFACTURING CO 19 1068 3620 BALLARD POWER SYSTEMS INC 6 1116 3640 CATALINA LIGHTING INC 12 1069 3620 CARBIDE/GRAPHITE GROUP INC 6 1117 3640 CHASE CORP 20 1070 3620 CLARY CORP 19 1118 3640 CHERRY CORP -CL A 19 1071 3620 COMMUNICATIONS INSTRUMENTS 3 1119 3640 COLEMAN CO INC 7 1072 3620 COMPUTER POWER INC 13 1120 3640 COMMUNICATIONS SYSTEMS INC 18 1073 3620 CONTROL CHIEF HOLDINGS INC 12 1121 3640 COOPER INDUSTRIES INC 20 1074 3620 EATON CORP 20 1122 3640 CSL LIGHTING MFG INC 5 1075 3620 EFI ELECTRONICS CORP 11 1123 3640 FIBERSTARS INC 6 1076 3620 GOLDEN GENESIS CO 11 1124 3640 GENLYTE GROUP INC 11 1077 3620 GOLDEN SYSTEMS INC 5 1125 3640 HOLOPHANE CORP 7 1078 3620 LATSHAW ENTERPRISES INC 20 1126 3640 HUBBELL INC -CL B 20 1079 3620 NDC AUTOMATION INC 10 1127 3640 IL INTERNATIONAL INC 11 1080 3620 ROCKWELL INTL CORP 20 1128 3640 JUNO LIGHTING INC 17 1081 3620 SPX CORP 20 1129 3640 LAMSON & SESSIONS CO 20 1082 3620 TECH OPS SEVCON INC 12 1130 3640 LSI INDS INC 15 1083 3620 UCAR INTERNATIONAL INC 5 1131 3640 M G PRODUCTS INC 8 1084 3620 WOODWARD GOVERNOR CO 20 1132 3640 NATIONAL SERVICE INDS INC 20 1085 3621 AMETEK INC 20 1133 3640 PREFORMED LINE PRODUCTS CO 3 1086 3621 BALDOR ELECTRIC 20 1134 3640 QUADRAX CORP 12 1087 3621 FRANKLIN ELECTRIC CO 20 1135 3640 RAYCHEM CORP 20 1088 3621 HATHAWAY CORP 20 1136 3640 SCIENTIFIC NRG INC 12 1089 3621 KOLLMORGEN CORP 20 1137 3640 SL INDS INC 20 1090 3621 NQL DRILLING TOOLS -CL A 10 1138 3640 SLI INC 5 1091 3621 OWOSSO CORP 6 1139 3640 TECHNITROL INC 20 1092 3621 SERVOTRONICS INC 19 1140 3640 TIVOLI INDUSTRIES INC 6 1093 3621 SMITH (A O) CORP 20 1141 3640 WOODHEAD INDUSTRIES INC 20
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1142 3651 ADVANCE DISPLAY TECH INC 9 1190 3661 COGNITRONICS CORP 19 1143 3651 AMERICAN TECHNOLOGY CORP 3 1191 3661 COM21 INC 3 1144 3651 BOSTON ACOUSTICS INC 13 1192 3661 COMDIAL CORP 20 1145 3651 CARVER CORP/WA 14 1193 3661 COMVERSE TECHNOLOGY INC 13 1146 3651 DIGITAL VIDEO SYSTEMS INC 3 1194 3661 COYOTE NETWORK SYSTEMS INC 19 1147 3651 E DIGITAL CORP 6 1195 3661 C-PHONE CORP 5 1148 3651 EMERSON RADIO 19 1196 3661 CYBER DIGITAL INC 12 1149 3651 EUPHONIX INC 5 1197 3661 DATA RACE INC 7 1150 3651 GREAT WALL ELECTR INTL -ADR 5 1198 3661 DAVOX CORP 12 1151 3651 HARMAN INTERNATIONAL INDS 13 1199 3661 DIGITAL TRANSMISSION SYSTEMS 4 1152 3651 KOSS CORP 20 1200 3661 DYNATEC INTERNATIONAL 13 1153 3651 LAFAYETTE INDUSTRIES INC 4 1201 3661 ECI TELECOMMUNICATIONS -ORD 18 1154 3651 PARKERVISION INC 7 1202 3661 EIS INTERNATIONAL INC 8 1155 3651 PHOENIX GOLD INTL INC 5 1203 3661 ELBIT COMPUTERS LTD 16 1156 3651 PIONEER ELECTRON -SPON ADR 19 1204 3661 ELCOTEL INC 13 1157 3651 POLK AUDIO INC 13 1205 3661 ELECTR TELE-COMMUNCTN 15 1158 3651 RECOTON CORP 19 1206 3661 EXCEL SWITCHING CORP 3 1159 3651 SENSORY SCIENCE CORP 11 1207 3661 GENERAL DATACOMM INDS 20 1160 3651 SINGING MACHINE CO INC 5 1208 3661 INTELECT COMMNCTN INC 16 1161 3651 SONY CORP -AMER SHARES 19 1209 3661 INTELIDATA TECHNOLOGIES CORP 4 1162 3651 UNIVERSAL ELECTRONICS INC 7 1210 3661 INTERACTIVE INC 7 1163 3651 VIDIKRON TECHNOLOGIES GROUP 6 1211 3661 INTER-TEL INC -SER A 19 1164 3651 VOICE IT WORLDWIDE INC 19 1212 3661 INTERVOICE INC 14 1165 3651 ZENITH ELECTRONICS CORP 20 1213 3661 IPC INFORMATION SYS INC 6 1166 3652 CINRAM INTERNATIONAL INC 10 1214 3661 KOOR INDUSTRIES LTD -ADR 9 1167 3652 INTEGRITY INC -CL A 6 1215 3661 LUCENT TECHNOLOGIES INC 4 1168 3652 K-TEL INTERNATIONAL 20 1216 3661 MER TELEMGMT SOLUTIONS LTD 3 1169 3652 METATEC CORP 8 1217 3661 METRO TEL CORP 19 1170 3652 PLATINUM ENTERTAINMENT INC 3 1218 3661 MICROLOG CORP 13 1171 3652 QUALITY DINO ENTERTNMT LTD 8 1219 3661 MICROTEL INTERNATIONAL INC 12 1172 3652 ZOMAX OPTICAL MEDIA INC 4 1220 3661 MITEL CORP 19 1173 3661 ACTIVE VOICE CORP 6 1221 3661 MOSAIX INC 10 1174 3661 ADC TELECOMMUNICATIONS INC 20 1222 3661 NATURAL MICROSYSTEMS CORP 6 1175 3661 ADTRAN INC 5 1223 3661 NEC CORP -ADR 19 1176 3661 ADVANCED FIBRE COMM INC 3 1224 3661 NEWBRIDGE NETWORKS CORP 10 1177 3661 ALCATEL -ADR 8 1225 3661 NICE SYSTEMS LTD -SPON ADR 4 1178 3661 APPLIED INNOVATION INC 7 1226 3661 NORTHERN TELECOM LTD 19 1179 3661 ASPECT TELECOMMUNICATIONS 10 1227 3661 ONEWORLD SYSTEMS INC 6 1180 3661 BOCA RESEARCH INC 6 1228 3661 ORCKIT COMMUNICATIONS LTD 4 1181 3661 BOGEN COMMUNICATIONS INTL 3 1229 3661 OSICOM TECHNOLOGIES INC 11 1182 3661 BRITE VOICE SYSTEMS INC 10 1230 3661 PAIRGAIN TECHNOLOGIES INC 6 1183 3661 BROADBAND TECHNOLOGIES INC 7 1231 3661 PERIPHONICS CORP 5 1184 3661 BROOKTROUT TECHNOLOGY INC 7 1232 3661 PICTURETEL CORP 12 1185 3661 CARRIER ACCESS CORP 3 1233 3661 PLANTRONICS INC 15 1186 3661 CENTIGRAM COMMUNICATIONS CP 9 1234 3661 POLYCOM INC 4 1187 3661 CIDCO INC 5 1235 3661 PORTA SYSTEMS CORP 19 1188 3661 CIENA CORP 3 1236 3661 PRECISION SYSTEMS INC 6 1189 3661 CMC INDUSTRIES INC 6 1237 3661 PREMISYS COMMUNICATIONS INC 5
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1238 3661 PULSEPOINT COMMUNICATIONS CP 9 1286 3663 DIGITAL MICROWAVE CORP 12 1239 3661 RADYNE COMSTREAM INC 13 1287 3663 DIGITAL RECORDERS INC 5 1240 3661 RELTEC CORP 3 1288 3663 DSP COMMUNICATIONS INC 3 1241 3661 RSI SYSTEMS INC 4 1289 3663 DSP GROUP INC 6 1242 3661 SCIENCE DYNAMICS CORP 18 1290 3663 ECHOSTAR COMMUN CORP -CL A 4 1243 3661 SYMMETRICOM INC 20 1291 3663 EMCEE BROADCAST PRODUCTS INC 19 1244 3661 SYNTELLECT INC 10 1292 3663 ERICSSON (L M) TEL -ADR 19 1245 3661 SYSTEMS TECHNOLOGY ASSOC INC 13 1293 3663 FAROUDJA INC 4 1246 3661 TADIRAN LTD -SPON ADR 7 1294 3663 GENERAL INSTRUMENT CORP 20 1247 3661 TADIRAN TELECOMMUNICATNS LTD 4 1295 3663 GENERAL MOTORS CL H 14 1248 3661 TELIDENT INC 4 1296 3663 GENTNER COMMUNICATIONS 10 1249 3661 TELLABS INC 20 1297 3663 GILAT SATELLITE NETWORKS LTD 6 1250 3661 TELTREND INC 4 1298 3663 GLENAYRE TECHNOLOGIES INC 11 1251 3661 V BAND CORPORATION 15 1299 3663 GLOBECOMM SYSTEMS INC 3 1252 3661 VERAMARK TECHNOLOGIES INC 14 1300 3663 GRANDETEL TECHNOLOGIES INC 11 1253 3661 VODAVI TECHNOLOGY INC 4 1301 3663 HARMONIC INC 5 1254 3661 WESTELL TECH INC -CL A 4 1302 3663 HARRIS CORP 20 1255 3661 WIDECOM GROUP INC 4 1303 3663 INFORMATION RES ENGR INC 8 1256 3661 WORLD ACCESS INC 9 1304 3663 INTERACTIVE FLGHT TCH -CL A 3 1257 3661 XETA CORP 13 1305 3663 INTERDIGITAL COMMUN CORP 16 1258 3661 ZOOM TELEPHONICS INC 10 1306 3663 ITRON INC 7 1259 3663 ACRODYNE COMMUNICATIONS INC 4 1307 3663 LARCAN-TTC INC 16 1260 3663 ACTV INC 8 1308 3663 LIFELINE SYSTEMS INC 17 1261 3663 ALLEN TELECOM INC 20 1309 3663 LOGIMETRICS INC -CL A 19 1262 3663 AM COMMUNICATIONS INC 19 1310 3663 MACKIE DESIGNS INC 5 1263 3663 AML COMMUNICATIONS INC 4 1311 3663 MERRIMAC INDUSTRIES INC 17 1264 3663 AMPEX CORP/DE -CL A 7 1312 3663 METRICOM INC 8 1265 3663 AMPLIDYNE INC 3 1313 3663 MICROWAVE PWR DEVICES INC/DE 5 1266 3663 ANDREA ELECTRONICS CORP 19 1314 3663 MOTOROLA INC 20 1267 3663 ANTEC CORP 6 1315 3663 NERA AS -SP ADR 3 1268 3663 ANTENNA PRODUCTS INC 14 1316 3663 NOKIA CORP -ADR 5 1269 3663 APPLIED SIGNAL TECHNOLOGY 7 1317 3663 NOVATEL INC 4 1270 3663 AYDIN CORP 19 1318 3663 ODETICS INC -CL A 18 1271 3663 BLONDER TONGUE LABS INC 4 1319 3663 OPTELECOM INC 19 1272 3663 CABLE LINK INC 4 1320 3663 ORTEL CORP 5 1273 3663 CALIFORNIA AMPLIFIER INC 16 1321 3663 PACIFIC RESH & ENGR CORP 3 1274 3663 CALIFORNIA MICROWAVE 20 1322 3663 P-COM INC 5 1275 3663 C-COR ELECTRONICS INC 18 1323 3663 PICO PRODUCTS INC 18 1276 3663 C-CUBE MICROSYSTEMS INC 6 1324 3663 POWERWAVE TECHNOLOGIES INC 4 1277 3663 CELERITEK INC 4 1325 3663 QUALCOMM INC 8 1278 3663 CHYRON CORP 19 1326 3663 RACAL ELECTRS PLC -SPON ADR 6 1279 3663 CIRCUIT RESEARCH LABS INC 16 1327 3663 RELM WIRLESS CORP 18 1280 3663 COBRA ELECTRS CORP 19 1328 3663 RF MONOLITHICS INC 5 1281 3663 CODED COMMUNICATIONS CORP 6 1329 3663 SALIENT 3 COMMUN INC -CL A 20 1282 3663 COMTECH TELECOMMUN 20 1330 3663 SAWTEK INC 4 1283 3663 DATAMARINE INTL INC 20 1331 3663 SCIENTIFIC-ATLANTA INC 20 1284 3663 DATRON SYSTEMS INC/DE 15 1332 3663 SOCKET COMMUNICATIONS INC 5 1285 3663 DESTRON FEARING CORP 11 1333 3663 SPECTRALINK CORP 4
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1334 3663 SPECTRIAN CORP 5 1382 3670 AVX CORP 15 1335 3663 SSE TELECOM INC 12 1383 3670 CALIFORNIA MICRO DEVICES CP 12 1336 3663 STANFORD TELECOMMUNICATIONS 16 1384 3670 EA INDUSTRIES INC 19 1337 3663 STM WIRELESS INC 7 1385 3670 KEMET CORP 7 1338 3663 SUNAIR ELECTRONICS INC 20 1386 3670 SCI SYSTEMS INC 20 1339 3663 TCI INTL INC 18 1387 3670 TDK CORP -ADS 18 1340 3663 TECHNICAL COMMUNICATIONS CP 20 1388 3670 VISHAY INTRTECHNOLOGY 20 1341 3663 TEKELEC 14 1389 3672 ACT MANUFACTURING INC 5 1342 3663 TELULAR CORP 6 1390 3672 ARIEL CORP 5 1343 3663 TRANSCRYPT INTERNATIONAL INC 4 1391 3672 BENCHMARK ELECTRONICS INC 10 1344 3663 VERTEX COMMUNICATIONS CORP 13 1392 3672 CIRCUIT SYSTEMS INC 11 1345 3663 VIASAT INC 3 1393 3672 DII GROUP INC 7 1346 3663 VIEWCAST.COM INC 4 1394 3672 EFTC CORP 5 1347 3663 VSI ENTERPRISES INC 8 1395 3672 ELAMEX S A DE CV 4 1348 3663 VTEL CORP 7 1396 3672 ELTEK LTD 3 1349 3663 WATKINS-JOHNSON 20 1397 3672 FLEXTRONICS INTERNATIONAL 6 1350 3663 WEGENER CORP 11 1398 3672 HADCO CORP 16 1351 3669 AMX CORP 4 1399 3672 IEC ELECTRONICS CORP 17 1352 3669 CHECKPOINT SYSTEMS INC 20 1400 3672 JABIL CIRCUIT INC 7 1353 3669 CODE-ALARM INC 12 1401 3672 MERIX CORP 5 1354 3669 CORRECTIONS SVCS INC 11 1402 3672 PARK ELECTROCHEMICAL CORP 19 1355 3669 CUSTOMTRACKS CORP 10 1403 3672 PARLEX CORP 16 1356 3669 DETECTION SYSTEMS INC 19 1404 3672 PLEXUS CORP 13 1357 3669 ENSEC INTERNATIONAL INC 3 1405 3672 PRAEGITZER INDUSTRIES INC 4 1358 3669 FIRECOM INC 16 1406 3672 SANMINA CORP 7 1359 3669 FIRETECTOR INC 10 1407 3672 SHELDAHL INC 20 1360 3669 FRISCO BAY INDS LTD 6 1408 3672 SIGMA DESIGNS INC 13 1361 3669 INTEGRATED SECURITY SYS INC 7 1409 3672 SIGMATRON INTERNATIONAL INC 6 1362 3669 INTL ELECTRONICS INC 16 1410 3672 SMTEK INTERNATIONAL INC 20 1363 3669 ITI TECHNOLOGIES INC 5 1411 3672 SOLECTRON CORP 10 1364 3669 LO-JACK CORPORATION 12 1412 3672 TELS CORP 14 1365 3669 MAGAL SECURITY SYS LTD 6 1413 3672 US TECHNOLOGIES INC 11 1366 3669 MIRTRONICS INC 13 1414 3672 XETEL CORP 4 1367 3669 MOSLER INC 10 1415 3674 3D LABS INC LTD 4 1368 3669 NAPCO SECURITY SYSTEMS INC 17 1416 3674 3DFX INTERACTIVE INC 3 1369 3669 NUMEREX CORP -CL A 5 1417 3674 8X8 INC 3 1370 3669 OSI SYSTEMS INC 3 1418 3674 ACTEL CORP 6 1371 3669 PITTWAY CORP/DE -CL A 19 1419 3674 ADVANCED MICRO DEVICES 20 1372 3669 RISK(GEORGE) INDS INC 16 1420 3674 ADVANCED PHOTONIX INC -CL A 8 1373 3669 SENSORMATIC ELECTRONICS 19 1421 3674 AEROFLEX INC 20 1374 3669 SENTRY TECHNOLOGY CORP 5 1422 3674 ALLIANCE SEMICONDUCTOR CORP 6 1375 3669 STRATESEC INC 3 1423 3674 ALPHA INDUSTRIES INC 19 1376 3669 THERMO POWER CORP 13 1424 3674 ALTERA CORP 13 1377 3669 VERSUS TECHNOLOGY INC 11 1425 3674 AMERICAN XTAL TECHNOLOGY INC 3 1378 3669 VICON INDUSTRIES INC 20 1426 3674 ANADIGICS INC 5 1379 3669 VIKONICS INC 12 1427 3674 ANALOG DEVICES 20 1380 3670 AMERICAN TECH CERAMICS CORP 15 1428 3674 APPLIED MICRO CIRCUITS CORP 3 1381 3670 ASD GROUP INC 3 1429 3674 ARM HOLDINGS LTD 3
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1430 3674 ASPEC TECHNOLOGY INC 3 1478 3674 MICROPAC INDUSTRIES INC 20 1431 3674 ASTROPOWER INC 4 1479 3674 MICROSEMI CORP 20 1432 3674 ATMEL CORP 8 1480 3674 MITSUBISHI ELEC CORP -ADR 5 1433 3674 AUREAL SEMICONDUCTOR 7 1481 3674 NATIONAL SEMICONDUCTOR CORP 19 1434 3674 BLUE WAVE SYSTEMS INC 4 1482 3674 NEOMAGIC CORP 4 1435 3674 BURR-BROWN CORP 17 1483 3674 OAK TECHNOLOGY INC 5 1436 3674 CATALYST SEMICONDUCTOR INC 6 1484 3674 OPTEK TECHNOLOGY INC 12 1437 3674 CREE RESEARCH INC 7 1485 3674 OPTI INC 7 1438 3674 CTS CORP 20 1486 3674 PERICOM SEMICONDUCTOR CORP 3 1439 3674 CYPRESS SEMICONDUCTOR CORP 14 1487 3674 PMC-SIERRA INC 9 1440 3674 DALLAS SEMICONDUCTOR CORP 13 1488 3674 POWER INTEGRATIONS INC 4 1441 3674 DENSE-PAC MICROSYSTEMS INC 14 1489 3674 QLOGIC CORP 5 1442 3674 DIODES INC 20 1490 3674 QUALITY SEMICONDUCTOR INC 5 1443 3674 DIONICS INC 20 1491 3674 RAMTRON INTERNATIONAL CORP 10 1444 3674 ELANTEC SEMICONDUCTOR INC 4 1492 3674 REMEC INC 4 1445 3674 ESS TECHNOLOGY INC 5 1493 3674 RF MICRO DEVICES INC 3 1446 3674 EXAR CORP 14 1494 3674 SDL INC 5 1447 3674 FAIRCHILD SEMICONDUCTOR CORP 3 1495 3674 SEMICON INC 20 1448 3674 GALILEO TECHNOLOGY LTD 4 1496 3674 SEMTECH CORP 20 1449 3674 GATEFIELD CORP 15 1497 3674 SILICON STORAGE TECHNOLOGY 5 1450 3674 GENESIS MICROCHIP INC 3 1498 3674 SILICONIX INC 20 1451 3674 HARMON INDUSTRIES INC 20 1499 3674 SIMTEK CORP 9 1452 3674 HEI INC 19 1500 3674 SIPEX CORP 4 1453 3674 HYTEK MICROSYSTEMS INC 16 1501 3674 SMART MODULAR TECHNOLGS INC 5 1454 3674 IBIS TECHNOLOGY INC 5 1502 3674 SOLITRON DEVICES INC 19 1455 3674 IMP INC 12 1503 3674 SPECTRUM SIGNAL PROCESSING 9 1456 3674 INTEGRATED CIRCUIT SYSTEMS 9 1504 3674 STANDARD MICROSYSTEMS CORP 19 1457 3674 INTEGRATED DEVICE TECH INC 16 1505 3674 STMICROELECTRONICS N V 6 1458 3674 INTEGRATED SENSOR SOLUTIONS 3 1506 3674 SUPERTEX INC 16 1459 3674 INTEGRATED SILICON SOLUTION 5 1507 3674 TANISYS TECHNOLOGY INC 3 1460 3674 INTEL CORP 20 1508 3674 TELCOM SEMICONDUCTOR INC 5 1461 3674 INTL RECTIFIER CORP 20 1509 3674 TEXAS INSTRUMENTS INC 20 1462 3674 JETRONIC INDUSTRIES INC 19 1510 3674 THREE-FIVE SYSTEMS INC 10 1463 3674 KOPIN CORP 7 1511 3674 TOWER SEMICONDUCTOR LTD 6 1464 3674 KYOCERA CORP -ADR 19 1512 3674 TRANSWITCH CORP 5 1465 3674 LATTICE SEMICONDUCTOR CORP 10 1513 3674 TRIDENT MICROSYSTEMS INC 7 1466 3674 LEVEL ONE COMMUNICATIONS INC 6 1514 3674 TRIQUINT SEMICONDUCTOR INC 7 1467 3674 LINEAR TECHNOLOGY CORP 14 1515 3674 UNIPHASE CORP 6 1468 3674 LOGIC DEVICES INC 11 1516 3674 UNITRODE CORP 19 1469 3674 LSI LOGIC CORP 17 1517 3674 UNIVERSAL DISPLAY CORP 4 1470 3674 MACRONIX INTL LTD -ADR 3 1518 3674 VITESSE SEMICONDUCTOR CORP 8 1471 3674 MAXIM INTEGRATED PRODUCTS 12 1519 3674 VLSI TECHNOLOGY INC 17 1472 3674 MEMC ELECTRONIC MATRIALS INC 5 1520 3674 WHITE ELECTRIC DESIGNS CORP 20 1473 3674 MICREL INC 6 1521 3674 XICOR INC 18 1474 3674 MICRO LINEAR CORP 6 1522 3674 XILINX INC 9 1475 3674 MICROCHIP TECHNOLOGY INC 7 1523 3674 ZING TECHNOLOGIES INC 14 1476 3674 MICROELECTRONIC PACKAGING 5 1524 3674 ZORAN CORP 5 1477 3674 MICRON TECHNOLOGY INC 16 1525 3677 BEL FUSE INC 16
