Supply Chain Performance Measurement and Management

Home , Performance indicator, Performance management

T.R.

MARMARA UNIVERSITY Department of Industrial Engineering

SUPPLY CHAIN PERFORMANCE MEASUREMENT AND MANAGEMENT

HAZAL KARABIYIK

June 2009 T.R.

MARMARA UNIVERSITY Department of Industrial Engineering

SUPPLY CHAIN PERFORMANCE MEASUREMENT AND MANAGEMENT

HAZAL KARABIYIK

June 2009

Asst. Prof. Dr. Bahar SENNAROĞLU Prof. Dr. Akif EYLER

Supervisor Jury Member

UNIVERSITÀ DEGLI STUDI DI PERUGIA

FACOLTÀ DI INGEGNERIA

Dipartimento di Ingegneria Industriale

SUPPLY CHAIN PERFORMANCE MEASUREMENT AND MANAGEMENT

Tesi di laurea di: Relatore: Hazal KARABIYIK Prof. Ing. Stefano Saetta

Correlatore:

Dr. Ing. Paolo Taticchi

Anno Accademico 2008-2009

ACKNOWLEDGMENT

This thesis was written in Italy, in the Università degli Studi di Perugia, during I was student by Erasmus Exchange Program. I have been blessed by having many good advisors and supporters with whom we could chew over different ideas that have gone into this thesis.

I am deeply grateful to my advisor, Dr. Ing. Paolo Taticchi, without his tremendous effort in supporting and guiding me I could not finish this thesis at this level.

Finally, I would like to thank to my family and my friends for their continual support during my studies. TABLE OF CONTENTS

ABSTRACT...... VI

ABBREVIATIONS...... VII

LIST OF FIGURES...... VIII

LIST OF TABLES ...... IX

PART I. INTRODUCTION and OBJECTIVES ...... 1 I.1. INTRODUCTION ...... 1 I.2. OBJECTIVES ...... 2

PART II. GENERAL BACKGROUND ...... 3 II.1. PERFORMANCE MEASUREMENT ...... 3 II.2. SUPPLY CHAIN PERFORMANCE MEASUREMENT...... 4 II.3. CITATION and COTATION ANALYSIS ...... 5 II.4. RESEARCH METHODOLOGY...... 7

PART III. THESIS ...... 11 III.1. FRAMEWORKS IDENTIFICATION ...... 11 III.2. GROUPING of GOOD CHARACTERISTICS ...... 16 III.3. DESIGN of a NEW FRAMEWORK ...... 19

PART IV. RESULTS ...... 26

PART V. DISCUSSIONS and EVALUATIONS ...... 27

REFERENCES ...... 28 ABSTRACT

SUPPLY CHAIN PERFORMANCE MEASUREMENT AND

MANAGEMET

This paper first gives the information about Performance Measurements and Supply Chain Management. After introduction to these concepts, the objective of paper is defined as to find a proper framework for measuring performance measurement in Supply Chain. Also, the need for performance measurement in Supply Chain was explained.

By means of ISI Web Knowledge “Performance” and “Measurement” and “Supply Chain” were searched as key words. By this research, 231 articles were found in literature. Also, the data set about the research was taken. With this data many graphics about literature were created and explained in this paper. This brings the idea of how the literature analysis like which authors are interested in this subject, which journals published about this and which years authors started to write more articles about this subject.

231 articles were investigated by reading each abstract and then 47 of them were selected for this project. After reading 47 articles, performance models that have been created by different authors were explained and strengths and weaknesses table were created in order to understand each framework. Also, common characteristics that authors mentioned in their articles were grouped.

In thesis part, while creating framework, common characteristics and the strengths of each frameworks were considered. In the first framework performance attributes and management level aspects were used. Also to identify Key Performance Indicators for new framework, frameworks that have been applied in the past were used and some of Key Performance Indicators were created for this framework. But it is not enough to reach the aim of the project. To add Supply Chain aspects Supply Chain Operations Reference Model was added to the first framework, the new framework was created according to performance attributes, management levels, internal and external aspect and whole Supply Chain parts.

By means of these frameworks, managers can show the whole Supply Chain. Also, to show that every part of supply chain is another company as themselves, same performance attributes in the first framework was put in the other parts. Then linking the all performance attributes, Global supply performance attributes were obtained. To consider this, managers can measure own company performance and also the whole Supply Chain performance.

Furthermore, in discussion part, it is discussed that the creativeness of the framework deeply. In Evaluation part, the positive and the negative side of the framework were explained. Then, suggestions for the using the framework were defined and in order to improve the framework some steps were suggested for future works like validation of the framework is needed.

ABBREVIATIONS

SC : Supply Chain

SCM : Supply Chain Management

KPI : Key Performance Indicator

SCP : Supply Chain Performance

PCTM : KPI Cost Transformation Matrix

JIT : Just in Time

TQM : Total Quality Management

CSF : Critical Success Factors

PBC : Performance Based Costing

CRM : Customer Relationship Management

QoS : Quality of Services

QLF : Quality Loss Function

DEA : Data Envelopment Analysis

SCOR : Supply Chain Operations Reference-model

SCC : Supply-Chain Council

GS : Global Supply

VII

LIST OF FIGURES

PAGE NO

Figure II.1 The phases of Development of Performance Measurement ...... 4 Figure II.2 Research Methodology ...... 7 Figure II.3 Published Items per Year ...... 8 Figure II.4 Number of Articles per Source Title...... 8 Figure II.5 Number of Articles per each Author...... 9 Figure II.6 Number of Articles per Document Type...... 9 Figure II.7 Number of Articles per General Categories ...... 10 Figure II.8 Number of Articles per Subject Area ...... 10 Figure III.1 Metrics for the Performance Evaluation of a Supply Chain ...... 12 Figure III.2 Supply Chain Performance Metrics Framework ...... 13 Figure III.3 Measuring Performance in New Enterprise ...... 13 Figure III.4 Measurement of Quality of Service in Supply Chain ...... 14 Figure III.5 Risk Management Framework ...... 15 Figure III.6 Performance Attributes at Level 1 ...... 19 Figure III.7 First framework ...... 20 Figure III.8 The SCOR Model described at different level of details ...... 22 Figure III.9 SCOR model ...... 23 Figure III.10 Second Framework with SCOR model ...... 25

VIII

LIST OF TABLES

PAGE NO

Table II.1 Most Frequently cited performance measurement works ...... 7 Table III.1 Frameworks for Performance Measurement in Supply Chain ...... 11 Table III.2 Strengths and Weaknesses of Frameworks ...... 15

PART I

INTRODUCTION AND OBJECTIVES

I.1. INTRODUCTION The interest in managing supply chains is growing rapidly among companies around the world. Major forces behind this development and increasing competitive pressure and belief that working cooperatively in supply chains (SCs) can create a competitive advantage. Coordinating activities in supply chain, however, is difficult. The difficulties are partly due to the complexity induced by the large number of related and independent activities in the supply chain. Understanding the interdependencies and the complex casual relationships in a supply chain is therefore crucial to the successful management of activities. In many organizations the problems show us that the use of system thinking is insufficiently developed although it has been with us for several decades. Because of the lack of system thinking, many firms approached to another important area: the design of performance measurement systems with supply chain. A performance measurement