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1526 3677 TOROTEL INC 19 1574 3679 SPECTRUM CONTROL INC 20 1527 3678 ADFLEX SOLUTIONS INC 6 1575 3679 STONERIDGE INC 3 1528 3678 AMP INC 20 1576 3679 TECHDYNE INC 13 1529 3678 AMPHENOL CORP 10 1577 3679 VARI-L COMPANY INC 5 1530 3678 IEH CORP 19 1578 3679 VERITEC INC 8 1531 3678 INSILCO HOLDING CO 18 1579 3679 VICOR CORP 10 1532 3678 METHODE ELECTRONICS -CL A 19 1580 3679 VOICE POWERED TECH INTL INC 6 1533 3678 MOLEX INC 20 1581 3690 ARROW AUTOMOTIVE INDUSTRIES 19 1534 3678 OAK INDUSTRIES INC 20 1582 3690 AXCESS INC 16 1535 3678 PCD INC 4 1583 3690 BVR TECHNOLOGIES LTD 8 1536 3678 PEN INTERCONNECT INC 5 1584 3690 C&D TECHNOLOGIES INC 12 1537 3678 R F INDUSTRIES LTD 16 1585 3690 DATAKEY INC 15 1538 3678 ROBINSON NUGENT INC 20 1586 3690 DIGITRAN SYSTEMS INC 7 1539 3678 SMARTFLEX SYSTEMS INC 5 1587 3690 ECC INTERNATIONAL CP 20 1540 3678 THOMAS & BETTS CORP 20 1588 3690 ELECTRIC FUEL CORP 6 1541 3679 ADVANCED ENERGY INDS INC 4 1589 3690 ELECTRO SCIENTIFIC INDS INC 16 1542 3679 ANAREN MICROWAVE INC 20 1590 3690 ENCORE GROUP INC 14 1543 3679 APPLIED MAGNETICS CORP 20 1591 3690 ENERGY CONVERSION DEV 18 1544 3679 ARTESYN TECHNOLOGIES INC 20 1592 3690 ENVIRONMENTAL TECTONICS CORP 19 1545 3679 AULT INC 16 1593 3690 ESHED ROBOTEC (1982) LTD 8 1546 3679 AXIOHM TRANSACTION SOLUTIONS 15 1594 3690 EVANS & SUTHERLAND CMP CORP 19 1547 3679 C P CLARE CORP 4 1595 3690 EXCEL TECHNOLOGY INC 9 1548 3679 CONOLOG CORP 20 1596 3690 EXIDE CORP 12 1549 3679 DEL GLOBAL TECHNOLOGIES CORP 20 1597 3690 FIREARMS TRAINING SYS -CL A 3 1550 3679 DIGITAL POWER CORP 3 1598 3690 INFINITE GROUP INC 4 1551 3679 EMS TECHNOLOGIES INC 19 1599 3690 LASER CORP 13 1552 3679 ESPEY MFG & ELECTRONICS CORP 20 1600 3690 LASER POWER CORP 4 1553 3679 FERROFLUIDICS CORP 18 1601 3690 MOTORCAR PTS & ACCESSORS INC 6 1554 3679 FIFTH DIMENSION INC 19 1602 3690 N VISION INC 3 1555 3679 HUTCHINSON TECH 15 1603 3690 PERCEPTRONICS INC 16 1556 3679 INNOVEX INC 15 1604 3690 RAYOVAC CORP 4 1557 3679 INRAD INC 16 1605 3690 ROBOMATIX TECH LTD -ORD 4 1558 3679 JPM CO 4 1606 3690 ROFIN SINAR TECHNOLOGIES INC 4 1559 3679 MATEC CORP/MD 19 1607 3690 SONICS & MATERIALS INC 4 1560 3679 MEDICORE INC 19 1608 3690 STANDARD MOTOR PRODS 20 1561 3679 MICROENERGY INC 14 1609 3690 TNR TECHNICAL INC 15 1562 3679 MICRONETICS WIRELESS INC 12 1610 3690 ULTRALIFE BATTERIES INC 7 1563 3679 MICROWAVE FILTER CO INC 16 1611 3690 UNITED INDUSTRIAL CORP 19 1564 3679 M-WAVE INC 8 1612 3695 CERION TECHNOLOGIES INC 3 1565 3679 NATIONAL MICRONETICS INC 20 1613 3695 CERTRON CORP 20 1566 3679 NII NORSAT INTL INC 10 1614 3695 KOMAG INC 13 1567 3679 NORTECH SYSTEMS INC 14 1615 3695 STORMEDIA INC -CL A 4 1568 3679 OIS OPTICAL IMAGING SYSTEMS 13 1616 3812 AEROSONIC CORP 19 1569 3679 PLANAR SYSTEMS INC 6 1617 3812 AIRPORT SYSTEMS INTL INC 6 1570 3679 POWER DESIGNS INC 19 1618 3812 BENTHOS INC 3 1571 3679 POWER-ONE INC 4 1619 3812 CANADIAN MARCONI CO 19 1572 3679 READ-RITE CORP 9 1620 3812 CUBIC CORP 20 1573 3679 SPARTON CORP 20 1621 3812 DEWEY ELECTRONICS CORP 20
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1718 3825 ORBIT/FR INC 3 1766 3827 II-VI INC 12 1719 3825 PHOTON DYNAMICS INC 4 1767 3827 KLA-TENCOR CORP 19 1720 3825 QUICKTURN DESIGN SYSTEMS INC 7 1768 3827 LIGHTPATH TECH INC -CL A 4 1721 3825 RELIABILITY INC 20 1769 3827 MEADE INSTRUMENTS CORP 4 1722 3825 SIGNAL TECHNOLOGY CORP 6 1770 3827 OPTICAL COATING LAB INC 20 1723 3825 SNAP-ON INC 20 1771 3827 OPT-SCIENCES CORP 20 1724 3825 TEKTRONIX INC 19 1772 3827 PERCEPTRON INC 7 1725 3825 TERADYNE INC 20 1773 3827 PHOTON TECHNOLOGY INTL INC 13 1726 3825 THERMO VOLTEK CORP 19 1774 3827 SCIENTIFIC TECHNOLOGIES INC 16 1727 3825 TOLLGRADE COMMUNICATIONS INC 5 1775 3827 STIMSONITE CORP 7 1728 3825 TRANSMATION INC 19 1776 3827 STOCKER & YALE INCORP 4 1729 3825 WIRELESS TELECOM GROUP INC 8 1777 3827 THERMO VISION CORP 4 1730 3826 APPLIED IMAGING CORP 3 1778 3827 ZYGO CORP 16 1731 3826 BECKMAN COULTER INC 16 1779 3829 ALCOHOL SENSORS INTL LTD 3 1732 3826 BIACORE INTL AB -SP ADR 3 1780 3829 BARRINGER TECHNOLOGIES 19 1733 3826 BIO-RAD LABS -CL A 19 1781 3829 BIOSYNERGY INC 15 1734 3826 CELLEX BIOSCIENCES INC 13 1782 3829 DSP TECHNOLOGY INC 14 1735 3826 CEM CORP 13 1783 3829 FARO TECHNOLOGIES INC 3 1736 3826 CHROMATICS COLOR SCIENCES 6 1784 3829 FIBERCHEM INC 11 1737 3826 COHERENT INC 20 1785 3829 GEOSCIENCE CORP 3 1738 3826 CYTYC CORP 4 1786 3829 INPUT/OUTPUT INC 9 1739 3826 DIASYS CORP 4 1787 3829 INSTRON CORP 19 1740 3826 FEI CO 5 1788 3829 JMAR TECHNOLOGIES INC 9 1741 3826 HABER INC 11 1789 3829 LARSON DAVIS INC 10 1742 3826 HACH CO 19 1790 3829 MECHANICAL TECHNOLOGY INC 20 1743 3826 HELIONETICS 16 1791 3829 MEDIA LOGIC INC 11 1744 3826 INTELLIGENT MED IMAGING INC 3 1792 3829 MFRI INC 9 1745 3826 INTL REMOTE IMAGING SYSTEMS 15 1793 3829 MODERN CONTROLS INC 19 1746 3826 LIFSCHULTZ INDS INC 11 1794 3829 MTS SYSTEMS CORP 20 1747 3826 MILLIPORE CORP 20 1795 3829 NANOMETRICS INC 15 1748 3826 MISONIX INC 8 1796 3829 OYO GEOSPACE CORP 3 1749 3826 MOLECULAR DEVICES CORP 5 1797 3829 QUALMARK CORP 3 1750 3826 NORLAND MEDICAL SYSTEMS INC 4 1798 3829 RHEOMETRIC SCIENTIFIC INC 13 1751 3826 NOVITRON INTL INC 17 1799 3829 SAC TECHNOLOGIES 3 1752 3826 O I CORP 19 1800 3829 SCIENTIFIC MEASUREMENT SYS 14 1753 3826 PERKIN-ELMER CORP 20 1801 3829 SENSYS TECHNOLOGIES INC 20 1754 3826 QIAGEN NV 3 1802 3829 SENTEX SENSING TECHNOLOGY 16 1755 3826 SEPRACOR INC 9 1803 3829 SIERRA MONITOR CORP 9 1756 3826 SEPRAGEN CORP -CL A 5 1804 3829 STRATEGIC DIAGNOSTICS INC 6 1757 3826 THERMO BIOANALYSIS CORP 3 1805 3829 SUTRON CORP 15 1758 3826 THERMO INSTRUMENT SYSTEMS 14 1806 3829 TECH-SYM CORP 19 1759 3826 THERMO OPTEK CORP 4 1807 3829 THERMO ELECTRON CORP 20 1760 3826 THERMOQUEST CORP 4 1808 3829 TRIMBLE NAVIGATION LTD 10 1761 3826 WATERS CORP 5 1809 3829 WINLAND ELECTRONICS INC 6 1762 3827 APA OPTICS INC 13 1810 3829 ZONIC CORP 18 1763 3827 AXSYS TECHNOLOGIES INC 19 1811 3841 ABIOMED INC 12 1764 3827 CYBEROPTICS CORP 13 1812 3841 ALARIS MEDICAL INC 8 1765 3827 GALILEO CORP 17 1813 3841 AMERICAN BIOMED INC 6
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1814 3841 ARADIGM CORP 3 1862 3841 PHARMANETICS INC 5 1815 3841 ARROW INTERNATIONAL 8 1863 3841 POSSIS MEDICAL INC 20 1816 3841 AVECOR CARDIOVASCULAR INC 6 1864 3841 RADIANCE MEDICAL SYSTEMS INC 3 1817 3841 BALLARD MEDICAL PRODUCTS 17 1865 3841 REPRO MEDSYSTEMS INC 15 1818 3841 BARD (C.R.) INC 20 1866 3841 RESMED INC 5 1819 3841 BAXTER INTERNATIONAL INC 20 1867 3841 ROCHESTER MEDICAL CORP 7 1820 3841 BECTON DICKINSON & CO 20 1868 3841 STRYKER CORP 20 1821 3841 BIOJECT MEDICAL TECHNOL 9 1869 3841 SURGIDYNE INC 13 1822 3841 BIO-PLEXUS INC 5 1870 3841 TELEFLEX INC 20 1823 3841 BIOSEARCH MEDICAL PRODS INC 18 1871 3841 TREX MEDICAL CORP 4 1824 3841 BOSTON SCIENTIFIC CORP 8 1872 3841 UROQUEST MEDICAL CORP 3 1825 3841 BOVIE MEDICAL CORP 12 1873 3841 UTAH MEDICAL PRODUCTS INC 17 1826 3841 CARDIAC PATHWAYS CORP 4 1874 3841 VIDAMED INC 3 1827 3841 CARDIMA INC 4 1875 3841 VITAL SIGNS INC 10 1828 3841 CARDIOTHORACIC SYSTEMS INC 3 1876 3841 VIVUS INC 3 1829 3841 CAS MEDICAL SYSTEMS INC 14 1877 3841 VYSIS INC 3 1830 3841 COMPUTER MOTION INC 4 1878 3842 ALLIED HEALTHCARE PRODS INC 8 1831 3841 CONCEPTUS INC 5 1879 3842 ATS MEDICAL INC 9 1832 3841 CYPRESS BIOSCIENCE INC 15 1880 3842 AVITAR INC 8 1833 3841 ELECTRO CATHETER CORP 20 1881 3842 BIO VASCULAR INC 13 1834 3841 EMBREX INC 8 1882 3842 BIOMET INC 17 1835 3841 FOCAL INC 4 1883 3842 CHAD THERAPEUTICS INC 15 1836 3841 GAMOGEN INC 11 1884 3842 CLOSURE MEDICAL CORP 4 1837 3841 GENERAL SURGICAL INNOVATIONS 4 1885 3842 CNS INC 13 1838 3841 GISH BIOMEDICAL INC 17 1886 3842 COHESION TECHNOLOGIES INC 3 1839 3841 HAEMONETICS CORPORATION 8 1887 3842 COLLAGEN AESTHETIC INC 18 1840 3841 HEARTPORT INC 3 1888 3842 DEXTERITY SURGICAL INC 6 1841 3841 HEMASURE INC 5 1889 3842 EMBRYO DEVELOPMENT CORP 3 1842 3841 ICU MEDICAL INC 8 1890 3842 EPITOPE INC 14 1843 3841 I-FLOW CORP 7 1891 3842 EXACTECH INC 4 1844 3841 IMAGYN MEDICAL TECHNOLOGIES 10 1892 3842 GUARDIAN TECHNLGS INTL INC 3 1845 3841 INNERDYNE INC 9 1893 3842 INAMED CORP 19 1846 3841 KENSEY NASH CORP 4 1894 3842 INNOVASIVE DEVICES INC 4 1847 3841 LJL BIOSYSTEMS INC 3 1895 3842 INTERPORE INTERNATIONAL 6 1848 3841 MAXXIM MEDICAL INC 10 1896 3842 INVACARE CORP 16 1849 3841 MEDAMICUS INC 9 1897 3842 KOALA CORP 7 1850 3841 MEDI-JECT CORP 3 1898 3842 LAKELAND INDUSTRIES INC 13 1851 3841 MERIDIAN MEDICAL TECH INC 20 1899 3842 LANGER BIOMECHANICS GROUP 14 1852 3841 MERIT MEDICAL SYSTEMS INC 9 1900 3842 LIFECELL CORP 7 1853 3841 MICRO THERAPEUTICS INC 4 1901 3842 MAMMATECH CORP 16 1854 3841 MINNTECH CORP 16 1902 3842 MEDICAL ACTION IND INC 14 1855 3841 NITINOL MED TECHNOLOGIES INC 3 1903 3842 MENTOR CORP 19 1856 3841 NUMEX CORP 18 1904 3842 MILESTONE SCIENTIFIC INC 3 1857 3841 OPHTHALMIC IMAGING SYS INC 8 1905 3842 MINE SAFETY APPLIANCES CO 19 1858 3841 OPTICAL SENSORS INC 3 1906 3842 MINIMED INC 5 1859 3841 ORTHOFIX INTERNATIONAL N V 7 1907 3842 RESOUND CORP 7 1860 3841 OXBORO MEDICAL INTL 19 1908 3842 RESPIRONICS INC 12 1861 3841 PERCLOSE INC 4 1909 3842 SAFETY FIRST INC 7
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1910 3842 SCOTT TECHNOLOGIES INC 20 1958 3845 BEI MEDICAL SYSTEMS CO INC 11 1911 3842 SMITH & NEPHEW PLC 7 1959 3845 BIOCONTROL TECHNOLOGY INC 17 1912 3842 SPAN-AMERICA MEDICAL SYS INC 17 1960 3845 BIO-LOGIC SYSTEMS CORP 15 1913 3842 STERIS CORP 7 1961 3845 BIOMAGNETIC TECHNOLOGIES 11 1914 3842 SULZER MEDICA -ADR 3 1962 3845 BIOSENSOR CORP 15 1915 3842 SUNRISE MEDICAL INC 15 1963 3845 BSD MEDICAL CORP/DE 18 1916 3842 TUTOGEN MEDICAL INC 13 1964 3845 CAMBRIDGE HEART INC 4 1917 3842 U S MEDICAL PRODUCTS INC 3 1965 3845 CANDELA CORP 14 1918 3842 UROMED CORP 4 1966 3845 CARDIAC CONTROL SYSTEMS INC 14 1919 3842 WORKSAFE INDUSTRIES INC 20 1967 3845 CARDIODYNAMICS INTL CORP 17 1920 3842 XOMED SURGICAL PRODS 4 1968 3845 CARDIOGENESIS CORP 3 1921 3843 BIOLASE TECHNOLOGY INC 7 1969 3845 CELSION CORP 16 1922 3843 BIOMERICA INC 19 1970 3845 CHOLESTECH CORP 7 1923 3843 BRITESMILE INC 8 1971 3845 COLORADO MEDTECH INC 16 1924 3843 DENTAL MED DIAGNOSTIC SYS 9 1972 3845 CONMED CORP 13 1925 3843 DENTSPLY INTERNATL INC 13 1973 3845 CRITICARE SYSTEMS INC 13 1926 3843 LANCER ORTHODONTICS INC 19 1974 3845 CRYOMEDICAL SCIENCES INC 5 1927 3843 LIFECORE BIOMEDICAL INC 20 1975 3845 CYBERONICS INC 7 1928 3843 MOYCO TECHNOLOGIES INC 20 1976 3845 DATASCOPE CORP 20 1929 3843 PRO-DEX INC/CO 14 1977 3845 DIAMETRICS MEDICAL INC 5 1930 3843 PROFESSIONAL DENTAL TECH INC 8 1978 3845 DIAPULSE CORP OF AMERICA 19 1931 3843 SUNRISE TECHNOLOGY INTL INC 11 1979 3845 DYNATRONICS CORP 15 1932 3843 SYBRON INTL CORP 11 1980 3845 ECLIPSE SURGICAL TECH INC 4 1933 3843 VICON FIBER OPTICS CORP 15 1981 3845 ELBIT MEDICAL IMAGING LTD 3 1934 3843 YOUNG INNOVATIONS INC 4 1982 3845 ELECTROPHARMACOLOGY INC 4 1935 3844 ADAC LABORATORIES 20 1983 3845 ELECTROSCOPE INC 3 1936 3844 AMERICAN SCIENCE ENGINEERING 19 1984 3845 ELSCINT LTD -ORD 19 1937 3844 FISCHER IMAGING CORP 9 1985 3845 EMPI INC 18 1938 3844 HOLOGIC INC 10 1986 3845 ENDOSONICS CORP 8 1939 3844 INVISION TECHNOLOGIES INC 4 1987 3845 EP MEDSYSTEMS INC 3 1940 3844 OEC MED SYS INC 17 1988 3845 ESC MEDICAL SYSTEMS LTD 4 1941 3844 SCHICK TECHNOLOGIES INC 3 1989 3845 EVEREST MEDICAL CORP 9 1942 3844 SWISSRAY INTERNATIONAL INC 3 1990 3845 EXOGEN INC 4 1943 3844 THERMOTREX CORP 9 1991 3845 GUIDANT CORP 6 1944 3844 VARIAN MEDICAL SYTEMS INC 20 1992 3845 HEALTHWATCH INC 15 1945 3844 VISION TEN INC 8 1993 3845 IMAGE GUIDED TECHNLGIES INC 3 1946 3844 VIVID TECHNOLOGIES INC 3 1994 3845 IMATRON INC 14 1947 3845 ABAXIS INC 5 1995 3845 INSTRUMENTARIUM CP -ADR 16 1948 3845 ACUSON CORP 14 1996 3845 INTEGRATED SURGICAL SYS INC 4 1949 3845 ADV NEUROMODULATION SYS INC 19 1997 3845 INVIVO CORP 13 1950 3845 ADVANCED MEDICAL PRODS 12 1998 3845 IRIDEX CORP 4 1951 3845 AFFYMETRIX INC 4 1999 3845 I-STAT CORP 8 1952 3845 AMERICAN DENTAL TECHNOL INC 9 2000 3845 LASER PHOTONICS INC 15 1953 3845 AMERICAN ELECTROMEDICS CORP 18 2001 3845 LASERSCOPE 11 1954 3845 ANGEION CORPORATION 9 2002 3845 LASERSIGHT INC 8 1955 3845 APPLIED BIOMETRICS INC 4 2003 3845 LECTEC CORP 13 1956 3845 ARRHYTHMIA RESH TECH 13 2004 3845 LUNAR CORPORATION 9 1957 3845 ARTHROCARE CORP 4 2005 3845 LUXTEC CORP 14
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2006 3845 MEDICAL DEVICE TECH INC 13 2054 3851 LUXOTTICA GROUP SPA -ADR 9 2007 3845 MEDICAL GRAPHICS CORP 18 2055 3851 OAKLEY INC 4 2008 3845 MEDIS EL LTD 4 2056 3851 OCULAR SCIENCES INC 3 2009 3845 MEDSTONE INTERNATIONAL INC 11 2057 3851 SERENGETI EYEWEAR INC 4 2010 3845 MEDTRONIC INC 19 2058 3851 SIGNATURE EYEWEAR INC 4 2011 3845 MIRAVANT MEDICAL TECHNOLGIES 4 2059 3851 SOLA INTL INC 6 2012 3845 NEOPATH INC 3 2060 3851 STAAR SURGICAL CO 14 2013 3845 NOVAMETRIX MEDICAL SYSTEMS 19 2061 3851 STAR SCIENTIFIC INC 12 2014 3845 NOVOSTE CORP 3 2062 3851 UNILENS VISION INC 9 2015 3845 PACE MEDICAL INC 13 2063 3851 WESLEY JESSEN VISIONCARE INC 4 2016 3845 PALOMAR MED TECHNOLOGIES INC 7 2064 3861 3D IMAGE TECHNOLOGY INC 3 2017 3845 PARADIGM MEDICAL INDS INC 3 2065 3861 ACCOM INC 5 2018 3845 PARK MEDITECH INC 4 2066 3861 AFP IMAGING CORP 19 2019 3845 PHYSIOMETRIX INC 4 2067 3861 ANACOMP INC 20 2020 3845 PLC SYSTEMS INC 6 2068 3861 AVID TECHNOLOGY INC 7 2021 3845 POSITRON CORP 6 2069 3861 BALLANTYNE OF OMAHA INC 4 2022 3845 PRECISION OPTICS CORP INC/MA 8 2070 3861 CAMERA PLATFORMS INTL INC 12 2023 3845 PREMIER LASER SYS -CL A 5 2071 3861 CONCORD CAMERA CORP 12 2024 3845 PROTOCOL SYSTEMS INC 8 2072 3861 DYCAM INC 5 2025 3845 Q MED INC 14 2073 3861 EASTMAN KODAK CO 20 2026 3845 REHABILICARE INC 19 2074 3861 FUJI PHOTO FILM -ADR 19 2027 3845 SABRATEK CORP 4 2075 3861 IMATION CORP 4 2028 3845 SEAMED CORP 3 2076 3861 IMAX CORP 5 2029 3845 SOMANETICS CORP 8 2077 3861 IN FOCUS SYSTEMS INC 10 2030 3845 SPACELABS MED INC 7 2078 3861 INTERSCIENCE COMPUTER CORP 6 2031 3845 SPARTA SURGICAL CORP 8 2079 3861 INTL TELEPRESENCE CDA CORP 10 2032 3845 SPECTRANETICS CORP 9 2080 3861 NU-KOTE HLDG INC -CL A 7 2033 3845 SPECTRASCIENCE 9 2081 3861 NVIEW CORP 7 2034 3845 ST JUDE MEDICAL INC 20 2082 3861 OCE NV -ADR 14 2035 3845 SURGICAL LASER TECH INC 11 2083 3861 PANAVISION INC 3 2036 3845 THERMO CARDIOSYSTEMS 12 2084 3861 PHOTO CONTROL CORP 19 2037 3845 THORATEC LABORATORIES CORP 19 2085 3861 PINNACLE SYSTEMS INC 5 2038 3845 TRIMEDYNE INC 16 2086 3861 POLAROID CORP 20 2039 3845 UROLOGIX INC 4 2087 3861 PRINTWARE INC 4 2040 3845 VALLEY FORGE SCIENTIFIC CORP 10 2088 3861 SPECTRA-PHYSICS LASERS INC 4 2041 3845 VASOMEDICAL INC 5 2089 3861 TRANSNATIONAL INDUSTRIES INC 13 2042 3845 VISIBLE GENETICS INC 3 2090 3861 ULTRATECH STEPPER INC 7 2043 3845 VISION-SCIENCES INC 7 2091 3861 VIDEOLABS INC 5 2044 3845 VISTA MED TECHNOLOGIES INC 3 2092 3861 VIDEONICS INC 5 2045 3845 VISX INC/DE 12 2093 3861 VIRTUALFUND.COM INC 9 2046 3845 WORK RECOVERY INC 7 2094 3861 X-RITE INC 14 2047 3845 ZEVEX INTERNATIONAL INC 4 2095 3873 COSMO COMMUNICATIONS CORP 16 2048 3845 ZOLL MEDICAL CORP 8 2096 3873 FOSSIL INC 6 2049 3851 AMERICAN CONSOLIDATED LABS 11 2097 3873 MOVADO GROUP INC 16 2050 3851 BACOU USA INC 3 2098 3873 RHODES (MH) INC 16 2051 3851 COOPER COMPANIES INC 17 2099 3873 TAG HEUER INTL SA -ADR 3 2052 3851 DE RIGO S P A -SPON ADR 4 2053 3851 GARGOYLES INC 3
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290
A3. Regression Results for OF2 using Sample #2
SIC NFIRM RSQR ADJRSQ Aj Aj_pval Bj Bj_pval 2800 15 0.118 0.050 -5.019 0.000 0.006 0.211 2810 21 0.000 -0.053 -4.824 0.000 0.001 0.963 2820 8 0.031 -0.130 -5.374 0.000 0.003 0.675 2821 23 0.000 -0.048 -5.029 0.000 0.000 0.988 2833 10 0.487 0.423 -6.804 0.000 3.234 0.025 2834 160 0.026 0.020 -5.752 0.000 0.014 0.042 2835 66 0.032 0.017 -6.421 0.000 0.131 0.153 2836 102 0.054 0.044 -6.177 0.000 0.400 0.019 2840 8 0.082 -0.071 -4.742 0.000 -0.006 0.492 2842 8 0.083 -0.069 -6.673 0.000 0.057 0.488 2844 33 0.038 0.007 -5.905 0.000 0.023 0.274 2851 11 0.025 -0.083 -5.972 0.000 0.011 0.641 2860 19 0.104 0.051 -5.361 0.000 0.020 0.178 2870 16 0.059 -0.009 -5.442 0.000 0.025 0.366 2890 18 0.145 0.091 -5.727 0.000 0.041 0.119 3411 6 0.015 -0.231 -4.794 0.003 0.016 0.816 3420 15 0.090 0.020 -5.818 0.000 0.030 0.279 3433 3 0.930 0.861 -7.167 0.023 2.383 0.170 3440 10 0.150 0.044 -5.334 0.000 0.330 0.269 3442 4 0.045 -0.433 -5.222 0.007 0.083 0.789 3443 15 0.006 -0.071 -5.210 0.000 0.011 0.784 3448 6 0.153 -0.059 -5.714 0.001 0.206 0.443 3452 9 0.245 0.137 -5.777 0.000 0.353 0.176 3460 15 0.126 0.058 -5.069 0.000 0.241 0.195 3470 8 0.202 0.069 -5.957 0.000 0.074 0.264 3480 6 0.253 0.066 -5.346 0.001 0.147 0.310 3490 20 0.042 -0.012 -5.637 0.000 0.055 0.388 3510 7 0.313 0.175 -4.142 0.000 -0.050 0.192 3523 15 0.063 -0.009 -5.143 0.000 0.014 0.367 3530 4 0.000 -0.500 -4.855 0.063 0.002 0.987 3531 9 0.002 -0.140 -4.978 0.000 0.001 0.906 3533 12 0.026 -0.072 -4.041 0.000 -0.030 0.618 3537 5 0.124 -0.168 -5.278 0.002 0.084 0.561 3540 16 0.186 0.128 -5.343 0.000 0.030 0.096 3541 4 0.042 -0.437 -3.897 0.046 -0.130 0.795 3550 7 0.743 0.692 -5.959 0.000 0.251 0.013 3555 10 0.025 -0.097 -5.085 0.000 0.239 0.665 3559 62 0.035 0.019 -4.872 0.000 0.164 0.147 3560 18 0.230 0.182 -5.619 0.000 0.011 0.044 3561 3 0.729 0.458 -6.876 0.072 0.657 0.349 3562 7 0.057 -0.132 -5.335 0.000 0.027 0.606 3564 12 0.197 0.117 -6.669 0.000 0.489 0.148 3567 7 0.077 -0.108 -6.407 0.001 2.318 0.547
291
3569 17 0.203 0.150 -6.315 0.000 0.099 0.070 3570 6 0.021 -0.224 -5.103 0.000 0.000 0.783 3571 38 0.032 0.005 -5.116 0.000 0.006 0.287 3572 28 0.048 0.012 -4.789 0.000 0.050 0.262 3575 12 0.032 -0.065 -5.769 0.000 0.096 0.580 3576 73 0.082 0.069 -5.168 0.000 0.171 0.014 3577 68 0.001 -0.014 -5.061 0.000 0.002 0.811 3578 21 0.008 -0.044 -5.703 0.000 0.011 0.702 3579 9 0.337 0.242 -5.684 0.000 0.015 0.101 3580 20 0.075 0.024 -5.861 0.000 0.086 0.243 3585 18 0.072 0.014 -5.402 0.000 0.030 0.282 3590 14 0.364 0.311 -6.472 0.000 0.664 0.022 3613 6 0.157 -0.054 -6.186 0.000 0.699 0.437 3620 20 0.096 0.046 -5.787 0.000 0.016 0.184 3621 11 0.191 0.101 -6.417 0.000 0.179 0.179 3630 9 0.030 -0.109 -3.951 0.000 -0.006 0.659 3634 7 0.020 -0.176 -4.806 0.000 0.037 0.762 3640 30 0.026 -0.009 -5.579 0.000 0.030 0.395 3651 24 0.023 -0.022 -5.629 0.000 0.005 0.483 3652 7 0.239 0.087 -5.161 0.000 0.919 0.266 3661 86 0.028 0.016 -5.127 0.000 0.007 0.125 3663 92 0.034 0.024 -5.435 0.000 0.016 0.077 3669 29 0.246 0.218 -6.409 0.000 0.585 0.006 3670 9 0.017 -0.124 -5.167 0.000 0.012 0.739 3672 26 0.108 0.071 -5.130 0.000 0.162 0.101 3674 111 0.013 0.004 -4.832 0.000 0.007 0.237 3678 14 0.007 -0.076 -5.237 0.000 -0.019 0.776 3679 40 0.322 0.304 -6.466 0.000 0.985 0.000 3690 32 0.218 0.192 -5.903 0.000 0.363 0.007 3695 4 0.136 -0.296 -4.868 0.084 0.771 0.631 3812 20 0.053 0.000 -5.538 0.000 0.009 0.331 3821 9 0.176 0.058 -7.649 0.000 2.326 0.261 3822 3 0.026 -0.948 -6.331 0.250 0.014 0.897 3823 31 0.010 -0.024 -6.340 0.000 0.013 0.587 3824 7 0.331 0.197 -7.670 0.000 4.156 0.177 3825 44 0.009 -0.015 -5.639 0.000 0.050 0.544 3826 32 0.137 0.108 -6.177 0.000 0.268 0.037 3827 17 0.223 0.171 -6.542 0.000 1.394 0.056 3829 32 0.094 0.064 -6.553 0.000 0.378 0.087 3841 67 0.045 0.030 -6.220 0.000 0.063 0.085 3842 44 0.208 0.190 -6.215 0.000 0.327 0.002 3843 14 0.481 0.438 -6.549 0.000 0.797 0.006 3844 12 0.015 -0.084 -4.983 0.000 -0.059 0.706 3845 102 0.093 0.084 -6.305 0.000 0.371 0.002 3851 15 0.242 0.184 -5.367 0.000 0.584 0.063 3861 31 0.008 -0.026 -5.288 0.000 0.005 0.633 3873 5 0.668 0.557 -6.911 0.002 1.505 0.091
292
A4. Regression Results for VF1 using Sample #2
SIC NFIRM RSQR ADJRSQ Aj Aj_pval Bj Bj_pval 2800 15 0.249 0.191 0.493 0.046 -0.005 0.058 2810 21 0.235 0.195 0.946 0.000 -0.045 0.026 2820 8 0.232 0.104 1.165 0.012 -0.005 0.227 2821 23 0.118 0.076 0.842 0.000 -0.011 0.109 2833 8 0.005 -0.161 1.261 0.110 -0.388 0.866 2834 121 0.176 0.169 1.337 0.000 -0.028 0.000 2835 58 0.035 0.018 1.478 0.000 -0.090 0.159 2836 49 0.080 0.061 1.508 0.000 -0.348 0.049 2840 8 0.132 -0.013 0.649 0.123 -0.006 0.376 2842 8 0.631 0.569 1.285 0.001 -0.154 0.019 2844 33 0.091 0.062 0.906 0.000 -0.031 0.088 2851 11 0.355 0.283 1.238 0.003 -0.047 0.053 2860 19 0.364 0.327 0.912 0.000 -0.024 0.006 2870 16 0.222 0.167 1.074 0.000 -0.016 0.065 2890 18 0.168 0.116 1.034 0.001 -0.032 0.091 3411 6 0.567 0.459 0.730 0.019 -0.037 0.084 3420 15 0.122 0.055 0.713 0.003 -0.026 0.202 3433 3 0.990 0.980 2.534 0.042 -4.284 0.064 3440 10 0.105 -0.007 0.106 0.867 0.318 0.360 3442 4 0.881 0.821 1.558 0.009 -0.335 0.062 3443 15 0.386 0.339 1.393 0.000 -0.054 0.013 3448 6 0.440 0.299 1.487 0.014 -0.226 0.151 3452 9 0.319 0.221 1.249 0.001 -0.293 0.113 3460 15 0.017 -0.058 0.850 0.028 -0.087 0.641 3470 8 0.338 0.227 1.614 0.002 -0.059 0.131 3480 6 0.031 -0.211 0.196 0.708 0.040 0.738 3490 20 0.510 0.483 0.962 0.000 -0.094 0.000 3510 7 0.538 0.445 1.609 0.027 -0.088 0.061 3523 15 0.418 0.373 0.685 0.003 -0.022 0.009 3530 4 0.874 0.812 0.884 0.071 -0.087 0.065 3531 9 0.225 0.114 0.672 0.022 -0.010 0.197 3533 11 0.184 0.094 1.270 0.002 -0.064 0.188 3537 5 0.030 -0.294 0.422 0.644 -0.060 0.782 3540 16 0.234 0.179 0.925 0.000 -0.045 0.058 3541 4 0.091 -0.364 1.078 0.389 -0.223 0.699 3550 7 0.931 0.917 1.194 0.000 -0.128 0.000 3555 10 0.210 0.111 1.340 0.005 -0.532 0.183 3559 61 0.049 0.033 1.373 0.000 -0.158 0.086 3560 18 0.291 0.247 1.207 0.000 -0.010 0.021 3561 3 0.224 -0.552 0.700 0.380 -0.132 0.686 3562 7 0.174 0.008 0.920 0.061 -0.032 0.353 3564 12 0.267 0.193 1.323 0.000 -0.300 0.086 3567 7 0.006 -0.194 1.835 0.029 -0.419 0.875
293
3569 17 0.218 0.166 1.053 0.008 -0.134 0.059 3570 6 0.568 0.460 0.335 0.361 -0.003 0.084 3571 38 0.133 0.108 1.105 0.000 -0.011 0.025 3572 28 0.161 0.129 1.567 0.000 -0.127 0.034 3575 12 0.175 0.092 1.055 0.024 -0.187 0.176 3576 73 0.147 0.135 1.439 0.000 -0.158 0.001 3577 68 0.027 0.012 1.082 0.000 -0.008 0.180 3578 20 0.168 0.122 1.102 0.000 -0.034 0.073 3579 9 0.002 -0.141 0.739 0.034 0.001 0.921 3580 20 0.045 -0.008 1.024 0.000 -0.033 0.370 3585 18 0.130 0.075 0.744 0.018 -0.035 0.142 3590 14 0.106 0.032 1.105 0.000 -0.190 0.256 3613 6 0.047 -0.191 1.160 0.073 -0.309 0.679 3620 20 0.073 0.022 0.779 0.001 -0.011 0.249 3621 11 0.249 0.166 1.032 0.001 -0.125 0.118 3630 9 0.396 0.309 0.990 0.006 -0.016 0.070 3634 7 0.164 -0.003 0.627 0.030 -0.043 0.368 3640 30 0.209 0.180 1.198 0.000 -0.061 0.011 3651 24 0.068 0.025 0.933 0.000 -0.009 0.220 3652 7 0.002 -0.197 2.233 0.054 -0.189 0.919 3661 86 0.033 0.022 1.059 0.000 -0.005 0.092 3663 92 0.125 0.115 1.019 0.000 -0.020 0.001 3669 29 0.084 0.050 0.997 0.000 -0.226 0.127 3670 9 0.501 0.430 1.192 0.001 -0.063 0.033 3672 26 0.146 0.111 1.184 0.000 -0.115 0.054 3674 110 0.021 0.012 1.212 0.000 -0.006 0.135 3678 14 0.501 0.459 1.121 0.000 -0.081 0.005 3679 40 0.008 -0.019 1.133 0.000 -0.072 0.592 3690 31 0.108 0.078 1.276 0.000 -0.203 0.071 3695 4 0.597 0.395 2.095 0.011 -0.349 0.227 3812 20 0.462 0.432 0.949 0.000 -0.021 0.001 3821 9 0.097 -0.032 1.128 0.003 -0.892 0.414 3822 3 0.846 0.691 1.560 0.175 -0.034 0.257 3823 31 0.058 0.026 1.056 0.000 -0.024 0.192 3824 7 0.001 -0.199 0.876 0.019 -0.060 0.943 3825 44 0.256 0.238 1.256 0.000 -0.191 0.001 3826 32 0.158 0.130 1.578 0.000 -0.244 0.024 3827 17 0.186 0.131 1.523 0.000 -0.544 0.084 3829 32 0.064 0.032 1.349 0.000 -0.191 0.164 3841 67 0.050 0.035 1.202 0.000 -0.049 0.070 3842 43 0.058 0.035 1.273 0.000 -0.126 0.120 3843 14 0.145 0.074 1.430 0.000 -0.256 0.179 3844 12 0.202 0.123 1.412 0.002 -0.203 0.142 3845 102 0.039 0.029 1.118 0.000 -0.190 0.048 3851 15 0.060 -0.013 0.880 0.004 -0.146 0.380 3861 31 0.089 0.057 0.815 0.000 -0.010 0.104 3873 5 0.325 0.100 0.577 0.115 -0.278 0.316
294
A5. Regression Results for VF2 using Sample #2
SIC NFIRM RSQR ADJRSQ Aj Aj_pval Bj Bj_pval 2800 15 0.004 -0.072 -0.028 0.671 0.000 0.817 2810 21 0.360 0.326 0.290 0.003 -0.023 0.004 2820 8 0.019 -0.144 0.065 0.589 0.000 0.744 2821 23 0.005 -0.042 0.174 0.043 -0.001 0.745 2833 10 0.109 -0.002 0.522 0.273 1.607 0.351 2834 160 0.003 -0.003 0.080 0.323 -0.003 0.494 2835 66 0.001 -0.015 -0.014 0.914 0.012 0.864 2836 102 0.010 0.001 -0.099 0.435 0.156 0.308 2840 8 0.013 -0.152 0.078 0.360 0.000 0.789 2842 8 0.296 0.178 0.381 0.028 -0.050 0.164 2844 33 0.033 0.002 0.398 0.010 -0.013 0.314 2851 11 0.051 -0.054 0.337 0.016 -0.006 0.503 2860 19 0.090 0.037 0.200 0.010 -0.004 0.212 2870 16 0.004 -0.068 -0.034 0.875 0.003 0.823 2890 18 0.132 0.078 0.172 0.071 -0.010 0.139 3411 6 0.061 -0.174 -0.008 0.875 0.002 0.637 3420 15 0.130 0.063 0.203 0.020 -0.011 0.187 3433 3 0.995 0.990 0.865 0.050 -2.522 0.044 3440 10 0.006 -0.119 0.132 0.554 -0.024 0.838 3442 4 0.022 -0.468 0.142 0.397 -0.017 0.853 3443 15 0.008 -0.068 0.145 0.024 -0.003 0.749 3448 6 0.384 0.230 0.167 0.069 -0.038 0.189 3452 9 0.136 0.013 0.321 0.002 -0.045 0.329 3460 15 0.000 -0.077 0.044 0.651 -0.004 0.944 3470 8 0.067 -0.089 0.233 0.098 -0.009 0.537 3480 6 0.362 0.202 0.330 0.060 -0.044 0.207 3490 20 0.195 0.150 0.251 0.000 -0.020 0.051 3510 7 0.623 0.548 0.277 0.013 -0.015 0.035 3523 15 0.002 -0.075 0.210 0.004 0.000 0.889 3530 4 0.385 0.077 0.099 0.403 -0.010 0.380 3531 9 0.006 -0.136 0.077 0.370 -0.001 0.841 3533 12 0.076 -0.016 0.214 0.031 -0.012 0.386 3537 5 0.499 0.331 0.242 0.075 -0.037 0.183 3540 16 0.215 0.159 0.185 0.000 -0.009 0.070 3541 4 0.578 0.367 0.323 0.168 -0.128 0.240 3550 7 0.499 0.399 0.342 0.009 -0.031 0.076 3555 10 0.002 -0.123 0.389 0.112 -0.028 0.905 3559 62 0.042 0.026 0.605 0.000 -0.100 0.112 3560 18 0.041 -0.019 0.252 0.000 -0.001 0.420 3561 3 0.064 -0.873 0.148 0.685 0.037 0.838 3562 7 0.263 0.116 0.304 0.014 -0.009 0.239 3564 12 0.105 0.016 0.365 0.014 -0.130 0.304 3567 7 0.059 -0.129 0.313 0.016 -0.208 0.598
295
3569 17 0.105 0.046 0.101 0.713 -0.068 0.204 3570 6 0.001 -0.249 -0.010 0.959 0.000 0.961 3571 38 0.124 0.100 0.401 0.000 -0.003 0.030 3572 28 0.033 -0.004 0.256 0.001 -0.013 0.353 3575 12 0.003 -0.097 0.106 0.555 -0.010 0.863 3576 73 0.071 0.058 0.644 0.000 -0.069 0.023 3577 68 0.122 0.109 0.376 0.000 -0.012 0.004 3578 21 0.056 0.006 0.241 0.015 -0.010 0.302 3579 9 0.178 0.060 0.247 0.025 -0.003 0.259 3580 20 0.000 -0.055 0.387 0.000 0.001 0.952 3585 18 0.333 0.291 0.266 0.001 -0.015 0.012 3590 14 0.460 0.415 0.444 0.000 -0.142 0.008 3613 6 0.544 0.431 0.257 0.028 0.241 0.094 3620 20 0.048 -0.005 0.165 0.021 -0.003 0.353 3621 11 0.175 0.083 0.291 0.040 -0.055 0.201 3630 9 0.000 -0.143 0.146 0.043 0.000 0.998 3634 7 0.613 0.535 0.360 0.000 -0.020 0.038 3640 30 0.108 0.076 0.220 0.000 -0.013 0.076 3651 24 0.001 -0.045 0.133 0.222 0.000 0.908 3652 7 0.265 0.118 0.654 0.035 -0.604 0.238 3661 86 0.022 0.011 0.418 0.000 -0.003 0.171 3663 92 0.012 0.001 0.327 0.000 -0.005 0.309 3669 29 0.031 -0.005 0.452 0.000 -0.064 0.360 3670 9 0.172 0.053 0.238 0.073 -0.015 0.268 3672 26 0.169 0.135 0.191 0.000 -0.038 0.037 3674 111 0.013 0.004 0.539 0.000 -0.004 0.233 3678 14 0.138 0.066 0.228 0.005 -0.014 0.191 3679 40 0.030 0.005 0.258 0.009 -0.112 0.282 3690 32 0.012 -0.021 0.326 0.007 -0.042 0.559 3695 4 0.036 -0.446 0.410 0.164 0.047 0.810 3812 20 0.109 0.059 0.312 0.002 -0.006 0.155 3821 9 0.005 -0.137 0.155 0.662 0.251 0.860 3822 3 0.813 0.626 0.526 0.228 -0.014 0.285 3823 31 0.074 0.042 0.537 0.000 -0.013 0.139 3824 7 0.315 0.178 0.680 0.010 -0.783 0.190 3825 44 0.065 0.043 0.610 0.000 -0.079 0.094 3826 32 0.020 -0.013 0.352 0.066 -0.061 0.442 3827 17 0.008 -0.058 0.186 0.458 0.157 0.734 3829 32 0.000 -0.033 0.258 0.240 0.007 0.966 3841 67 0.005 -0.011 0.337 0.016 -0.014 0.584 3842 44 0.003 -0.021 0.264 0.025 -0.019 0.713 3843 14 0.418 0.370 0.640 0.000 -0.181 0.013 3844 12 0.181 0.099 0.464 0.005 -0.073 0.168 3845 102 0.000 -0.010 0.342 0.001 0.004 0.961 3851 15 0.010 -0.066 0.773 0.111 -0.105 0.722 3861 31 0.010 -0.025 0.420 0.001 -0.003 0.599 3873 5 0.823 0.764 -0.174 0.602 0.983 0.034
296
A6. Regression Results for VF3 using Sample #2
SIC NFIRM RSQR ADJRSQ Aj Aj_pval Bj Bj_pval 2800 15 0.138 0.072 -0.248 0.038 -0.002 0.173 2810 21 0.292 0.255 0.064 0.563 -0.025 0.011 2820 8 0.120 -0.027 -0.061 0.622 -0.001 0.401 2821 23 0.063 0.019 -0.038 0.626 -0.003 0.247 2833 10 0.025 -0.097 0.546 0.315 -0.847 0.662 2834 160 0.023 0.017 -0.048 0.542 -0.009 0.057 2835 66 0.000 -0.016 -0.088 0.476 0.001 0.993 2836 102 0.000 -0.010 -0.145 0.260 0.008 0.956 2840 8 0.415 0.318 -0.188 0.028 -0.002 0.085 2842 8 0.578 0.508 0.264 0.067 -0.081 0.029 2844 33 0.046 0.015 0.099 0.528 -0.017 0.233 2851 11 0.571 0.523 0.180 0.061 -0.020 0.007 2860 19 0.315 0.275 0.058 0.495 -0.011 0.012 2870 16 0.001 -0.071 -0.131 0.516 -0.001 0.920 2890 18 0.156 0.103 -0.031 0.759 -0.013 0.105 3411 6 0.448 0.310 -0.108 0.224 -0.011 0.146 3420 15 0.133 0.067 -0.031 0.753 -0.014 0.181 3433 3 0.999 0.998 0.871 0.025 -2.763 0.020 3440 10 0.052 -0.067 -0.474 0.292 0.148 0.528 3442 4 0.314 -0.030 0.118 0.377 -0.061 0.440 3443 15 0.317 0.264 0.085 0.079 -0.015 0.029 3448 6 0.525 0.407 0.111 0.245 -0.061 0.103 3452 9 0.402 0.317 0.206 0.020 -0.100 0.067 3460 15 0.000 -0.077 -0.166 0.440 -0.002 0.987 3470 8 0.183 0.047 0.173 0.233 -0.017 0.291 3480 6 0.007 -0.241 -0.166 0.598 -0.011 0.875 3490 20 0.491 0.463 0.117 0.100 -0.047 0.001 3510 7 0.602 0.522 0.156 0.324 -0.027 0.040 3523 15 0.387 0.340 -0.030 0.756 -0.010 0.013 3530 4 0.850 0.776 0.009 0.953 -0.041 0.078 3531 9 0.148 0.026 -0.093 0.424 -0.004 0.307 3533 12 0.218 0.139 0.106 0.259 -0.023 0.127 3537 5 0.001 -0.332 -0.368 0.533 -0.008 0.954 3540 16 0.273 0.222 0.011 0.837 -0.016 0.038 3541 4 0.145 -0.283 0.081 0.831 -0.099 0.620 3550 7 0.917 0.901 0.273 0.006 -0.074 0.001 3555 10 0.072 -0.044 0.309 0.196 -0.183 0.455 3559 62 0.082 0.067 0.359 0.000 -0.086 0.024 3560 18 0.649 0.627 0.137 0.004 -0.005 0.000 3561 3 0.481 -0.038 0.003 0.970 -0.027 0.512 3562 7 0.288 0.146 0.101 0.441 -0.014 0.214 3564 12 0.152 0.067 0.263 0.049 -0.154 0.210 3567 7 0.095 -0.086 0.244 0.008 -0.175 0.500 3569 17 0.166 0.110 -0.086 0.743 -0.084 0.105 3570 6 0.489 0.361 -0.189 0.484 -0.002 0.122
297
3571 38 0.195 0.173 0.129 0.077 -0.005 0.006 3572 28 0.212 0.181 0.142 0.026 -0.032 0.014 3575 12 0.126 0.039 -0.022 0.927 -0.090 0.257 3576 73 0.127 0.115 0.468 0.000 -0.090 0.002 3577 68 0.132 0.119 0.201 0.002 -0.012 0.002 3578 21 0.117 0.071 0.106 0.308 -0.017 0.129 3579 9 0.055 -0.080 0.028 0.848 -0.003 0.544 3580 20 0.002 -0.053 0.157 0.034 -0.003 0.844 3585 18 0.127 0.072 -0.083 0.614 -0.020 0.147 3590 14 0.302 0.243 0.249 0.007 -0.128 0.042 3613 6 0.106 -0.118 0.088 0.647 0.176 0.530 3620 20 0.074 0.023 -0.044 0.595 -0.005 0.245 3621 11 0.224 0.138 0.174 0.240 -0.073 0.142 3630 9 0.309 0.210 0.027 0.802 -0.005 0.120 3634 7 0.343 0.212 0.077 0.355 -0.026 0.167 3640 30 0.243 0.216 0.112 0.039 -0.024 0.006 3651 24 0.025 -0.020 -0.052 0.643 -0.003 0.465 3652 7 0.069 -0.118 0.328 0.055 -0.158 0.570 3661 86 0.052 0.041 0.182 0.002 -0.004 0.034 3663 92 0.065 0.055 0.119 0.065 -0.011 0.014 3669 29 0.060 0.025 0.212 0.017 -0.107 0.201 3670 9 0.437 0.357 0.128 0.253 -0.026 0.052 3672 26 0.173 0.139 0.075 0.176 -0.048 0.034 3674 111 0.016 0.007 0.311 0.000 -0.004 0.187 3678 14 0.542 0.504 0.158 0.038 -0.039 0.003 3679 40 0.017 -0.009 0.100 0.259 -0.076 0.427 3690 32 0.030 -0.002 0.150 0.130 -0.058 0.344 3695 4 0.005 -0.493 0.397 0.175 0.017 0.930 3812 20 0.388 0.354 0.150 0.061 -0.011 0.003 3821 9 0.001 -0.142 -0.023 0.939 0.071 0.953 3822 3 0.872 0.743 0.461 0.287 -0.019 0.233 3823 31 0.103 0.072 0.305 0.001 -0.015 0.078 3824 7 0.142 -0.029 0.339 0.082 -0.437 0.404 3825 44 0.124 0.103 0.419 0.000 -0.113 0.019 3826 32 0.054 0.022 0.166 0.253 -0.078 0.203 3827 17 0.002 -0.065 0.130 0.600 0.075 0.870 3829 32 0.001 -0.032 0.159 0.460 -0.032 0.837 3841 67 0.014 -0.001 0.056 0.583 -0.018 0.345 3842 44 0.005 -0.019 0.082 0.473 -0.023 0.650 3843 14 0.367 0.314 0.474 0.000 -0.173 0.022 3844 12 0.315 0.247 0.291 0.039 -0.100 0.058 3845 102 0.002 -0.008 0.103 0.296 -0.041 0.630 3851 15 0.013 -0.063 0.183 0.444 -0.062 0.682 3861 31 0.040 0.007 0.124 0.288 -0.006 0.282 3873 5 0.012 -0.318 0.002 0.989 -0.025 0.862
298
A7. Regression Results for VF4 using Sample #2
Our Hypothesis Formulations Results H1 SVij = aj + bj*Sij + eij
· SVij = standard error from the regression of sales data for firm i in industry j. · Sij = size of firm i in industry j, measured by the natural log of average sales .
H1 suggest bj < 0 bj > 0
SIC NFIRM RSQR Bj Bj_pval 2800 15 0.710 -0.070 0.000 2810 21 0.648 -0.100 0.000 2820 8 0.431 0.737 0.077 2821 23 0.391 0.227 0.001 2833 10 0.198 0.770 0.269 2834 160 0.008 -0.210 0.338 2835 66 0.261 3.083 0.000 2836 102 0.312 2.803 0.000 2840 8 0.692 -0.123 0.010 2842 8 0.290 1.401 0.168 2844 33 0.092 0.186 0.086 2851 11 0.454 0.397 0.023 2860 19 0.537 -0.103 0.000 2870 16 0.006 0.011 0.770 2890 18 0.373 0.679 0.007 3411 6 0.601 -0.066 0.070 3420 15 0.366 0.427 0.017 3433 3 0.964 0.039 0.122 3440 10 0.594 0.368 0.009 3442 4 0.958 -0.033 0.021 3443 15 0.371 -0.024 0.016 3448 6 0.542 0.084 0.095 3452 9 0.356 -0.061 0.090 3460 15 0.648 -0.076 0.000 3470 8 0.089 -0.032 0.472 3480 6 0.022 0.009 0.777 3490 20 0.451 -0.051 0.001 3510 7 0.081 -0.028 0.537 3523 15 0.760 -0.062 0.000
299
3530 4 0.673 -0.110 0.180 3531 9 0.630 -0.056 0.011 3533 12 0.062 -0.024 0.459 3537 5 0.117 -0.035 0.573 3540 16 0.291 0.769 0.031 3541 4 0.580 0.144 0.239 3550 7 0.136 0.073 0.416 3555 10 0.369 0.379 0.048 3559 62 0.228 0.393 0.000 3560 18 0.133 0.381 0.125 3561 3 0.541 -0.090 0.474 3562 7 0.458 -0.065 0.095 3564 12 0.371 1.354 0.035 3567 7 0.220 0.454 0.288 3569 17 0.320 0.552 0.018 3570 6 0.432 -0.117 0.156 3571 38 0.118 1.080 0.038 3572 28 0.004 -0.012 0.751 3575 12 0.134 0.462 0.268 3576 73 0.062 0.144 0.038 3577 68 0.113 0.205 0.005 3578 21 0.336 9.126 0.007 3579 9 0.247 -0.048 0.173 3580 20 0.310 3.254 0.013 3585 18 0.338 1.165 0.011 3590 14 0.127 0.050 0.192 3613 6 0.430 -0.054 0.157 3620 20 0.288 -0.057 0.015 3621 11 0.144 0.063 0.249 3630 9 0.294 -0.055 0.131 3634 7 0.017 0.017 0.781 3640 30 0.151 0.123 0.034 3651 24 0.274 0.774 0.009 3652 7 0.091 0.108 0.511 3661 86 0.228 0.415 0.000 3663 92 0.103 0.557 0.002 3669 29 0.096 0.120 0.103 3670 9 0.308 -0.065 0.121 3672 26 0.059 0.052 0.233 3674 111 0.066 -0.120 0.007 3678 14 0.348 -0.085 0.026 3679 40 0.270 0.895 0.001 3690 32 0.241 0.566 0.005 3695 4 0.206 -0.060 0.547 3812 20 0.674 -0.076 0.000 3821 9 0.001 0.013 0.938 3822 3 0.153 -0.017 0.744 3823 31 0.009 -0.020 0.619
300
3824 7 0.004 0.009 0.889 3825 44 0.247 -0.322 0.001 3826 32 0.428 2.993 0.000 3827 17 0.362 0.919 0.011 3829 32 0.130 -4.131 0.043 3841 67 0.135 3.247 0.002 3842 44 0.107 1.999 0.032 3843 14 0.123 0.372 0.219 3844 12 0.540 1.094 0.007 3845 102 0.112 -70.718 0.001 3851 15 0.375 0.289 0.015 3861 31 0.174 0.156 0.020 3873 5 0.001 0.002 0.955
301
Appendix B. Supporting Information for Chapter 4
B.1. Case Study Questionnaire
B.1.1 Demographic Data
Firm Name Firm’s Address: Respondent’s Name: Respondent’s Title: The major product(s) that we produce: The primary industry that we compete in is: The average number of a. 500 employees we have is: b. Employees > 500 Our average annual a. $100M sales place us in the b. Sales > $100M following category:
B.1.2 Assessing your Competitive Environment
How have the following factors affected your ability to satisfy your customers’ demands? a. Rapid pace of technological changes Very little 1 2 3 4 5 Big Effect effect b. Increasing rate of new product Very little 1 2 3 4 5 Big Effect introduction or product obsolescence effect c. Increasing number of competitors Very little 1 2 3 4 5 Big Effect effect d. Changing demand pattern of your Very little 1 2 3 4 5 Big Effect customers effect e. Variety of product or service delivery Very little 1 2 3 4 5 Big Effect modes effect f. Government regulations Very little 1 2 3 4 5 Big Effect effect
302
Comments:
B.1.3 Forecasting your demand
a. We collaborate very closely with our Strongly 1 2 3 4 5 Strongly customers in order to develop our sales Disagree Agree forecast. b. Over the past five years our sales Strongly 1 2 3 4 5 Strongly forecasts have been relatively accurate. Disagree Agree c. We accept a wide range of order sizes Strongly 1 2 3 4 5 Strongly from our customers. Disagree Agree d. Our lead-time for responding to Strongly 1 2 3 4 5 Strongly customer orders is relatively short. Disagree Agree e. Relative to our competitors, we can Strongly 1 2 3 4 5 Strongly accept a wider range of orders for our Disagree Agree products.
Comments:
B.1.4 Measuring your delivery performance
a. Our capability of providing Poor 1 2 3 4 5 Excellent dependable delivery is ______b. Orders submitted to us are delivered Strongly 1 2 3 4 5 Strongly on time, as defined by the customer. Disagree Agree c. Our capability of providing the Poor 1 2 3 4 5 Excellent variety (mix) of products needed on-time is ______d. Our capability of delivering the Poor 1 2 3 4 5 Excellent correct quantity of products on-time is ______d. We respond with accurate Strongly 1 2 3 4 5 Strongly information to our customers’ inquiry Disagree Agree concerning an order
Comments:
303
B.1.5 Measuring your overall performance
Compared to your competitors, indicate your position on the following dimensions:
a. Market share Worse than 1 2 3 4 5 Much our better than competitors competitors b. Sales growth Worse than 1 2 3 4 5 Much our better than competitors our competitors c. Profitability (ROI) Worse than 1 2 3 4 5 Much our better than competitors our competitors d. Delivery reliability Worse than 1 2 3 4 5 Much our better than competitors our competitors e. Customer satisfaction Worse than 1 2 3 4 5 Much our better than competitors our competitors
Comments:
B.1.6 Marketing Strategy
a. Volume flexibility is an important Strongly 1 2 3 4 5 Strongly element of our marketing strategy Disagree Agree b. To what extent do the following Please indicate the impact of these factors on volume flexibility marketing strategy factors impact your using a 10-point scale (1 = little impact, 10 = significant impact) ability to be volume flexible? Factors Impact
Pricing policy ______Promotions ______Advertising ______Service ______Other (______) ______
304
Comments:
B.1.7 Business strategy decision-making
To what extent is the head of manufacturing/operations involved in the following business strategy decisions? a. Specifying the strategy of the No 1 2 3 4 5 Total business unit responsibility responsibility b. Strategic changes in manufacturing No 1 2 3 4 5 Total responsibility responsibility c. Strategy for growth No 1 2 3 4 5 Total responsibility responsibility d. Capital budget decisions No 1 2 3 4 5 Total responsibility responsibility e. Product or service decisions No 1 2 3 4 5 Total responsibility responsibility f. Product R&D decisions No 1 2 3 4 5 Total responsibility responsibility g. Outsourcing decisions No 1 2 3 4 5 Total responsibility responsibility h. Manufacturing/Operations managers Strongly 1 2 3 4 5 Strongly have a good understanding of how Agree Disagree company/divisional strategy is formed.
Comments:
305
B.1.8 Manufacturing Strategy
a. In your manufacturing strategy, which Please allocate 100 points to the following set of items: of the following factors are most Items Points important to you? Cost ______Quality ______Delivery ______Flexibility ______Other (______) ______b. Volume flexibility is an important Strongly 1 2 3 4 5 Strongly element of our manufacturing strategy. Disagree Agree c. Volume flexibility helps us to achieve Strongly 1 2 3 4 5 Strongly a competitive advantage. Disagree Agree d. Our volume flexibility strategy helps Strongly 1 2 3 4 5 Strongly us gain new customers and grow our Disagree Agree business. e. To what extent have the following Please indicate the causes or drivers of volume flexibility by using a factors driven the need for a volume 10-point scale (1 = not important, 10 = very important) flexibility strategy within the company? Factors driving Volume Flexibility Importance
1. Forecasting problems ______2. Short delivery time ______3. Need to be responsive ______4. Differences in each customer segment ______5. Changing customer demand patterns ______6. Increasing number of competitors ______6. Need to gain market share ______7. Need to maintain a core competency ______8. Improved product/process technology ______9. Shortage of production capacity ______10. Industry structure ______11. Inefficiencies in the supply chain network ______12. Others (______) ______
Comments:
306
B.1.9 Manufacturing Planning and Control
a. To what extend does your As a percent of your business, please indicate the importance of the manufacturing plant use the following following planning and control principles: planning and control principles? Items Percent Make-to-order ______Make-to-stock ______b. To what extend does your As a percent of value added, please indicate the importance of the manufacturing plant use the following following process layouts: product or process layouts? Items Percent
Assembly line production ______Batch production ______Job shop ______Manufacturing cells ______Continuous flow ______c. Production lines are dedicated to Strongly 1 2 3 4 5 Strongly specific products. Disagree Agree d. Inventory is built up to cover peak Strongly 1 2 3 4 5 Strongly demands. Disagree Agree e. Our plant operates at full capacity. Strongly 1 2 3 4 5 Strongly Disagree Agree f. This plant adapts well to unexpected Strongly 1 2 3 4 5 Strongly changes in the volume of orders for Disagree Agree existing products. g. To what extent is senior management Not 1 2 3 4 5 Very of this plant cooperative in responding to Cooperative cooperative unexpected volume changes? h. To what extent is quality affected by No quality 1 2 3 4 5 Significant unexpected changes in volume shifts? problems quality problems i. To what extent is cost affected by No change in 1 2 3 4 5 Significant unexpected changes in volume shifts? cost cost changes j. To what extent is Delivery No change in 1 2 3 4 5 Significant Performance (DP) affected by DP changes in unexpected changes in volume shifts DP k. How quickly does this plant respond Slow to 1 2 3 4 5 Quick to to changes in variety (mix) of products respond respond demanded by customers
Comments:
307
B.1.10 Workforce development and usage
a. Our workforce is an important source Strongly 1 2 3 4 5 Strongly of volume flexibility. Disagree Agree b. We cross-train our workforce to Strongly 1 2 3 4 5 Strongly improve our flexibility. Disagree Agree c. Team participation is a significant part Strongly 1 2 3 4 5 Strongly of employee performance evaluations at Disagree Agree our plant. d. Our skilled workforce gives us a Strongly 1 2 3 4 5 Strongly competitive advantage. Disagree Agree e. We empower our workers to make Strongly 1 2 3 4 5 Strongly timely decisions to achieve our goals. Disagree Agree f. We are able to significantly increase Strongly 1 2 3 4 5 Strongly or decrease our workforce to respond to Disagree Agree customer demands. g. We can increase or decrease the Strongly 1 2 3 4 5 Strongly number of shifts to respond to customer Disagree Agree demands.
Comments:
B.1.11 Equipment and technology
Indicate the degree to which your manufacturing plant uses the following a. Equipment used for high-speed or Not used 1 2 3 4 5 Used long runs extensively b. Equipment designed to support quick Not used 1 2 3 4 5 Used changeovers. extensively c. Computer-controlled or automated Not used 1 2 3 4 5 Used manufacturing equipment extensively d. Manually-controlled systems that rely Not used 1 2 3 4 5 Used primarily on labor extensively
Comments:
308
B.1.12 Networks and strategic alliances
a. Our vendors/suppliers replenish us on Strongly 1 2 3 4 5 Strongly a just-in-time basis Disagree Agree b. Our vendors/suppliers are certified Strongly 1 2 3 4 5 Strongly for quality Disagree Agree c. We use the best quality of materials Strongly 1 2 3 4 5 Strongly (within a given cost range) Disagree Agree d. We have long-term arrangements Strongly 1 2 3 4 5 Strongly with our vendors/suppliers Disagree Agree e. Our vendors/suppliers are very Strongly 1 2 3 4 5 Strongly responsive Disagree Agree f. To what extent does your network of No impact on 1 2 3 4 5 Significant vendors/suppliers enable you to be volume impact on volume flexible? flexibility volume flexibility g. How responsive are your logistics Please indicate the responsiveness of the following functions using functions to unexpected changes in a 10-point scale (1 = not responsive, 10 = very responsive) average order volume for existing Logistics Function Responsiveness products? Purchasing ______Inventory Control ______Warehousing ______Transportation ______h. We have long-term arrangements Strongly 1 2 3 4 5 Strongly with our distributors Disagree Agree i. Our distributors are very responsive in Strongly 1 2 3 4 5 Strongly helping us to fulfill customer orders Disagree Agree j. To what extent does your network of No impact on 1 2 3 4 5 Significant distributors enable you to be volume volume impact on flexible? flexibility volume flexibility k. To what extent does your network of No impact on 1 2 3 4 5 Significant manufacturing plants enable you to be volume impact on volume flexible? flexibility volume flexibility
Comments:
309
B.1.13 Short-term Sources of Volume Flexibility
A short-term period is defined as less than 3 months (an operating quarter) a. In the short-term, we can profitably Please select the range of possible output changes during the short- increase or decrease our output within term the following ranges: Range Check one ± 0 to 24% (small changes in output) ______± 25 to 50% (medium changes in output) ______± 51 to 100% (large changes in output) ______b. To remain profitable and support the Please select the range of probable changes in cost to support range of output achievable in item (a) output changes during the short run above, we estimate that our costs would Range Check one also change by the following ± 0 to 24% (small changes in cost) ______± 25 to 50% (medium changes in cost) ______± 51 to 100% (large changes in cost) ______
c. The key sources of our volume Please indicate the sources of your volume flexibility during the flexibility during the short-term are: short-run using a 10-point scale (1 = not important, 10 = very important) Sources of Volume Flexibility Importance 1. Inventory buffers ______2. Maintain more idle capacity ______3. Reassign workers ______4. Use more cross-trained workers ______5. Hire temporary workers ______6. Renegotiate delivery schedule ______7. Overtime ______8. Outsourcing ______9. Vendors/suppliers network ______10. Other (______) ______
Comments:
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B.1.14 Mid-term Sources of Volume Flexibility
The mid-term period is defined as 3 to 6 months (two operating quarters) a. In the mid-term, we can profitably Please select the range of possible output changes during the mid- increase or decrease our output within term the following ranges: Range Check one ± 0 to 24% (small changes in output) ______± 25 to 50% (medium changes in output) ______± 51 to 100% (large changes in output) ______b. To remain profitable and support the Please select the range of possible changes in cost to support output range of output achievable in item (a) changes during the mid-term above, we estimate that our costs would Range Check one also change by the following ± 0 to 24% (small changes in cost) ______± 25 to 50% (medium changes in cost) ______± 51 to 100% (large changes in cost) ______
c. The key sources of our volume Please indicate the sources of your volume flexibility during the flexibility during the mid-term are: mid-term using a 10-point scale (1 = not important, 10 = very important) Sources of Volume Flexibility Importance 1. Inventory buffers ______2. Maintain more idle capacity ______3. Reassign workers ______4. Use more cross-trained workers ______5. Hire temporary workers ______6. Renegotiate delivery schedule ______7. Overtime ______8. Outsourcing ______9. Vendors/suppliers network ______10. Purchase Manufacturing equipment ______11. Change manufacturing processes ______12. Other (______) ______
Comments:
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B.1.15 Long-term Sources of Volume Flexibility
The long-term period is defined as more than 6 months a. In the long-term, we can profitably Please select the range of possible output changes during the long increase or decrease our output within term the following ranges: Range Check one ± 0 to 24% (small changes in output) ______± 25 to 50% (medium changes in output) ______± 51 to 100% (large changes in output) ______b. To remain profitable and support the Please select the range of possible changes in cost to support output range of output achievable in item (a) changes during the long-run above, we estimate that our costs would Range Check one also change by the following: ± 0 to 24% (small changes in cost) ______± 25 to 50% (medium changes in cost) ______± 51 to 100% (large changes in cost) ______c. The key sources of our volume Please indicate the sources of your volume flexibility during the flexibility during the long-run are: long-run using a 10-point scale (1 = not important, 10 = very important) Sources of Volume Flexibility Importance 1. Inventory buffers ______2. Maintain more idle capacity ______3. Reassign workers ______4. Use more cross-trained workers ______5. Hire temporary workers ______6. Renegotiate delivery schedule ______7. Overtime ______8. Outsourcing ______9. Vendors/suppliers network ______10. Purchase Manufacturing equipment ______11. Change manufacturing processes ______12. Improve Forecasting ______13. Expand/reduce plant capacity ______14. Acquire additional plants ______15. Off-shore capacity ______
Comments:
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B.2. Case Study Narrative: Company A
B.2.1 Interview Data:
Date of interview: 9/23/99
Respondent: Vice President of Operations
Primary Product: Computer cables and connectors
Industry: Computer subassemblies and components
SIC: 357X
Average Number of Employees: 140
Average Annual Sales: $40M
B.2.2 Summary of Findings
We interviewed the operations manager at Company B to gather some
information on how this small company is able to achieve volume flexibility. We find
that the key dimensions of this company’s volume flexibility lie primarily in its labor
flexibility, its supplier networks, its production control system used to support its delivery
strategy, and its slack capacity.
B.2.3 Company Overview
This company is a manufacturer of standard and
custom computer cable products specializing in cable
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connectors, data switches, fiber optic multiplexers, twisted pair cables, network gateways
and surge protectors with noise filters. These products are sold to several customers
including resellers, OEMs and retail customers. Since its formation in 1985, the
company has grown steadily. Recent acquisitions include ASP Computer Products Inc.,
Pen Cabling Technology, and CTG International. The company employs 160 employees
with reported annual sales of $36M in fiscal year 1997. The company has operations at
four locations: Dayton, Ohio; San Jose, California; Salt Lake City, Utah; and Taiwan.
B.2.4 Business Environment
Influenced by the rapid pace of changes in the computer industry, the company’s
business environment is characterized by rapid pace of technological changes, increasing
rate of new product introduction, changing pattern of customer demands and an
increasing number of competitors. Of these factors, the increasing number of competitors
gives this company the most concern.
The cabling industry is filled with companies that build custom cable assemblies
for industry OEMs. Most of those suppliers have only one core capability, making them
attractive in very specific situations. There are the well-established ISO-certified assembly houses and the "garage shop" price leaders. There are the hungry newcomers looking to gain a foothold, and the industry powerhouses who command the highest prices for their vast experience. There are some that invest very little in technical resources and others that are burdened with the cost of an extensive arsenal of engineering personnel. For many, their focus is creating new cable designs quickly with complete prototyping, while others rely on their low cost manufacturing capability.
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B.2.5 Demand Forecasting
As is the case in the volatile computer industry where product life cycles are measured in months, demand forecasting is a significant challenge at the CTG plant in
Dayton, Ohio. Forecast for the past few years have either been over or short. The forecasts are developed internally by the sales and marketing team who collaborate very closely with their customers. These forecasts are projected for a six month planning horizon and updated weekly. A significant challenge in forecasting this demand is the range of order sizes that the company accepts from its customers. The range of orders the company accepts goes from a single order for a retail customer to a large order for a truckload of custom cables from other customers. In addition, demand patters have been changing recently due to their customers increasing concern about Y2K issues.
B.2.6 Delivery Performance
The company sees itself as providing a critical service to its customers who need the products to keep their computer systems running reliably. The average lead-time for an order is two days. However, most orders are shipped within 24 hours. Orders are tracked through their internal logistics system and most of the items are shipped via UPS or Federal Express. The company uses internal metrics to track delivery performance and they believe that their delivery performance gives them a competitive advantage.
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B.2.7 Overall Performance
The company’s goal is to do things bigger, better and faster while increasing
sales, and maintaining quality and timeliness. By remaining on the leading edge of the
market, CTG servers their customers as the cabling experts, balancing component
performance, reliability, and cost to best meet the needs of their valued customers.
Internally, the company is satisfied with its overall performance in meeting the demands
of the market through effective use of technology, people and ideas. When asked to
compare its performance to that of its competition, the company believes that they are
better than the competition in terms of sales growth, profitability, delivery reliability, and
customer satisfaction. However, market share growth is difficult to assess because of the
increasing number of competitors that range from small mom and pop operations to
larger manufacturers.
B.2.8 Business Strategy Decision-making
The internal business strategy decision-making process is a team-based iterative
process. At the core, the company relies on ideas and feedback from its employees.
There are also monthly meetings for senior and mid-level managers. In addition, the overall corporate strategy is reviewed quarterly. An interesting aspect of this process is that the operations manager believes that he is intimately involved in the business strategy decision-making process.
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B.2.9 Manufacturing Strategy
The manufacturing strategy is designed to support a business strategy built upon
service quality and delivery reliability. To satisfy a wide range of customer orders for its products within a relatively short lead-time (2 days to 3 months), the company believes that manufacturing flexibility is an essential component of their strategy. Specifically, the company believes that volume flexibility is an important element of its manufacturing strategy. The plant normally operates at 50-80 percent of its production capacity.
Therefore, the operations manager stated that they could increase or decrease the output of this company by 100% within a six-month timeframe. The major input required to achieve this increase would be an increase in the workforce by 50%. The other avenue for increasing their volume flexibility is through outsourcing. However, the company’s emphasis on maintaining internal knowledge and control could restrict any decision to outsource.
B.2.10 Production Control
The internal manufacturing planning and control system is based primarily on a make-to-order process. New orders are continually screened to determine how best to fulfill these orders. Depending on the volume, technical requirements, and the lead- time, the orders are filled using resources at one of the four locations. The plant in
Dayton, Ohio fills specialty items. Orders for large volumes are handled through the
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plant in Taiwan. The plant in Salt Lake City, Utah is ISO 9002 certified and handles
orders for OEM customers. At the Dayton plant, the actual manufacturing processes are
based primarily on manually-controlled systems with relatively low technology
(disconnect technology, termination technology, crimping, and soldering). More
sophisticated technology is available at the plants in Utah and Taiwan. The company
believes that it can easily incorporate demand variations (volume changes, product-mix,
and design changes) or equipment breakdowns because it maintains 20-50 percent slack capacity.
B.2.11 Supplier/Vendor Networks
Inventories of materials and supplies are maintained internally and orders are placed routinely with suppliers. Most of the supplies are purchased using the basic industry standards. In general, mil standards and acceptance sampling procedures are used to maintain quality standards. The company maintains long-term relationships with its suppliers and rates the performance of their suppliers very highly. By participating in industry standards groups like BCSI, ANSI X3T10, PCMCIA, and others, the company is able to stay abreast of new developments in connector and wire technology, and the evolution of standards governing the interconnect business. Also, by developing relationships with the elite computer hardware OEMs, they proactively participate in the industry's ongoing goal of producing more compact, robust, and affordable cabling interconnects. To further enhance its responsiveness, the company has teamed up with some of the most recognized distributors in the world such as Ingram Micro, Merisel Inc., and Tech Data.
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· Ingram Micro is the largest distributor of computer technology products and services in the world. They've gained the loyalty of more than 140,000 reseller customers in 130 countries, who rely on them for the latest technology and business-building services at competitive prices. Hardware and software manufacturers partner with them to sell their products and build market share. · Merisel, Inc., (NASDAQ:MSEL) is a leader in the distribution of computer hardware and software products. Based in El Segundo, Calif., Merisel is a Fortune 500 Company with 1998 sales of $4.6 billion. Merisel distributes a full line of 25,000 products from the industry's leading manufacturers to resellers throughout North America. · Tech Data Corporation (NASDAQ/NMS: TECD), founded in 1974, is a leading full-line distributor of technology products worldwide. The Fortune 500 company and its subsidiaries operate in over 30 countries, serving more than 100,000 resellers in the United States, Canada, the Caribbean, Latin America, Europe and the Middle East.
B.2.12 Labor
In a highly competitive business, the company believes that its workforce is key to success. Workforce flexibility is emphasized and encouraged. After having tried multiple shifts over the past few years, the company now maintains a single-shift operation. Overtime is used sparingly. The rationale given for the single shift and sparse use of overtime is based on the desire to balance customer demands on the one hand and the needs of the workers (in terms of job security and employee morale). Workers are continually cross-trained to improve their flexibility. The company has an internal training organization called the CTG University that provides training to all employees.
On two different visits, while sitting in the waiting room, I noticed that the even the receptionist had to take time out for training while others filled her position. In addition the company promotes and supports employee training through other local sources.
Employees are also encouraged to work in teams and rewarded for their effort.
Employees are also rewarded for their ideas and suggestions that are incorporated into the internal planning processes. The value that the company places on their employees is perhaps best understood by the senior managers concern that they maintain a no-layoff
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policy. They also attempt to promote many employees from within the company. Every
decision on the company’s business strategy must also consider the impact on the
employees.
B.2.13 Conclusions
After a close examination of the CTG plant in Dayton, Ohio, does this company
validate what we already know about the drivers and sources of volume flexibility? We
conclude affirmatively. This company is operating in the highly volatile computer
industry. Hyper competitive forces (D’Aveni, 1995) clearly drive the company to adopt a
volume flexible manufacturing strategy. In responding to this uncertainty, we see how
the company effectively uses its resources to become volume flexible. These findings
support the extant knowledge of the sources of volume flexibility. For example, we find
significant evidence of Cox’s (1989) four key sources for volume flexibility (vendor
networks, labor, production planning and control, and facilities and equipment). Clearly,
the company effectively meets its customer demands through efficient use of its
distribution network coupled with a responsive production system and significant slack
capacity (including off-shore production facilities). In addition, perhaps the key to this company’s volume flexibility lies in its effective use of labor through cross-training,
employee empowerment and rewards.
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B.3. Case Narrative: Company B
B.3.1 Interview Data:
Interview Dates Respondents 12/3/99 CEO 1/5/00 Director of Operations Maintenance Engineering Manager 1/6/00 V.P. of Sales Human Resources Manager V.P. of Marketing Financial Manager V.P. of Engineering CEO
Primary Product: Industrial/Environmental Filtration
Industry: Industrial and Commercial Fans and Blowers
SICs: 3559 and 3564
Average Number of Employees: 300
Average Annual Sales: $48M
B.3.2 Summary of Our First Meeting
Our initial meeting was held with the CEO of the company in order to gain support and commitment for the project. We began the meeting by sharing some benchmark data using 5 different measures of volume flexibility from our Compustat statistical analysis. This data suggested that small firms in SIC 3559 were more volume
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flexible that the larger firms were in this SIC. For SIC 3564, the data suggested that
larger firms were more volume flexible. We discussed some of the implications and
offered to redo the analysis with a larger data set and also to compute some measures
using actual financial data from UAS. The CEO indicated that we would have to sign a
confidential agreement.
In the second phase of this meeting, we explained that the focus of the case study
was to gain a deeper understanding of the drivers and sources of volume flexibility. The
CEO explained that the company was in the middle of several organizational changes.
The CEO himself had only been at the company for 4 months and the company was
undergoing some changes due to the recent acquisition by Clarcor. After much discussion, the CEO expressed some support for the project and he indicated that it may be beneficial to look at their forecasting mechanisms, cost structure, shift schedules, and some manufacturing processes. At the end of this meeting we agreed that detailed
interviews would be conducted with several representatives of the company over the 3-
day period of 5-7 January 2000.
B.3.3 The Plant
On 1/5/00, we began this case study by taking a 45-minute tour of the
manufacturing plant in order to be familiar with the products, manufacturing processes,
and logistical activities. The company manufacturers seven different types of products:
industrial air cleaning systems (SMOG-HOG® and DUST-CAT®), industrial air pollution
control systems (SMOG-HOG®), industrial dust collection systems (DUST-HOG®),
commercial air cleaning systems (SMOKEETER®), heat recovery ventilators (FRESH-X-
CHANGER®), electrostatic oil cleaning systems (KLEENTEX®), and electrostatic liquid
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coating systems (TOTALSTAT ®). The technologies that are used in these products include air handling and control systems, air filtration, electrostatic air precipitation, ferromagnetics, and electrostatic liquid applications.
During this tour, I was impressed with the variety of manufacturing processes,
materials, workmanship, and the extent of customizations that the company offers along
its product line. This plant utilizes state-of-the-art CAD systems and DNC machine tools
(turret and break press), various skilled craftsmen (e.g. machine operators, welders and electricians), various paint application systems, manual assembly, and packaging. I was also impressed by the variety of activities (incoming materials, manufacturing, warehousing, and shipping) that are accomplished within a relatively small space. Very little finished goods were kept inventory.
B.3.4 Assessing the Competitive Environment
In assessing the competitive environment, there were five factors that have a
significant impact on this business. First, except for TOTALSTAT ® the company’s
products are essentially capital equipment that are not used directly in the end-users production processes. This means that many customers view these products as discretionary items in their capital budgets; which presents significant challenges in demand forecasting. Second, one would think that government regulations through
OSHA and EPA would be driving sales in the business. However, because enforcement is sporadic from state to state, these regulations essentially drive awareness (not sales) for air cleaning and air pollution control. Third, there is a strong trend of market consolidations through acquisition; Clarcor recently acquired this company and their
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competitors are making similar acquisitions. Fourth, the company essentially competes in three main segments (industrial, commercial and the electrostatic spray systems segments). The nature of competition and the types of customers vary by market segment. For example, a competitor dominates the industrial segment and in order to effectively compete in this market segment, our case firm relies on niche-marketing opportunities that result in mass customization and product proliferation. Fifth, the company is adopting a more global focus with a market presence in Europe and emerging opportunities in Asia. Perhaps the biggest concern expressed about the competitive environment was the extent of customizations required to gain new customers when customer loyalty is sensitive to price.
In the industrial segment, the products are sold through a network of independent manufacturer’s representatives in North America, Asia, and Europe. In the commercial segment, the company uses a sales force that is organized by region. In the industrial segment, process engineers and manufacturing systems integrators are the main customers. Sales are highly influenced by technical requirements as specified by these customers. The company responds to these requirements using an in-house engineering staff and manufacturing expertise to assist in sales quotations.
In assessing the relative risks to the company’s products from substitutes and technical innovations; there were no clear threats on the horizon. The company has been in this business since the early 1970’s and they have experienced very little product obsolescence. The company’s core competence is in electrostatic precipitation and they have developed a competitive advantage based on application knowledge and manufacturing expertise.
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B.3.5 Demand Forecasting
Forecasting is done using a three-year business plan and a rolling twelve-month
planning horizon. However, all of the managers expressed serious concerns about the
effectiveness and reliability of the forecast. The VP of sales summarized the forecasting
challenge in three areas: seasonality of capital equipment, the variety of capital
budgeting approval processes that the customers must overcome, and the reliance on
manufacturing representatives for forecasting information in the industrial segment. The
VP of Marketing summarized the challenges in forecasting as being caused by limited direct presence in the industrial segment, reliance on sales leads in the customer segment, and the consolidation of the entire forecast into monthly and annual sales dollar targets rather than by product or process. The Operations managers expressed concern that the
inability to forecast essentially leads to a 3-4 week planning horizon that is difficult to
manage and execute efficiently.
B.3.6 Delivery Performance
Difficulties in forecasting lead to significant problems in delivery performance.
The internal metrics suggest that delivery performance has a 70% average success rate as measured by promise date versus actual ship date. Each of the managers gave a different perspective on the case of the delivery problems. The sales manager highlighted the problem and emphasized the difficulties in anticipating high-impact orders with short
delivery time frames. Engineering expressed concerns about the quotation process that
leads to customization without engineering input on the front-end. Incidentally, the
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company is currently implementing a plan to matrix engineers to the sales team. In
addition the company has also implemented an automated product configuration process
in order to shorten the internal planning and engineering lead-time. The marketing manager views the delivery problem from the perspective of inefficiencies in the internal order flow and information management processes. The operations managers view the delivery problem as being primarily caused by not enough lead-time given to operations to efficiently schedule and flow the jobs to meet customer demands. The result is that there are huge swings in plant output and inefficient used of resources to meet monthly and quarterly dollar targets. In order to track orders and respond to customer inquiries, the company uses an internal expediter who reports to the sales manager.
B.3.7 Overall Performance
The company has made some attempts to benchmark itself against the competition. In the $100M industrial segment, the company ranks second for the dust collection product; but, a competitor dominates 90% of the market. For the air pollution product, the company has strong brand recognition and ranks #1. The commercial segment is very competitive and there are no clear market leaders. In the electrostatic liquid application segment, there are three major players and the company ranks #1.
Overall, sales growth in this industry has been relatively flat. The company experienced a significant sales growth in 1998. But 1999 was a bad year for both sales and profitability. These financial results and the recent acquisition of this company by
Clarcor have resulted in significant leadership and operational changes in the company.
Customer satisfaction is not formally assessed. There have been some attempts to
survey customers using the warranty registration card but feedback is sporadic and many
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believe that it is too early in the life of the product to assess customer satisfaction.
However, all customer complaints are logged by type and incident and the managers review this data to improve their processes. In addition, there is a new Quality Assurance manager in operations who is now tasked with customer warranty and follow-ups.
B.3.8 Marketing Strategy
In assessing the marketing factors that impact volume flexibility there were several different perspectives from the managers. Sales believed that pricing had the most impact on volume flexibility particularly in the industrial segment. In addition, the sales manager also viewed the breadth of the company’s product offerings and the extent of customizations as have a significant impact on volume flexibility. The marketing manager viewed effective advertising and leads management as having the most significant impact on volume flexibility. The CEO viewed pricing as having the most impact on volume flexibility with promotions and service running a close second. The engineering manager viewed customizations as having the most impact on volume flexibility. However, his concern was that the cost accounting systems were not effective in capturing the costs of all resources used in customizing the products. The financial manager is relatively new and he has been tasked to improve the cost accounting system in order to provide real-time information to efficiently allocate resources.
Considering all these responses, the relative importance of the company’s marketing strategy on volume flexibility can be summarized as follows:
1. Pricing policy (price discounts impact order volume) 2. Customization (core competency)
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3. Advertising (in commercial segment) 4. Promotions (targeted to key customers) 5. Service reliability (not formally measured)
B.3.9 Business Strategy and Decision-Making
The involvement of Operations managers in business strategy and decision- making was not clearly evident. Perhaps the best indication of the problem is that the company does not have a VP of Operations. Instead, the company uses three key managers to run the manufacturing operations: operations manager, manufacturing engineering manager, and quality assurance manager. The operations managers do not believe that they have a real voice in developing company strategy. This perspective was validated by several of the other managers. Subsequent discussions revealed that the company previously had a VP of Operations but he was ineffective and was not replace when he departed. The CEO is aware of the problem and he has invited the operations managers to participate in key strategy meetings. However, the pressing operations problems limit the participation of operations managers in business strategy decision- making processes.
B.3.10 Manufacturing Strategy
We asked the managers to describe the relative importance of the competitive criteria used to assess their operations. In doing so, we asked them to allocate 100 points among the operation strategy competitive factors. The managers gave significantly different perspectives and the results are summarized in the following table.
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Operations Competitive CEO Ops-1 Ops-2 Sales Mkt Engr Avg. Criteria Rank 1. Delivery 30 30 30 20 25 40 29.17 2. Quality 30 30 30 30 30 20 28.33 3. Cost 20 30 30 25 20 30 25.83 4. Flexibility 20 10 10 25 25 10 16.67
Surprisingly, the operations managers gave little importance to manufacturing flexibility. Discussions revealed that the pressures to reduce cost, improve quality, and increase delivery reliability all have the daily attention of the operations managers.
Consequently the operations managers place little importance on flexibility. However, the CEO and other managers gave significantly more importance to manufacturing flexibility, in general, and volume flexibility in particular.
When we asked to rank order (using a 10-point scale) a list of factors that drive the need for volume flexibility, the managers gave different rankings and shared their rationale. The ranking of these factors are summarized and sorted based on the average responses from all the managers:
Drivers of Volume Flexibility sorted by average ranking of managers Drivers of volume Flexibility CEO Ops-1 Ops-2 Sales Mkt Engr Avg. Rank 1. Forecasting problems 8 9 10 9 9 7 8.67 2. Need to be responsive 9 8 9 10 9 7 8.67 3. Short delivery time 8 8 9 7 9 10 8.50 4. Differences in customer segments 8 10 10 8 7 5 8.00 5. Need to gain market share 8 6 10 7 7 7 7.50 6. Need to maintain a core competency 8 8 8 8 8 3 7.17 7. Changing customer demand patterns 8 9 8 5 5 7 7.00 8. Improved product/process tech 9 5 8 8 10 2 7.00 9. Shortage of production capacity 6 5 8 7 5 7 6.33 10. Inefficiencies in supply chain 6 7 8 6 3 3 5.50 11. Industry structure 6 5 8 5 5 1 5.00 12. Increasing number of customers 7 5 8 3 4 2 4.83
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B.3.11 Manufacturing Planning and Control
As a percent of the business, the manufacturing processes are primarily done in a make-to-order environment (90%) as opposed to make-to-stock (10%). This also reflects the level of effort on manufactured products as compared to spare parts. When considered as a percent of the value added, manufacturing is accomplished primarily in a job-shop environment (90%) as opposed to batch production of standard parts (10%).
Consequently, the plant responds very quickly to the variety of products that it produces and a lot of time is spent on customizing products for customers. However, when asked about the costs of customizations, the managers stated that the cost accounting systems did not provide adequate real-time information for decision-making. In further discussions with the new financial manager, he explained some of the problems with the system but his initial impression is that a 300% overhead burden on direct labor is not unusual in the manufacturing environment.
Two important sources of volume flexibility were evident: plant capacity and overtime. Most of the managers believe that the plant does not operate at full capacity.
Estimates of the current operating plant capacity ranges from 50-70%. The plant operates four days/week using two 10-hour shifts. The second shift is primarily used to operate the high-use manufacturing equipment and essential skills e.g. welding. If necessary, the second shift could be fully staffed to increase the plant’s output. However, staffing the shift with skilled labor could be problematic in a tight labor market. Also, a significant bottleneck at the plant is the automated turret machine. This machine is positioned at the front-end of the manufacturing process and is used to create different shapes in sheet
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metal for access, inlets, outlets etc. The operations manager believes that the core
competence of the plant is in sheet metal operations. In his view, any time lost on the
turret is time lost in the entire system.
The second major source of volume flexibility is the use of overtime. Since
forecasting is problematic and orders are erratic, the operations managers rely heavily on
overtime to meet monthly and quarterly sales targets. However, extended use of
overtime has its drawbacks and there are occasional problems with quality when the plant
uses too much overtime to meet surges in demand.
B.3.12 Workforce
In general, the managers view their workforce as an important source of volume
flexibility. However, there are no formal programs for cross training, skills-based
compensation, or team/group assignments. The plant operates in a non-union
environment and the operations manager reassigns workers to meet requirements as
necessary.
Discussions with the HR manager gave further insights into the challenges in
attracting and retaining a highly-skilled workforce. The availability of skilled labor in the
Cincinnati area is relatively low in a very tight labor market. The company competes by
offering very competitive compensation package to the employees, including a profit-
sharing plan. In addition, the company has an unofficial no-layoff policy and has never had a layoff. On the other hand, the company proceeds very cautiously in hiring additional skilled workers. Over the last three years, the company has hired an additional
35 workers and has experienced a 50% retention rate. Temporary workers are used for
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unskilled tasks in assembly and shipping. When asked about the need for cross-training,
the HR manager discussed the difficulties in administering a formal cross-training
program. In addition, he explained that they had tried a skills-based compensation program but that it was too costly and difficult to administer.
B.3.13 Networks and Strategic Alliances
The vendor/supplier network is another important source of volume flexibility.
The company has over 100 suppliers and they have maintained long-term relationships with several of these suppliers/vendors. Some of these vendors are certified for quality.
When asked to rank the importance of their logistics functions in relation to how responsive these functions are in supporting significant changes in output levels, the managers believe that their logistics functions need to be improved, primarily in the purchasing area. Here is a summary of how they view the responsiveness of their logistics functions to unexpected changes in output:
1. Transportation (products are transported using LTL contract carriers) 2. Warehousing (for materials and spare-parts) 3. Inventory control (primarily for commodities) 4. Purchasing (not very responsive)
Off-shore production capability is rarely used. The company does have two manufacturing plants in the U.K. and Germany. However, standard components are made in the US and shipped to the U.K. and Germany for local customizations.
A limited amount of finished goods are not kept in inventory to support the commercial segment. The company also uses its distribution network to respond to demand in the commercial segment. In the industrial segment, they rely heavily on their
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network of independent manufacturing representatives and they also use licensing
agreements to sell their manufactured products. However, as the company begins to
compete in the global market-place, they see the need for a more efficient distribution
network, especially for the service portion of the business.
B.3.14 Short-term Sources of Volume Flexibility
The operations managers were asked to consider a typical air quality product as a
basis for estimating how significantly they could change their output (up and down) in
the short run (less than 3 months). The operations managers believe that they could
profitably increase/decrease output in the ± 25% to 50% change in output.
Correspondingly, they anticipate small changes in the overhead cost since the overhead
burden is estimated at 50% of the cost of the product. When asked to rank order the key
sources of volume flexibility that would enable them to adjust their output in the short-
run, the managers gave different rankings and these sources are summarized and sorted
based upon the average ranking as follows:
Short-term Sources of Volume CEO Ops-1 Ops-2 Sales Mkt Engr Avg. Flexibility Rank 1. Reassign workers 8 9 7 7 10 10 8.50 2. Overtime 8 10 9 9 9 5 8.33 3. Cross-trained workers 8 9 7 7 7 10 8.00 4. Inventory buffers 6 8 8 8 3 8 6.83 5. Temporary workers 7 8 5 5 8 4 6.17 6. Renegotiate delivery schedule 7 5 6 6 6 3 5.50 7. Vendor/supplier networks 7 3 7 7 2 3 4.83 8. Idle capacity 6 8 2 2 5 5 4.67 9. Outsourcing 7 3 1 1 4 5 3.50
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These results were interesting in that it demonstrated that effective use of the
workforce is an important source of volume flexibility in the short run. I also followed
up with each manager to understand why outsourcing was viewed so low as a source of
volume flexibility. Discussions revealed that the company had tried outsourcing in the
past and had some poor results. The main problems are that the costs of outsourcing are
too high, too many difficulties in specifying requirements and volume levels to get good
prices in the contract, poor quality levels, and delivery reliability.
B.3.15 Mid-term Sources of Volume Flexibility
The mid-term was defined as a period of 3 to 6 months. The managers indicated
that output could be increased 25-50% with a corresponding 25-50% increase in cost.
The managers ranked the sources of this volume flexibility in the mid-term and these results are summarized by average rankings as follows:
Mid-term Sources of Volume Flexibility CEO Ops-1 Ops-2 Sales Mkt Engr Avg. Reassign workers 8 6 10 8 7 7.80 Cross-trained Workers 6 6 10 7 10 7.80 Inventory buffers 2 8 6 6 10 6.40 Temporary workers 3 8 9 6 5 6.20 Vendor/supplier networks 8 4 8 7 4 6.20 Overtime 6 8 5 5 5 5.80 Change manufacturing processes 8 6 9 3 3 5.80 Idle capacity 3 8 8 4 5 5.60 Purchase manufacturing equipment 7 6 9 2 3 5.40 Outsourcing 6 5 8 1 5 5.00 Renegotiate delivery schedule 6 3 5 3 3 4.00
I note here that even in the mid-term, outsourcing and renegotiating the delivery schedule are not viewed as important sources of volume flexibility.
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B.3.16 Long-term Sources of Volume Flexibility
The managers were asked to consider a typical manufactured product as a basis
for estimating how significantly they could change their output (up and down) in the long
run (more than 6 months). They believe that they could profitably increase/decrease
output in the range of ± 51% to 100% change in output. Correspondingly, they anticipate
changes in cost to be in the 25% to 50% range. When asked to rank order the key sources
of volume flexibility that would enable them to adjust their output in the long-run, the managers ranked these following manner:
Long-term Sources of Volume CEO Ops-1 Ops-2 Sales Mkt Engr Avg. Flexibility 1. Improve forecasting 8 8 10 8 8 8.40 2. Change manufacturing 8 7 10 7 9 processes 8.20 3. Expand/reduce plant capacity 6 8 9 8 7 7.60 4. Cross-trained Workers 8 5 10 7 7 7.40 5. Purchase manufacturing 8 6 10 5 8 equipment 7.40 6. Vendor/supplier networks 8 3 9 7 7 6.80 7. Reassign workers 8 5 8 8 3 6.40 8. Inventory buffers 2 8 2 5 5 4.40 9. Outsourcing 6 3 9 1 3 4.40 10. Acquire additional plants 6 1 10 1 4 4.40 11. Overtime 5 6 2 5 3 4.20 12. Idle capacity 2 6 2 4 2 3.20 13. Temporary workers 5 5 2 3 1 3.20 14. Off-shore capacity 6 1 2 1 3 2.60 15. Renegotiate delivery schedule 4 2 2 3 1 2.40
The responses detailing the long-term sources of volume flexibility at this plant are interesting. Clearly more reliable forecast would be of immense benefit. The operations managers expressed great concern about the short 3-4 week planning horizon.
The sales manager understands operations concern but he explains the realities of
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forecasting in the capital goods industries especially when your products are not an essential part of the customer’s production processes.
Another interesting result is that the delivery schedule is not really viewed as a source of volume flexibility even in the long-run. The operations manager explained that promised dates to customers are rarely, if ever changed. In fact, only customers can change their promised dates. The company can negotiate a new delivery date, but the promised date remains in the system. The CEO explained that on several occasions, the company had produced the product on time but the customer was not ready to accept it.
Closer collaboration with the customers throughout the delivery process could help in more efficient use of company resources. However, since the company operates on an end-of-month planning and execution cycle, it is difficult to communicate with customers and effectively use this information to improve internal processes.
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B.4. Case Narrative: Company C
B.4.1 Interview Data:
Interview Dates Respondents 12/7/99 Vice President of Operations and Engineering Owner/Manager Operations Quality/Service Manager 12/15/99 Vice President of Marketing
Primary Product: Industrial Screeners and Separators
Industry: General Industrial Machinery and Equipment
SICs: 3559, 3569
Average Number of Employees: 120
Average Annual Sales: $17M
B.4.2 The Plant
We began this case study by taking a 45-minute tour of the manufacturing plant in
order to be familiar with the products, manufacturing processes, and logistical activities of this company. The company manufacturers three types of products: normal capacity screeners/separators, high capacity screeners, and particle analyzers. The principal product produced at this plant is a series of over 100 industrial screeners or separators.
These products are used for sifting and screening in a wide range of applications
including chemical and food processing, petrochemicals, agribusiness, minerals, and
primary metal processing. For each of these applications, the products are customized to
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customer specifications based on the input and output feed rate, size of the screening
area, type of screening materials, number of screening levels required (up to five),
temperature requirements, and, commercial sanitary designs. The screeners process
hundreds of materials, such as fertilizer, coffee, cereals, dextrose, roofing granules,
pharmaceuticals, sugar, salt, and grains.
During this tour, I was impressed with the materials used, the workmanship, the
variety of products and the extent of customizations that the company offers along its
product line. This plant utilizes state-of-the-art DNC machine tools, laser cutting equipment and CAD systems. I was also impressed by the relatively low levels of raw material inventories and work-in-process inventories. Finished goods were not kept inventory. There were also separate inventories of spear parts that are used to service installed equipment at customer sites.
B.4.3 Volume Flexibility Benchmark Data
We began the meeting by sharing some benchmark data using 5 different measures of volume flexibility from our Compustat statistical analysis. This data suggested that small firms in SICs 3559 and 3569 were more volume flexible that the larger firms were. We discussed some of the implications of these findings and the operations managers explained the importance they place on volume flexibility as a component of their overall agile manufacturing strategy. We then offered to redo the benchmark analysis with a larger data set and also to compute some measures using actual financial data from the company.
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In the second phase of the meeting, we explained that the focus of the case study
was to gain a deeper understanding of the drivers and sources of volume flexibility. We
agreed that the case study questionnaire was perhaps the best way to organize our
discussion. Therefore, the following information was gathered from our discussions of
the case study protocol instrument.
B.4.4 Assessing the Competitive Environment
In the first meeting with the Operations managers, their responses to the case
survey questions suggest that the company is in a relatively stable market. They
experience very little impact of technology on the manufacturing side of the business.
However they have made recent investments in LASER cutting equipment and CADD
systems for engineering designs. The actual products are also relatively stable and there
is little obsolescence experienced. The company believes that their competitive
advantage is derived from three factors: (1) their experience (150 years); (2) their long-
term service capability; and (3) their ability to customize products for systems integrators
and manufacturing process designers. The biggest concern was expressed about the
increasing number of competitors that the company encounters as this business takes on a
more global focus.
The V.P. of Marketing provided a much clearer picture of the competitive environment. The company competes in five market categories dictated by application: chemical, plastics, food, mineral, and agribusiness. Each of these application areas is divided into approximately 40 different subcategories. For example, food can be divided into sugar, flour, salt, etc. The markets are generally divided into two geographical
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regions (North and South of Cincinnati, Ohio). The products are sold through a network
of 150 independent manufacturer’s representatives in North America, Latin America,
Asia, and Europe.
Process engineers and manufacturing systems integrators are the main customers
in each of the application areas. Sales are highly influenced by technical requirements as
specified by these customers. The company responds to these requirements using an in-
house engineering staff and manufacturing expertise to assist in sales quotations.
In assessing the competitive environment, the V.P. of Marketing saw three factors
as having the most impact on the competitive environment. First, as the company takes
on a more global focus, they are encountering many more competitors. Second, brand
loyalty is decreasing in importance and customers are placing more emphasis price and
on meeting specification requirements. Third, the systems integrators are playing an
increasingly important role as a middleman between the company and the ultimate end-
users of the products. The V.P. of marketing also agreed that technological substitutes,
product obsolescence, and government regulations had little impact on the competitive
environment.
B.4.5 Demand Forecasting
Forecasting is done on two different time horizons (12-month and 3-yeay business
plan). The VP of Operations and the Quality/Service manager both expressed some
concern that it is rather difficult to forecast their products. Their basic perspective is that
forecasts are either lucky or wrong. We agreed that I should follow-up with the VP of
Marketing and a separate interview was arranged for this purpose. However, from an
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operations perspective, the company is able to achieve its sales forecasts within +/-
$100K. However, their concerned was more focused on trying to control demand for manufacturing products over a 7-14 week horizon and for spear parts over a 2-3 week horizon respectively. Relative to their competitors, the operations managers believe that they normally accept a wider range of orders for their products.
In my follow-up meeting with the V.P. of Marketing, he also expressed his concerns about the difficulties in trying to forecast demand. The regional marketing managers collaborate very closely with the 150 manufacturing representatives to develop a 3-year business plan and a rolling 12-month sales forecast. The 12-month rolling forecasts are updated on a 30, 60, and 90-day horizon. At the aggregate level, the company meets its forecast targets. However, the main challenge is in forecasting the mix of products required. Evidence of this problem can be seen in the case of screeners of the size of over 100 SF, (referred to as MegaTex). In past years, the company has sold a relatively high volume of these products; however, this current year, the company will ship less than 10 of these products.
Product proliferation has been a double-edged sword. On the one hand, the company derives a significant competitive advantage from its application knowledge and its ability to customize products. On the other hand, as product offerings proliferate, it has a negative effect on forecast accuracy with corresponding effects on the use of resources in engineering and manufacturing.
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B.4.6 Delivery Performance
Overall, the operations managers express some concern about their delivery
performance. Delivery performance was acceptable for spare parts. However, their
internal metrics suggest a 35% on-time delivery performance rate for manufactured
products. The complicating factor is the extent of customization that is done of each type
of screener. Customization causes delivery problems in both the engineering and the
manufacturing phases. However, they believe that for many of their customers, the
screeners represent a small part of a new manufacturing process and there are usually
delays in other parts of the processes that ultimately give more realistic lead-times for
their products.
The V.P. of Marketing expressed similar concerns about delivery performance.
The goal of the company’s internal metric is for 90% delivery performance. The actual
delivery performance is approximately 35-50% on-time delivery. Three bottlenecks are at the core of these problems. For example, in a normal price quotation of 10-14 weeks for a typical screening product, the allocations for order processing, engineering, and manufacturing can be 2 weeks, 6 weeks, and 6 weeks respectively. The first bottleneck is actually at the customer end and it overlaps the front end of the quatoation cycle. For example, any delays in the approval process at the customer’s end can eat into the remaining cycle time for delivery performance. The other bottleneck is in engineering.
While investments in CADD systems have helped to reduce engineering cycle time, the extent of customizations offered, makes engineering a bottleneck process. Finally, the customization processes in manufacturing can also be a bottleneck. Delays can occur
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either because of technical process complications or engineering design changes to meet the product specifications.
B.4.7 Overall Performance
The company has made some attempts to benchmark itself against the competition. In each of the five major application markets (chemicals, plastics, food, minerals, and agribusiness), the company’s dominant position is #2, #1, #1, #4, and #1 respectively. Compared to 35 major competitors, the company ranks 2nd in customer brand awareness (35%) compared to the other major competitor (45%). However the brand recognition for the rest of the competitors falls to 15%. Therefore, the company is in a very competitive position.
The company believes that sales growth in this industry has been relatively flat.
However, a closely held internal assessment of the market suggests that they are growing relatively faster than the competition. There is some indication that analysis of the financial data would suggest that actual sales have been relatively flat in the last few years. The cause of these problems is uncertain. However there was some discussion about new foreign competitors in the marketplace. Despite these problems, the company rates itself much better than the competition on overall profitability.
Customer satisfaction performance is difficult to assess objectively. Despite the delivery reliability problems, the company believes that they score well on customer satisfaction measures. They have used customer satisfaction surveys but the return rate is low and they consider these to be unreliable. The company is considering the use of customer audits and focus interviews to improve the their customer satisfaction measures.
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B.4.8 Marketing Strategy
In our discussions with the operations representatives of the company, we sought the operations perspective on how the company’s marketing strategy supports their manufacturing flexibility. Of all the factors involved, pricing flexibility (discounts), service reliability, and applications knowledge in custom designs appear to have the most impact on manufacturing flexibility. Of these three influential marketing factors, applications knowledge was perceived by operations as the single marketing factor that improved the company’s competitive position the most.
The V.P. of Marketing also painted a similar picture of the company’s marketing strategy. When asked to rank order the marketing factors that impact volume flexibility, the following rankings were provided:
6. Applications knowledge (core competency) 7. Pricing policy (price discounts can impact order volume) 8. Promotions (targeted to key customers) 9. Service reliability (recognized through customer audits) 10. Advertising (reduced emphasis verus 10 years ago)
B.4.9 Business Strategy and Decision-Making
The involvement of Operations managers in business strategy and decision- making was clearly evident. In this regard, the ownership and organizational structure of the company gave the operations personnel a good overall perspective of the business strategy decisions-making process. In fact, the V.P. of Operations is an owner-manager.
Therefore, in this company, the operations managers are highly involved in the business
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strategy and capital resources decisions. With this business strategy insight, the
operations manager explained the need to implement an agile manufacturing strategy.
Perhaps one area that still needs to be further investigated is the role that operations,
engineering and marketing play in product R&D decisions. This is important because the
operations managers believe that a lot of engineering and manufacturing time is spent on
customizing products.
In our interview with the Marketing manager, we asked him to validate the
statements made by operations concerning their involvement in strategic decision-
making. The V.P. of Marketing is also highly involved in the strategic decision-making
process and he validated operations involvement in these processes.
B.4.10 Manufacturing Strategy
When asked to rank order the manufacturing competitive factors of cost, quality,
delivery and flexibility, the operations managers gave the highest ranking to cost and
quality. Flexibility was viewed as an indirect effect that occurs when actions were taken to reduce cost and improve quality. However, the operations managers still believed that volume flexibility gave them an important competitive capability. Volume flexibility
allows them to accept a wide range of orders for their products. Volume flexibility also
allows them to respond to customer demands on a relatively short lead-time of 6-12
weeks.
The V.P. of marketing gave a slightly different perspective by explicitly placing
more emphasis on flexibility. Using a 100 points allocation scale, he stated that
flexibility was twice as important as cost, quality or delivery. He also agreed that volume
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flexibility was important but that the bigger challenge for the company was in the mix of
products needed to meet changing customer demand.
When we asked the operations managers to rank order a list of factors that drive
the need for volume flexibility, the list in order of importance emerged as follows:
1. Differences in customer segments (5 markets and approx. 40 segments) 2. Need to maintain a core competency (application knowledge, engineering design, and in-house manufacturing capability) 3. Inefficiencies in the vendor/supplier network 4. Short delivery time (viewed as an important driver of how engineering and manufacturing resources are used) 5. Increasing number of competitors (increasing number of competitors from the far east) 6. Need to be responsive (this level of importance is surprising because actual delivery reliability is relatively low) 7. Industry structure (the important role that systems integrators and manufacturing engineering companies play in driving demand) 8. Changing customer demand patterns (relatively inelastic) 9. Production capacity (viewed as a source of flexibility in the short run) 10. Improved product/process technology (viewed as a source of flexibility e.g. recent investments in LASER technology) 11. Forecasting (not much emphasis is placed on the forecast for the manufactured products; parts forecasts are more reliable) 12. Need to gain marker share (not viewed as a driver for flexibility)
When we asked the marketing manager to rank order the list of factors that drive the need for volume flexibility, he provided the following rankings:
1. Differences in customer segments (5 markets and approx. 40 segments) 2. Need to maintain a core competency (application knowledge, engineering design, and in-house manufacturing capability) 3. Need to gain marker share (driver for flexibility) 4. Increasing number of competitors (increasing number of competitors from the far east) 5. Changing customer demand patterns (no brand loyalty and pressure for pricing discounts ) 6. Need to be responsive (volume flexibility should help reduce delivery reliability) 7. Industry structure (the important role that systems integrators and manufacturing engineering companies play in driving demand)
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8. Short delivery time (viewed as an important driver of how engineering and manufacturing resources are used) 9. Inefficiencies in the vendor/supplier network 10. Production capacity (viewed as a source of flexibility in the short run) 11. Improved product/process technology (viewed as a source of flexibility e.g. recent investments in LASER technology) 12. Forecasting (not much emphasis is placed on the forecast for the manufactured products; parts forecasts are more reliable)
B.4.11 Manufacturing Planning and Control
As a percent of the business, the manufacturing processes are primarily done in a make-to-order environment (80 %) as opposed to make-to-stock (20%). This also reflects the level of effort on manufactured products as compared to spare parts. When considered as a percent of the value added, manufacturing is accomplished primarily in a job-shop environment (90%) as opposed to batch production of standard parts (10%).
Consequently, the plant responds very quickly to the variety of products that it produces and a lot of time is spent on customizing products for customers. However, when asked about the relatively high costs of customizations, the managers believe that the cost impacts were minor and that profit margins were good on these custom products and in addition, they have a competitive advantage with their knowledge and manufacturing capabilities. In further discussions about the cost allocation system, we understand that the overhead burden is approximately 50%. The implications of this high cost structure are that it ultimately drives the need for more manufacturing flexibility. The operations managers are currently underscoring this need in their desire to adopt a more agile manufacturing system. In addition, to get a better handle on their costs, the company is currently implementing a new computerized cost management system.
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Two important sources of volume flexibility were evident: plant capacity and
outsourcing through vendors. The operations managers believe that the plant currently
operates at 45-60% capacity. There are currently two shifts (8 a.m. to 5 p.m. and 5 p.m.
to 12 a.m.). The first shift represents 70% of the output and the 2nd shift represents 30%.
The managers believe that they could easily surge to a third shift with a skeleton crew.
However, staffing the shift with skilled labor could be problematic in a tight labor
market). The second major source of volume flexibility is ability to outsource products
through a network of fabrication vendors. The ability to surge with this option is limited
by the technical capability of the vendors along with the need for training and quality
compliance. Despite these potential problems, outsourcing through vendors is viewed as
a core capability and the company has continually fought at union bargaining agreements
to retain this flexibility option.
When we asked how quality at the plant is affected by major shifts in volume, the
operations managers gave anecdotal evidence that suggests that the quality level is not
adversely impacted when output levels change significantly. However, delivery
reliability is currently a challenge and this can be problematic if there are major shifts in
output levels.
B.4.12 Workforce
In general, the operations managers view their workforce as an important source
of volume flexibility. There is currently an effort to cross-train workers to improve their
flexibility. In fact, a major concern is the engineering design time that is consumed in the delivery process. The managers place a premium on any training that will help improve
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the engineering-manufacturing hand-off. Of course, in a union-shop environment, the company is somewhat limited in the amount of cross-training that it can accomplish. In addition, there are skill certification requirements and other technical concerns that limit their ability to cross-train more workers (e.g. in welding). The company also uses 26 different Continuous Improvement (C&I) teams to help streamline their processes and these teams have been in existence for 3 years. In fact, they have recently adopted a buyer-planner process that empowers the workers to improve the purchasing process to support their manufacturing efforts. Finally, incentive plans are used to motivate and reward workers for improving their productivity.
B.4.13 Networks and Strategic Alliances
The vendor/supplier network is another important source of volume flexibility.
The company has over 600 suppliers and they have maintained long-term relationships
(over 75 years) with several of these suppliers/vendors. Some of these vendors are certified for quality.
When asked to rank order the importance of their logistics functions in relation to how responsive to these functions are in supporting significant changes in output levels, the managers ranked the logistics functions as follows:
5. Transportation (products are transported using LTL contract carriers) 6. Warehousing (for spare-parts) 7. Purchasing (now decentralized through their buyer-planners) 8. Inventory control (primarily for commodities)
Off-shore production capability is rarely used. However, the company does have some agreements with manufacturing plants in Canada.
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Because finished goods are not kept in inventory, the company does not use the traditional type of distribution network. They rely heavily on their network of independent manufacturing representatives and they also use licensing agreements to sell their manufactured products. However, as the company begins to compete in the global market-place, they see the need for a distribution network, especially for the service part of the business. In fact, they have recently used a distributor to get access to the market in Mexico.
B.4.14 Short-term Sources of Volume Flexibility
The operations managers were asked to consider a typical screener product as a basis for estimating how significantly they could change their output (up and down) in the short run (less than 3 months). They believe that they could profitably increase/decrease output in the ± 25% to 50% change in output. Correspondingly, they anticipate small changes in the overhead cost since the overhead burden is estimated at
50% of the cost of the product. However, variable costs could change in the ± 0% to
25% range.
When asked to rank order the key sources of volume flexibility that would enable them to adjust their output in the short-run, the managers ranked these sources in the following manner:
1. Use current slack capacity (plant operates at 45-60% capacity) 2. Outsourcing (through their current set of fabrication vendors) 3. Supplier networks (12 key suppliers can respond efficiently in the short-run) 4. Overtime (negotiated agreements with the union require that a certain level of requirements be given to the workers before outsourcing) 5. Inventory buffers (this is low because finished goods are not kept in inventory) 6. Reassign workers
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7. Cross-train workers 8. Absorb the increased idle capacity (when output levels decrease) 9. Renegotiate their delivery schedule 10. Hire temporary workers (temporary workers cannot be used efficiently due to the skills and training required)
B.4.15 Mid-term Sources of Volume Flexibility
The mid-term was defined as a period of 3 to 6 months. However, the operations managers saw no real difference between the short-run and the mid-term capabilities and sources of volume flexibility.
B.4.16 Long-term Sources of Volume Flexibility
The operations managers were asked to consider a typical manufactured product as a basis for estimating how significantly they could change their output (up and down) in the long run (more than 6 months). They believe that they could profitably increase/decrease output in the range of ± 51% to 100% change in output.
Correspondingly, they anticipate small changes in the overhead cost since the current overhead burden is estimated at 50% of the cost of the product. However, variable costs could change in the ± 25% to 50% range.
When asked to rank order the key sources of volume flexibility that would enable them to adjust their output in the long-run, the managers ranked these following manner:
1. Use the 10 short-term sources of volume flexibility 2. Use more vendors (certification and training) 3. Purchase additional equipment 4. Streamline manufacturing processes (consider more standardization) 5. Off-shore production capacity (not really desired by this company) 6. Improve forecasting
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C. Supporting Information for Chapter 5
C.1. Correlation Matrix
D1 D2 D3 D4 D5 D6 IM1 IM2 IM3 VF1 VF2 VF3 VF4 ST1 ST2 ST3 ST4 ST5 ST6 LT1 LT2 LT3 LT4 LT5 LT6 P1 P2 P3 P4
D1 1.0
D2 0.1 1.0
D3 0.0 0.2 1.0
D4 0.1 0.1 0.1 1.0
D5 0.1 -0.1 0.1 -0.1 1.0
D6 0.0 0.2 0.1 0.2 -0.2 1.0
IM1 0.0 0.2 0.1 0.1 0.0 0.4 1.0
IM2 -0.1 0.2 0.1 0.1 0.0 0.5 0.7 1.0
IM3 0.0 0.1 0.1 0.1 0.0 0.6 0.7 0.7 1.0
VF1 -0.2 0.1 0.2 0.1 0.0 0.2 0.2 0.4 0.3 1.0
VF2 0.0 0.0 0.1 0.0 -0.1 0.2 0.2 0.3 0.3 0.4 1.0
VF3 0.0 0.0 0.1 0.0 -0.1 0.1 0.1 0.2 0.1 0.3 0.3 1.0
VF4 -0.1 0.0 0.1 -0.2 -0.1 0.0 0.2 0.3 0.1 0.4 0.4 0.4 1.0
ST1 0.1 0.0 0.1 0.1 0.0 0.1 0.0 0.0 0.0 0.2 0.0 0.0 0.1 1.0
ST2 -0.1 -0.1 0.1 0.0 0.0 0.0 0.1 0.1 0.0 0.1 0.3 0.2 0.3 0.0 1.0
ST3 0.1 0.0 0.1 0.1 0.1 0.0 0.0 -0.1 -0.1 0.1 -0.1 -0.3 -0.2 0.1 0.0 1.0
ST4 -0.1 0.0 0.0 0.0 0.0 0.0 0.2 0.2 0.1 0.2 0.0 0.1 0.2 0.0 0.1 0.1 1.0
ST5 0.1 0.1 0.0 0.1 0.1 0.2 0.2 0.2 0.2 0.2 0.1 0.2 0.2 0.1 0.1 0.1 0.3 1.0
ST6 0.2 0.1 -0.1 0.1 -0.1 0.1 0.0 0.0 0.0 -0.2 -0.1 0.0 -0.2 0.0 -0.1 0.0 0.1 0.3 1.0
LT1 0.0 0.2 0.0 0.1 0.1 -0.1 0.1 0.1 0.1 0.1 0.0 0.2 0.1 0.0 0.0 0.0 0.2 0.1 0.1 1.0
LT2 -0.1 0.1 0.1 -0.1 0.2 -0.2 0.1 0.2 -0.1 0.1 0.1 0.2 0.3 0.0 0.1 -0.1 0.2 0.0 -0.1 0.3 1.0
LT3 -0.1 0.1 0.0 0.1 0.0 0.1 0.1 0.2 0.2 0.2 0.2 0.1 0.2 0.2 0.1 -0.1 0.0 0.1 0.0 0.2 0.4 1.0
LT4 0.1 0.1 -0.1 0.1 -0.1 0.0 0.0 -0.1 -0.2 -0.1 0.0 0.0 -0.1 0.1 0.0 -0.1 0.2 0.1 0.6 0.2 0.2 0.0 1.0
LT5 -0.1 0.0 0.0 0.1 -0.1 0.1 0.1 0.1 0.1 0.2 0.1 0.1 0.0 0.0 0.1 0.1 0.2 0.3 0.4 0.1 0.1 0.2 0.2 1.0
LT6 0.0 0.1 -0.1 0.0 -0.1 0.0 0.1 0.1 0.1 -0.1 0.0 0.0 0.1 0.1 0.0 0.0 0.2 0.3 0.5 0.2 0.2 0.2 0.5 0.4 1.0
P1 -0.2 0.0 0.1 -0.1 0.0 0.1 0.1 0.2 0.2 0.3 0.2 0.1 0.3 0.1 0.1 -0.2 0.0 0.0 -0.1 0.0 0.2 0.2 0.0 0.0 0.0 1.0
P2 -0.1 0.0 0.0 -0.1 -0.2 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 -0.1 0.1 -0.1 0.1 0.1 0.1 0.1 0.0 0.1 0.0 0.1 0.2 0.1 1.0
P3 -0.2 0.0 0.1 -0.2 -0.1 0.1 0.2 0.1 0.1 0.1 0.2 0.1 0.1 -0.1 0.1 -0.1 0.1 0.0 -0.1 0.1 0.1 0.2 -0.1 0.1 0.1 0.2 0.5 1.0
P4 -0.1 -0.1 0.1 -0.1 -0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.1 -0.1 0.2 0.0 0.1 0.1 -0.1 0.1 0.1 0.1 0.0 0.2 0.0 0.3 0.5 0.8 1.0
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C.2. Factor Analysis Results
Individual Cumulative
Factors Items Factor Description Eigenvalue Percent Percent
1 P2, P3, and P4 Performance 2.153 16.23 16.23
2 LT4, LT5, LT6 and ST6 External Sources 2.265 17.08 33.31
3 IMP1, IMP2, IMP3, and D6 Importance 2.826 21.31 54.62
4 VF1, VF2, VF3, and VF4 VF Capability 1.980 14.93 69.55
5 LT1, and LT2 LT Internal Sources 1.160 8.75 78.30
6 ST3 ST Internal Source 0.891 6.72 85.02
7 LT2, and LT3 LT Internal Sources 1.016 7.66 92.68
8 D1 Driver of VF 0.896 6.75 99.43
353
Using a Varimax factor rotation, the eight eigenvectors are summarized as follows:
Variables Factor1 Factor2 Factor3 Factor4 Factor5 Factor6 Factor7 Factor8 D1 0.085 -0.147 -0.088 0.056 0.063 0.069 0.091 -0.432 D2 -0.093 -0.136 -0.118 0.002 0.207 -0.091 0.232 -0.227 D3 -0.074 0.079 -0.101 -0.055 0.194 0.124 0.155 -0.183 D4 -0.020 -0.185 -0.125 -0.003 0.074 0.140 0.235 -0.220 D5 0.061 0.022 -0.067 -0.158 0.321 0.045 -0.219 -0.017 D6 -0.222 -0.121 -0.290 0.227 -0.102 0.025 0.158 -0.049 IMP1 -0.304 -0.116 -0.228 0.111 0.124 -0.096 -0.238 0.019 IMP2 -0.362 -0.067 -0.285 0.018 0.067 -0.195 -0.175 0.148 IMP3 -0.321 -0.089 -0.374 0.200 0.045 -0.135 -0.084 0.083 VF1 -0.264 0.105 -0.083 -0.203 -0.019 0.347 0.094 0.001 VF2 -0.254 0.116 -0.023 -0.060 -0.230 0.110 0.272 -0.054 VF3 -0.215 0.076 0.046 -0.206 -0.334 -0.034 -0.099 -0.452 VF4 -0.237 0.140 0.038 -0.394 -0.235 0.096 -0.122 0.067 ST1 -0.033 -0.067 -0.026 -0.101 0.024 0.163 0.335 0.053 ST2 -0.114 0.099 0.097 -0.068 -0.142 0.211 -0.025 -0.010 ST3 0.068 -0.103 -0.046 0.072 0.349 0.557 0.077 0.135 ST4 -0.145 -0.137 0.140 -0.126 0.127 0.207 -0.438 0.023 ST5 -0.178 -0.221 0.035 -0.076 -0.026 0.298 -0.163 -0.130 ST6 -0.018 -0.470 0.190 0.135 -0.200 -0.059 -0.010 -0.057 LT1 -0.109 -0.103 0.148 -0.171 0.335 -0.182 -0.080 -0.372 LT2 -0.153 0.046 0.203 -0.409 0.381 -0.248 0.071 0.089 LT3 -0.185 -0.024 0.059 -0.143 0.143 -0.177 0.441 0.156 LT4 -0.010 -0.366 0.255 -0.077 -0.079 -0.140 0.083 -0.113 LT5 -0.164 -0.245 0.192 0.027 -0.017 0.238 0.023 0.240 LT6 -0.132 -0.386 0.262 0.024 -0.051 -0.077 0.067 0.282 P1 -0.177 0.146 0.037 -0.104 -0.088 -0.088 0.170 0.184 P2 -0.204 0.109 0.248 0.229 -0.067 -0.006 -0.016 -0.110 P3 -0.224 0.266 0.313 0.385 0.205 -0.028 0.015 0.007 P4 -0.232 0.236 0.337 0.355 0.122 0.088 0.011 -0.164
354
C.3. ANOVA Results
Response VFLEX_A (VF1- VF4) vs Size (DD2)
Expected Mean Squares Section Source Term Denominator Expected Term DF Fixed? Term Mean Square A: DD2 2 Yes S S+sA S 132 No S Note: Expected Mean Squares are for the balanced cell-frequency case.
Analysis of Variance Table Source Sum of Mean Prob Power Term DF Squares Square F-Ratio Level (Alpha=0.05) A: DD2 Size($M) 2 1.050321 0.5251604 0.69 0.504482 0.163962 S 132 100.7895 0.7635568 Total (Adjusted) 134 101.8398 Total 135 * Term significant at alpha = 0.05
Means and Effects Section Standard Term Count Mean Error Effect All 135 3.112963 3.089741 A: DD2 Size($M) 1 31 2.951613 0.1569422 -0.1381278 2 31 3.153226 0.1569422 6.348505E-02 3 73 3.164384 0.1022726 7.464281E-02
Plots Section
Means of VFLEX_A (VF1- VF4)
6.00
4.50
3.00
1.50 VFLEX_A (VF1- VF4)
0.00 1 2 3 DD2 Size($M)
355
Response ST_A (ST4, ST5) vs Size (DD2)
Expected Mean Squares Section Source Term Denominator Expected Term DF Fixed? Term Mean Square A: DD2 2 Yes S S+sA S 132 No S Note: Expected Mean Squares are for the balanced cell-frequency case.
Analysis of Variance Table Source Sum of Mean Prob Power Term DF Squares Square F-Ratio Level (Alpha=0.05) A: DD2 Size($M) 2 6.30028 3.15014 3.56 0.031318* 0.652229 S 132 116.9331 0.8858564 Total (Adjusted) 134 123.2333 Total 135 * Term significant at alpha = 0.05
Means and Effects Section Standard Term Count Mean Error Effect All 135 2.955555 2.876712 A: DD2 Size($M) 1 31 2.596774 0.1690444 -0.2799381 2 31 2.903226 0.1690444 2.651348E-02 3 73 3.130137 0.1101591 0.2534246
Plots Section
Means of ST_A (ST4, ST5)
6.00
4.50
3.00
ST_A (ST4, ST5) 1.50
0.00 1 2 3 DD2 Size($M)
Bonferroni (All-Pairwise) Multiple Comparison Test
Response: ST_A (ST4, ST5) Term A: DD2 Size($M)
Alpha=0.050 Error Term=S DF=132 MSE=0.8858564 Critical Value=2.424873
Different Group Count Mean From Groups 1 31 2.596774 3 2 31 2.903226 3 73 3.130137 1
356
Response ST_A (ST4, ST5) vs Size (DD1)
Expected Mean Squares Section Source Term Denominator Expected Term DF Fixed? Term Mean Square A: DD1 2 Yes S S+sA S 132 No S Note: Expected Mean Squares are for the balanced cell-frequency case.
Analysis of Variance Table Source Sum of Mean Prob Power Term DF Squares Square F-Ratio Level (Alpha=0.05) A: DD1 Size(Empls) 2 5.080555 2.540278 2.84 0.062121 0.548811 S 132 118.1528 0.8950968 Total (Adjusted) 134 123.2333 Total 135 * Term significant at alpha = 0.05
Means and Effects Section Standard Term Count Mean Error Effect All 135 2.955555 2.975926 A: DD1 Size(Empls) 1 60 2.766667 0.1221404 -0.2092593 2 30 2.95 0.1727326 -2.592593E-02 3 45 3.211111 0.1410356 0.2351852
Plots Section
Means of ST_A (ST4, ST5)
6.00
4.50
3.00
ST_A (ST4, ST5) 1.50
0.00 1 2 3 DD1 Size(Empls)
Bonferroni (All-Pairwise) Multiple Comparison Test
Response: ST_A (ST4, ST5) Term A: DD1 Size(Empls)
Alpha=0.050 Error Term=S DF=132 MSE=0.8950968 Critical Value=2.424873
Different Group Count Mean From Groups 1 60 2.766667 2 30 2.95 3 45 3.211111
357
Response VFLEX_A (VF1- VF4) vs Performance (P1)
Expected Mean Squares Section Source Term Denominator Expected Term DF Fixed? Term Mean Square A: P1 4 Yes S S+sA S 130 No S Note: Expected Mean Squares are for the balanced cell-frequency case.
Analysis of Variance Table Source Sum of Mean Prob Power Term DF Squares Square F-Ratio Level (Alpha=0.05) A: P1 Delivery 4 12.77805 3.194513 4.66 0.001503* 0.942896 S 130 89.06176 0.6850905 Total (Adjusted) 134 101.8398 Total 135 * Term significant at alpha = 0.05
Means and Effects Section Standard Term Count Mean Error Effect All 135 3.112963 2.993954 A: P1 Delivery 1 8 2.75 0.2926368 -0.2439541 2 12 2.583333 0.238937 -0.4106207 3 29 2.818965 0.1537004 -0.1749886 4 64 3.226563 0.1034627 0.2326084 5 22 3.590909 0.1764666 0.596955
Plots Section
Means of VFLEX_A (VF1- VF4)
6.00
4.50
3.00
1.50 VFLEX_A (VF1- VF4)
0.00 1 2 3 4 5 P1 Delivery
Bonferroni (All-Pairwise) Multiple Comparison Test
Response: VFLEX_A (VF1- VF4) Term A: P1 Delivery
Alpha=0.050 Error Term=S DF=130 MSE=0.6850905 Critical Value=2.855736
Different Group Count Mean From Groups 2 12 2.583333 5 1 8 2.75 3 29 2.818965 5 4 64 3.226563 5 22 3.590909 2, 3
358
Response VFLEX_A (VF1- VF4) vs Importance (IMP1)
Expected Mean Squares Section Source Term Denominator Expected Term DF Fixed? Term Mean Square A: IMP1 4 Yes S S+sA S 130 No S Note: Expected Mean Squares are for the balanced cell-frequency case.
Analysis of Variance Table Source Sum of Mean Prob Power Term DF Squares Square F-Ratio Level (Alpha=0.05) A: IMP1 4 8.670311 2.167578 3.02 0.020106* 0.789611 S 130 93.1695 0.7166885 Total (Adjusted) 134 101.8398 Total 135 * Term significant at alpha = 0.05
Means and Effects Section Standard Term Count Mean Error Effect All 135 3.112963 3.010948 A: IMP1 1 14 2.625 0.2262566 -0.3859483 2 12 2.875 0.244385 -0.1359483 3 29 3.017241 0.157205 6.293104E-03 4 40 3.09375 0.1338552 8.280172E-02 5 40 3.44375 0.1338552 0.4328017
Plots Section
Means of VFLEX_A (VF1- VF4)
6.00
4.50
3.00
1.50 VFLEX_A (VF1- VF4)
0.00 1 2 3 4 5 IMP1
Bonferroni (All-Pairwise) Multiple Comparison Test
Response: VFLEX_A (VF1- VF4) Term A: IMP1
Alpha=0.050 Error Term=S DF=130 MSE=0.7166885 Critical Value=2.855736
Different Group Count Mean From Groups 1 14 2.625 5 2 12 2.875 3 29 3.017241 4 40 3.09375 5 40 3.44375 1
359
Response ST_A (ST4, ST5) vs Importance (IMP1)
Expected Mean Squares Section Source Term Denominator Expected Term DF Fixed? Term Mean Square A: IMP1 4 Yes S S+sA S 130 No S Note: Expected Mean Squares are for the balanced cell-frequency case.
Analysis of Variance Table Source Sum of Mean Prob Power Term DF Squares Square F-Ratio Level (Alpha=0.05) A: IMP1 4 10.41434 2.603584 3.00 0.020891* 0.785901 S 130 112.819 0.8678384 Total (Adjusted) 134 123.2333 Total 135 * Term significant at alpha = 0.05
Means and Effects Section Standard Term Count Mean Error Effect All 135 2.955555 2.864815 A: IMP1 1 14 2.285714 0.2489748 -0.579101 2 12 3 0.2689236 0.1351847 3 29 2.775862 0.1729898 -0.0889532 4 40 3.0375 0.1472955 0.1726847 5 40 3.225 0.1472955 0.3601847
Plots Section
Means of ST_A (ST4, ST5)
6.00
4.50
3.00
ST_A (ST4, ST5) 1.50
0.00 1 2 3 4 5 IMP1
Bonferroni (All-Pairwise) Multiple Comparison Test
Response: ST_A (ST4, ST5) Term A: IMP1
Alpha=0.050 Error Term=S DF=130 MSE=0.8678384 Critical Value=2.855736
Different Group Count Mean From Groups 1 14 2.285714 5 3 29 2.775862 2 12 3 4 40 3.0375 5 40 3.225 1
360
Response Overtime (ST3) vs Size (DD2)
Expected Mean Squares Section Source Term Denominator Expected Term DF Fixed? Term Mean Square A: DD2 2 Yes S S+sA S 132 No S Note: Expected Mean Squares are for the balanced cell-frequency case.
Analysis of Variance Table Source Sum of Mean Prob Power Term DF Squares Square F-Ratio Level (Alpha=0.05) A: DD2 Size($M) 2 4.677986 2.338993 3.20 0.044142* 0.602524 S 132 96.62572 0.732013 Total (Adjusted) 134 101.3037 Total 135 * Term significant at alpha = 0.05
Means and Effects Section Standard Term Count Mean Error Effect All 135 4.318519 4.31389 A: DD2 Size($M) 1 31 4.032258 0.1536663 -0.2816321 2 31 4.580645 0.1536663 0.266755 3 73 4.328767 0.1001378 1.487701E-02
Plots Section
Means of ST3
6.00
4.50
3.00 ST3
1.50
0.00 1 2 3 DD2 Size($M)
Bonferroni (All-Pairwise) Multiple Comparison Test
Response: ST3 Term A: DD2 Size($M)
Alpha=0.050 Error Term=S DF=132 MSE=0.732013 Critical Value=2.424873
Different Group Count Mean From Groups 1 31 4.032258 2 3 73 4.328767 2 31 4.580645 1
361
Response PERF (P2, P3, P4) vs Size (DD2)
Expected Mean Squares Section Source Term Denominator Expected Term DF Fixed? Term Mean Square A: DD2 2 Yes S S+sA S 132 No S Note: Expected Mean Squares are for the balanced cell-frequency case.
Analysis of Variance Table Source Sum of Mean Prob Power Term DF Squares Square F-Ratio Level (Alpha=0.05) A: DD2 Size($M) 2 1.682827 0.8414134 1.46 0.236518 0.307131 S 132 76.19865 0.5772625 Total (Adjusted) 134 77.88148 Total 135 * Term significant at alpha = 0.05
Means and Effects Section Standard Term Count Mean Error Effect All 135 3.451852 3.410517 A: DD2 Size($M) 1 31 3.268817 0.1364602 -0.1416998 2 31 3.419355 0.1364602 8.837826E-03 3 73 3.543379 8.892528E-02 0.132862
Plots Section
Means of PERF (P2, P3, P4)
6.00
4.50
3.00
PERF (P2, P3, P4) 1.50
0.00 1 2 3 DD2 Size($M)
Bonferroni (All-Pairwise) Multiple Comparison Test
Response: PERF (P2, P3, P4) Term A: DD2 Size($M)
Alpha=0.050 Error Term=S DF=132 MSE=0.5772625 Critical Value=2.424873
Different Group Count Mean From Groups 1 31 3.268817 2 31 3.419355 3 73 3.543379
362
Response LT_C (LT4, LT5, LT6) vs Size (DD2)
Expected Mean Squares Section Source Term Denominator Expected Term DF Fixed? Term Mean Square A: DD2 2 Yes S S+sA S 132 No S Note: Expected Mean Squares are for the balanced cell-frequency case.
Analysis of Variance Table Source Sum of Mean Prob Power Term DF Squares Square F-Ratio Level (Alpha=0.05) A: DD2 Size($M) 2 7.743584 3.871792 3.92 0.022280* 0.697199 S 132 130.5148 0.9887489 Total (Adjusted) 134 138.2584 Total 135 * Term significant at alpha = 0.05
Means and Effects Section Standard Term Count Mean Error Effect All 135 2.953086 2.857662 A: DD2 Size($M) 1 31 2.60215 0.1785921 -0.2555114 2 31 2.806452 0.1785921 -5.121029E-02 3 73 3.164384 0.1163809 0.3067217
Plots Section
Means of LT_C (LT4, LT5, LT6)
6.00
4.50
3.00
1.50 LT_C (LT4, LT5, LT6)
0.00 1 2 3 DD2 Size($M)
Bonferroni (All-Pairwise) Multiple Comparison Test
Response: LT_C (LT4, LT5, LT6) Term A: DD2 Size($M)
Alpha=0.050 Error Term=S DF=132 MSE=0.9887489 Critical Value=2.424873
Different Group Count Mean From Groups 1 31 2.60215 3 2 31 2.806452 3 73 3.164384 1
363
C.4. Lisrel (version 8.3) Output for SEM Analysis in Section 5.5.6.
DATE: 6/25/2000 TIME: 15:52
L I S R E L 8.30
BY
Karl G. Jöreskog & Dag Sörbom
This program is published exclusively by Scientific Software International, Inc. 7383 N. Lincoln Avenue, Suite 100 Lincolnwood, IL 60712, U.S.A. Phone: (800)247-6113, (847)675-0720, Fax: (847)675-2140 Copyright by Scientific Software International, Inc., 1981-2000 Use of this program is subject to the terms specified in the Universal Copyright Convention. Website: www.ssicentral.com
The following lines were read from file C:\DISSER~1\SURVEY\SEM_MOD2.LPJ:
Observed variables: VF1 VF2 VF3 VF4 ST4 ST5 LT1 LT2 LT3 P2 P3 P4
Covariance matrix:
1.41 0.57 1.44 0.45 0.44 1.38 0.62 0.52 0.62 1.44 0.29 -0.04 0.13 0.34 1.54 0.23 0.16 0.27 0.21 0.46 1.16 0.15 -0.05 0.26 0.12 0.33 0.09 1.23 0.20 0.21 0.25 0.46 0.34 0.05 0.46 1.50 0.21 0.31 0.17 0.25 -0.02 0.16 0.25 0.55 1.30 0.19 0.22 0.16 0.15 0.09 0.11 0.10 0.05 0.11 0.75 0.11 0.21 0.11 0.06 0.10 -0.02 0.08 0.14 0.16 0.40 0.75 0.16 0.25 0.14 0.07 0.11 0.06 0.11 0.13 0.10 0.41 0.60 0.75
Sample size: 140 Latent variables: S_Term L_Term V_Flex Perf Relationships:
ST4 ST5 = S_Term LT2 LT3 = L_Term VF1 VF2 VF3 VF4 = V_Flex P2 P3 P4 = Perf V_Flex = S_Term L_Term Perf = V_Flex
Path Diagram
OPTIONS: AD>50 LISREL OUTPUT: RS MI SS SC EF
End of Problem
364
Covariance Matrix to be Analyzed
VF1 VF2 VF3 VF4 P2 P3 ------VF1 1.41 VF2 0.57 1.44 VF3 0.45 0.44 1.38 VF4 0.62 0.52 0.62 1.44 P2 0.19 0.22 0.16 0.15 0.75 P3 0.11 0.21 0.11 0.06 0.40 0.75 P4 0.16 0.25 0.14 0.07 0.41 0.60 ST4 0.29 -0.04 0.13 0.34 0.09 0.10 ST5 0.23 0.16 0.27 0.21 0.11 -0.02 LT2 0.20 0.21 0.25 0.46 0.05 0.14 LT3 0.21 0.31 0.17 0.25 0.11 0.16
Covariance Matrix to be Analyzed
P4 ST4 ST5 LT2 LT3 ------P4 0.75 ST4 0.11 1.54 ST5 0.06 0.46 1.16 LT2 0.13 0.34 0.05 1.50 LT3 0.10 -0.02 0.16 0.55 1.30
Parameter Specifications
LAMBDA-Y
V_Flex Perf ------VF1 0 0 VF2 1 0 VF3 2 0 VF4 3 0 P2 0 0 P3 0 4 P4 0 5
LAMBDA-X
S_Term L_Term ------ST4 6 0 ST5 7 0 LT2 0 8 LT3 0 9
BETA
V_Flex Perf ------V_Flex 0 0 Perf 10 0
GAMMA
S_Term L_Term ------V_Flex 11 12 Perf 0 0
PHI
S_Term L_Term ------
365
S_Term 0 L_Term 13 0
PSI Note: This matrix is diagonal.
V_Flex Perf ------14 15
THETA-EPS
VF1 VF2 VF3 VF4 P2 P3 ------16 17 18 19 20 21
THETA-EPS
P4 ------22
THETA-DELTA
ST4 ST5 LT2 LT3 ------23 24 25 26
Number of Iterations = 19
LISREL Estimates (Maximum Likelihood)
LAMBDA-Y
V_Flex Perf ------VF1 0.73 - -
VF2 0.66 - - (0.14) 4.82
VF3 0.67 - - (0.14) 4.95
VF4 0.85 - - (0.16) 5.48
P2 - - 0.53
P3 - - 0.76 (0.10) 7.57
P4 - - 0.79 (0.11) 7.50
LAMBDA-X
S_Term L_Term ------ST4 0.72 - - (0.18)
366
3.88
ST5 0.64 - - (0.16) 3.92
LT2 - - 0.86 (0.18) 4.90
LT3 - - 0.64 (0.14) 4.48
BETA
V_Flex Perf ------V_Flex - - - -
Perf 0.25 - - (0.11) 2.26
GAMMA
S_Term L_Term ------V_Flex 0.32 0.40 (0.15) (0.14) 2.16 2.78
Perf - - - -
Covariance Matrix of ETA and KSI
V_Flex Perf S_Term L_Term ------V_Flex 1.00 Perf 0.25 1.00 S_Term 0.43 0.11 1.00 L_Term 0.49 0.12 0.27 1.00
PHI
S_Term L_Term ------S_Term 1.00
L_Term 0.27 1.00 (0.15) 1.77
PSI Note: This matrix is diagonal.
V_Flex Perf ------0.67 0.94 (0.22) (0.25) 3.00 3.74
367
Squared Multiple Correlations for Structural Equations
V_Flex Perf ------0.33 0.06
Squared Multiple Correlations for Reduced Form
V_Flex Perf ------0.33 0.02
Reduced Form
S_Term L_Term ------V_Flex 0.32 0.40 (0.15) (0.14) 2.16 2.78
Perf 0.08 0.10 (0.05) (0.05) 1.64 1.86
THETA-EPS
VF1 VF2 VF3 VF4 P2 P3 ------0.87 1.00 0.92 0.71 0.47 0.17 (0.13) (0.14) (0.13) (0.14) (0.06) (0.05) 6.46 7.02 6.86 5.27 7.75 3.43
THETA-EPS
P4 ------0.13 (0.05) 2.53
Squared Multiple Correlations for Y - Variables
VF1 VF2 VF3 VF4 P2 P3 ------0.38 0.31 0.33 0.51 0.37 0.77
Squared Multiple Correlations for Y - Variables
P4 ------0.83
THETA-DELTA
ST4 ST5 LT2 LT3 ------1.03 0.75 0.76 0.89 (0.26) (0.20) (0.27) (0.18) 4.00 3.68 2.77 4.99
Squared Multiple Correlations for X - Variables
ST4 ST5 LT2 LT3 ------0.33 0.36 0.49 0.31
368
Goodness of Fit Statistics
Degrees of Freedom = 40 Minimum Fit Function Chi-Square = 47.13 (P = 0.20) Normal Theory Weighted Least Squares Chi-Square = 46.51 (P = 0.22) Estimated Non-centrality Parameter (NCP) = 6.51 90 Percent Confidence Interval for NCP = (0.0 ; 27.66)
Minimum Fit Function Value = 0.34 Population Discrepancy Function Value (F0) = 0.047 90 Percent Confidence Interval for F0 = (0.0 ; 0.20) Root Mean Square Error of Approximation (RMSEA) = 0.034 90 Percent Confidence Interval for RMSEA = (0.0 ; 0.071) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.72
Expected Cross-Validation Index (ECVI) = 0.71 90 Percent Confidence Interval for ECVI = (0.66 ; 0.86) ECVI for Saturated Model = 0.95 ECVI for Independence Model = 3.11
Chi-Square for Independence Model with 55 Degrees of Freedom = 409.79 Independence AIC = 431.79 Model AIC = 98.51 Saturated AIC = 132.00 Independence CAIC = 475.15 Model CAIC = 200.99 Saturated CAIC = 392.15
Normed Fit Index (NFI) = 0.88 Non-Normed Fit Index (NNFI) = 0.97 Parsimony Normed Fit Index (PNFI) = 0.64 Comparative Fit Index (CFI) = 0.98 Incremental Fit Index (IFI) = 0.98 Relative Fit Index (RFI) = 0.84
Critical N (CN) = 188.84
Root Mean Square Residual (RMR) = 0.069 Standardized RMR = 0.056 Goodness of Fit Index (GFI) = 0.94 Adjusted Goodness of Fit Index (AGFI) = 0.91 Parsimony Goodness of Fit Index (PGFI) = 0.57
Fitted Covariance Matrix
VF1 VF2 VF3 VF4 P2 P3 ------VF1 1.41 VF2 0.49 1.44 VF3 0.50 0.45 1.38 VF4 0.63 0.57 0.58 1.44 P2 0.10 0.09 0.09 0.11 0.75 P3 0.14 0.13 0.13 0.16 0.40 0.75 P4 0.15 0.13 0.13 0.17 0.41 0.60 ST4 0.22 0.20 0.21 0.26 0.04 0.06 ST5 0.20 0.18 0.18 0.23 0.04 0.05 LT2 0.31 0.28 0.28 0.36 0.05 0.08 LT3 0.23 0.21 0.21 0.26 0.04 0.06
Fitted Covariance Matrix
P4 ST4 ST5 LT2 LT3 ------P4 0.75 ST4 0.06 1.54 ST5 0.05 0.46 1.16 LT2 0.08 0.17 0.15 1.50 LT3 0.06 0.12 0.11 0.55 1.30
369
Fitted Residuals
VF1 VF2 VF3 VF4 P2 P3 ------VF1 0.00 VF2 0.08 0.00 VF3 -0.05 -0.01 0.00 VF4 -0.01 -0.05 0.04 0.00 P2 0.09 0.13 0.07 0.04 0.00 P3 -0.03 0.08 -0.02 -0.10 0.00 0.00 P4 0.01 0.12 0.01 -0.10 0.00 0.00 ST4 0.07 -0.24 -0.08 0.08 0.05 0.04 ST5 0.03 -0.02 0.09 -0.02 0.07 -0.07 LT2 -0.11 -0.07 -0.03 0.10 0.00 0.06 LT3 -0.02 0.10 -0.04 -0.01 0.07 0.10
Fitted Residuals
P4 ST4 ST5 LT2 LT3 ------P4 0.00 ST4 0.05 0.00 ST5 0.01 0.00 0.00 LT2 0.05 0.17 -0.10 0.00 LT3 0.04 -0.14 0.05 0.00 0.00
Summary Statistics for Fitted Residuals
Smallest Fitted Residual = -0.24 Median Fitted Residual = 0.00 Largest Fitted Residual = 0.17
Stemleaf Plot
- 2|4 - 1| - 1|41000 - 0|87755 - 0|433222211100000000000000000 0|11134444 0|55556777788899 1|00023 1|7
Standardized Residuals
VF1 VF2 VF3 VF4 P2 P3 ------VF1 - - VF2 1.43 - - VF3 -0.86 -0.13 - - VF4 -0.19 -1.07 1.10 - - P2 1.23 1.69 0.93 0.52 - - P3 -0.48 1.23 -0.29 -1.91 0.09 - - P4 0.25 1.79 0.11 -1.93 -0.96 1.64 ST4 0.80 -2.62 -0.86 1.19 0.57 0.49 ST5 0.41 -0.27 1.13 -0.40 0.97 -0.98 LT2 -1.39 -0.78 -0.39 1.70 -0.06 0.75 LT3 -0.24 1.24 -0.50 -0.24 0.86 1.30
Standardized Residuals
P4 ST4 ST5 LT2 LT3 ------P4 - - ST4 0.59 - - ST5 0.08 - - - - LT2 0.59 2.30 -1.57 - - LT3 0.50 -1.63 0.66 - - - -
370
Summary Statistics for Standardized Residuals
Smallest Standardized Residual = -2.62 Median Standardized Residual = 0.00 Largest Standardized Residual = 2.30
Stemleaf Plot
- 2|6 - 2| - 1|9966 - 1|4100 - 0|99855 - 0|4433222110000000000000 0|11124 0|55566677899 1|011222234 1|6778 2|3 Largest Negative Standardized Residuals Residual for ST4 and VF2 -2.62
Qplot of Standardized Residuals
3.5...... x . . . . . x...... x . . . * . N . .xx . o . . * . r . . * . m . . x* . a . . *x . l . . *x . . . *x . Q . . x x* . u . .*x . a . xxx . n . . x . t . .*x . i . x. * . l . .* . e . * . s . x .x . . xx . . . x ...... x...... x ...... -3.5...... -3.5 3.5 Standardized Residuals
371
Modification Indices and Expected Change
Modification Indices for LAMBDA-Y
V_Flex Perf ------VF1 - - 0.01 VF2 - - 3.25 VF3 - - 0.00 VF4 - - 4.09 P2 2.67 - - P3 0.92 - - P4 0.01 - -
Expected Change for LAMBDA-Y
V_Flex Perf ------VF1 - - 0.01 VF2 - - 0.19 VF3 - - 0.01 VF4 - - -0.20 P2 0.12 - - P3 -0.05 - - P4 0.01 - -
Standardized Expected Change for LAMBDA-Y
V_Flex Perf ------VF1 - - 0.01 VF2 - - 0.19 VF3 - - 0.01 VF4 - - -0.20 P2 0.12 - - P3 -0.05 - - P4 0.01 - -
Completely Standardized Expected Change for LAMBDA-Y
V_Flex Perf ------VF1 - - 0.01 VF2 - - 0.15 VF3 - - 0.00 VF4 - - -0.17 P2 0.14 - - P3 -0.06 - - P4 0.01 - -
Modification Indices for LAMBDA-X
S_Term L_Term ------ST4 - - 0.95 ST5 - - 0.95 LT2 0.44 - - LT3 0.44 - -
Expected Change for LAMBDA-X
S_Term L_Term ------ST4 - - 0.21 ST5 - - -0.19 LT2 0.16 - - LT3 -0.12 - -
372
Standardized Expected Change for LAMBDA-X
S_Term L_Term ------ST4 - - 0.21 ST5 - - -0.19 LT2 0.16 - - LT3 -0.12 - -
Completely Standardized Expected Change for LAMBDA-X
S_Term L_Term ------ST4 - - 0.17 ST5 - - -0.18 LT2 0.13 - - LT3 -0.10 - -
Modification Indices for BETA
V_Flex Perf ------V_Flex - - 0.73 Perf - - - -
Expected Change for BETA
V_Flex Perf ------V_Flex - - -0.17 Perf - - - -
Standardized Expected Change for BETA
V_Flex Perf ------V_Flex - - -0.17 Perf - - - -
Modification Indices for GAMMA
S_Term L_Term ------V_Flex - - - - Perf 0.06 0.87
Expected Change for GAMMA
S_Term L_Term ------V_Flex - - - - Perf 0.04 0.13
Standardized Expected Change for GAMMA
S_Term L_Term ------V_Flex - - - - Perf 0.04 0.13
No Non-Zero Modification Indices for PHI
Modification Indices for PSI
V_Flex Perf ------V_Flex - - Perf 0.73 - -
373
Expected Change for PSI
V_Flex Perf ------V_Flex - - Perf -0.16 - -
Standardized Expected Change for PSI
V_Flex Perf ------V_Flex - - Perf -0.16 - -
Modification Indices for THETA-EPS
VF1 VF2 VF3 VF4 P2 P3 ------VF1 - - VF2 2.03 - - VF3 0.73 0.02 - - VF4 0.04 1.15 1.22 - - P2 0.50 0.09 0.13 0.44 - - P3 0.64 0.00 0.08 0.09 0.01 - - P4 0.23 0.81 0.02 1.40 0.92 2.67
Modification Indices for THETA-EPS
P4 ------P4 - -
Expected Change for THETA-EPS
VF1 VF2 VF3 VF4 P2 P3 ------VF1 - - VF2 0.16 - - VF3 -0.09 -0.01 - - VF4 -0.02 -0.13 0.13 - - P2 0.04 0.02 0.02 0.04 - - P3 -0.04 0.00 -0.01 -0.01 0.01 - - P4 0.02 0.04 0.01 -0.05 -0.11 0.51
Expected Change for THETA-EPS
P4 ------P4 - -
Completely Standardized Expected Change for THETA-EPS
VF1 VF2 VF3 VF4 P2 P3 ------VF1 - - VF2 0.11 - - VF3 -0.07 -0.01 - - VF4 -0.02 -0.09 0.09 - - P2 0.04 0.02 0.02 0.04 - - P3 -0.03 0.00 -0.01 -0.01 0.01 - - P4 0.02 0.04 0.01 -0.05 -0.15 0.68
Completely Standardized Expected Change for THETA-EPS
P4 ------P4 - -
374
Modification Indices for THETA-DELTA-EPS
VF1 VF2 VF3 VF4 P2 P3 ------ST4 0.67 6.66 1.35 2.14 0.13 0.50 ST5 0.01 0.22 1.92 0.87 1.37 3.61 LT2 1.81 1.37 0.04 3.55 1.28 0.12 LT3 0.02 2.42 0.19 0.81 0.19 1.91
Modification Indices for THETA-DELTA-EPS
P4 ------ST4 0.00 ST5 0.89 LT2 0.19 LT3 1.13
Expected Change for THETA-DELTA-EPS
VF1 VF2 VF3 VF4 P2 P3 ------ST4 0.09 -0.28 -0.12 0.15 -0.02 0.04 ST5 0.01 0.04 0.12 -0.08 0.07 -0.08 LT2 -0.13 -0.12 -0.02 0.19 -0.07 0.02 LT3 0.01 0.15 -0.04 -0.08 0.03 0.06
Expected Change for THETA-DELTA-EPS
P4 ------ST4 0.00 ST5 0.04 LT2 0.02 LT3 -0.05
Completely Standardized Expected Change for THETA-DELTA-EPS
VF1 VF2 VF3 VF4 P2 P3 ------ST4 0.06 -0.19 -0.08 0.10 -0.02 0.03 ST5 0.01 0.03 0.10 -0.07 0.08 -0.09 LT2 -0.09 -0.08 -0.01 0.13 -0.07 0.02 LT3 0.01 0.11 -0.03 -0.06 0.03 0.06
Completely Standardized Expected Change for THETA-DELTA-EPS
P4 ------ST4 0.00 ST5 0.04 LT2 0.02 LT3 -0.05
Modification Indices for THETA-DELTA
ST4 ST5 LT2 LT3 ------ST4 - - ST5 - - - - LT2 8.17 4.84 - - LT3 5.54 2.48 - - - -
Expected Change for THETA-DELTA
ST4 ST5 LT2 LT3 ------ST4 - - ST5 - - - - LT2 0.34 -0.23 - - LT3 -0.25 0.14 - - - -
375
Completely Standardized Expected Change for THETA-DELTA
ST4 ST5 LT2 LT3 ------ST4 - - ST5 - - - - LT2 0.22 -0.17 - - LT3 -0.18 0.12 - - - -
Maximum Modification Index is 8.17 for Element ( 3, 1) of THETA-DELTA
Standardized Solution
LAMBDA-Y
V_Flex Perf ------VF1 0.73 - - VF2 0.66 - - VF3 0.67 - - VF4 0.85 - - P2 - - 0.53 P3 - - 0.76 P4 - - 0.79
LAMBDA-X
S_Term L_Term ------ST4 0.72 - - ST5 0.64 - - LT2 - - 0.86 LT3 - - 0.64
BETA
V_Flex Perf ------V_Flex - - - - Perf 0.25 - -
GAMMA
S_Term L_Term ------V_Flex 0.32 0.40 Perf - - - -
Correlation Matrix of ETA and KSI
V_Flex Perf S_Term L_Term ------V_Flex 1.00 Perf 0.25 1.00 S_Term 0.43 0.11 1.00 L_Term 0.49 0.12 0.27 1.00
PSI Note: This matrix is diagonal.
V_Flex Perf ------0.67 0.94
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Regression Matrix ETA on KSI (Standardized)
S_Term L_Term ------V_Flex 0.32 0.40 Perf 0.08 0.10
Completely Standardized Solution
LAMBDA-Y
V_Flex Perf ------VF1 0.62 - - VF2 0.55 - - VF3 0.57 - - VF4 0.71 - - P2 - - 0.61 P3 - - 0.88 P4 - - 0.91
LAMBDA-X
S_Term L_Term ------ST4 0.58 - - ST5 0.60 - - LT2 - - 0.70 LT3 - - 0.56
BETA
V_Flex Perf ------V_Flex - - - - Perf 0.25 - -
GAMMA
S_Term L_Term ------V_Flex 0.32 0.40 Perf - - - -
Correlation Matrix of ETA and KSI
V_Flex Perf S_Term L_Term ------V_Flex 1.00 Perf 0.25 1.00 S_Term 0.43 0.11 1.00 L_Term 0.49 0.12 0.27 1.00
PSI Note: This matrix is diagonal.
V_Flex Perf ------0.67 0.94
THETA-EPS
VF1 VF2 VF3 VF4 P2 P3 ------0.62 0.69 0.67 0.49 0.63 0.23
377
THETA-EPS
P4 ------0.17
THETA-DELTA
ST4 ST5 LT2 LT3 ------0.67 0.64 0.51 0.69
Regression Matrix ETA on KSI (Standardized)
S_Term L_Term ------V_Flex 0.32 0.40 Perf 0.08 0.10
Total and Indirect Effects
Total Effects of KSI on ETA
S_Term L_Term ------V_Flex 0.32 0.40 (0.15) (0.14) 2.16 2.78
Perf 0.08 0.10 (0.05) (0.05) 1.64 1.86
Indirect Effects of KSI on ETA
S_Term L_Term ------V_Flex - - - -
Perf 0.08 0.10 (0.05) (0.05) 1.64 1.86
Total Effects of ETA on ETA
V_Flex Perf ------V_Flex - - - -
Perf 0.25 - - (0.11) 2.26
Largest Eigenvalue of B*B' (Stability Index) is 0.063
Total Effects of ETA on Y
V_Flex Perf ------VF1 0.73 - -
VF2 0.66 - - (0.14) 4.82
VF3 0.67 - -
378
(0.14) 4.95
VF4 0.85 - - (0.16) 5.48
P2 0.13 0.53 (0.06) 2.26
P3 0.19 0.76 (0.08) (0.10) 2.32 7.57
P4 0.20 0.79 (0.08) (0.11) 2.33 7.50
Indirect Effects of ETA on Y
V_Flex Perf ------VF1 - - - -
VF2 - - - -
VF3 - - - -
VF4 - - - -
P2 0.13 - - (0.06) 2.26
P3 0.19 - - (0.08) 2.32
P4 0.20 - - (0.08) 2.33
Total Effects of KSI on Y
S_Term L_Term ------VF1 0.23 0.29 (0.11) (0.11) 2.16 2.78
VF2 0.21 0.27 (0.10) (0.10) 2.13 2.71
VF3 0.21 0.27 (0.10) (0.10) 2.14 2.74
VF4 0.27 0.34 (0.12) (0.12) 2.20 2.85
P2 0.04 0.05 (0.03) (0.03) 1.64 1.86
P3 0.06 0.08 (0.04) (0.04)
379
1.66 1.89
P4 0.06 0.08 (0.04) (0.04) 1.66 1.90
Standardized Total and Indirect Effects
Standardized Total Effects of KSI on ETA
S_Term L_Term ------V_Flex 0.32 0.40 Perf 0.08 0.10
Standardized Indirect Effects of KSI on ETA
S_Term L_Term ------V_Flex - - - - Perf 0.08 0.10
Standardized Total Effects of ETA on ETA
V_Flex Perf ------V_Flex - - - - Perf 0.25 - -
Standardized Total Effects of ETA on Y
V_Flex Perf ------VF1 0.73 - - VF2 0.66 - - VF3 0.67 - - VF4 0.85 - - P2 0.13 0.53 P3 0.19 0.76 P4 0.20 0.79
Completely Standardized Total Effects of ETA on Y
V_Flex Perf ------VF1 0.62 - - VF2 0.55 - - VF3 0.57 - - VF4 0.71 - - P2 0.15 0.61 P3 0.22 0.88 P4 0.23 0.91
Standardized Indirect Effects of ETA on Y
V_Flex Perf ------VF1 - - - - VF2 - - - - VF3 - - - - VF4 - - - - P2 0.13 - - P3 0.19 - - P4 0.20 - -
380
Completely Standardized Indirect Effects of ETA on Y
V_Flex Perf ------VF1 - - - - VF2 - - - - VF3 - - - - VF4 - - - - P2 0.15 - - P3 0.22 - - P4 0.23 - -
Standardized Total Effects of KSI on Y
S_Term L_Term ------VF1 0.23 0.29 VF2 0.21 0.27 VF3 0.21 0.27 VF4 0.27 0.34 P2 0.04 0.05 P3 0.06 0.08 P4 0.06 0.08
Completely Standardized Total Effects of KSI on Y
S_Term L_Term ------VF1 0.20 0.25 VF2 0.18 0.22 VF3 0.18 0.23 VF4 0.23 0.28 P2 0.05 0.06 P3 0.07 0.09 P4 0.07 0.09
The Problem used 21992 Bytes (= 0.0% of Available Workspace)
Time used: 0.332 Seconds
381
C.4.1 Standardized Solution and T-values for Alternative Model #2
Standardized Solution for Alternative Model #2
T-Values for the Path Coefficients for Alternative Model #2
382
C.4.2 Fit Statistics for Alternative Structural Equation Model #2
Degrees of Freedom = 40 Minimum Fit Function Chi-Square = 47.52 (P = 0.19) Normal Theory Weighted Least Squares Chi-Square = 44.41 (P = 0.29) Estimated Non-centrality Parameter (NCP) = 4.41 90 Percent Confidence Interval for NCP = (0.0 ; 24.91) Minimum Fit Function Value = 0.35 Population Discrepancy Function Value (F0) = 0.033 90 Percent Confidence Interval for F0 = (0.0 ; 0.19) Root Mean Square Error of Approximation (RMSEA) = 0.029 90 Percent Confidence Interval for RMSEA = (0.0 ; 0.068) P-Value for Test of Close Fit (RMSEA < 0.05) = 0.77 Expected Cross-Validation Index (ECVI) = 0.72 90 Percent Confidence Interval for ECVI = (0.69 ; 0.87) ECVI for Saturated Model = 0.99 ECVI for Independence Model = 3.21 Chi-Square for Independence Model with 55 Degrees of Freedom = 407.62 Independence AIC = 429.62 Model AIC = 96.41 Saturated AIC = 132.00 Independence CAIC = 472.58 Model CAIC = 197.95 Saturated CAIC = 389.75 Normed Fit Index (NFI) = 0.88 Non-Normed Fit Index (NNFI) = 0.97 Parsimony Normed Fit Index (PNFI) = 0.64 Comparative Fit Index (CFI) = 0.98 Incremental Fit Index (IFI) = 0.98 Relative Fit Index (RFI) = 0.84 Critical N (CN) = 180.59 Root Mean Square Residual (RMR) = 0.075 Standardized RMR = 0.061 Goodness of Fit Index (GFI) = 0.94 Adjusted Goodness of Fit Index (AGFI) = 0.91 Parsimony Goodness of Fit Index (PGFI) = 0.57
383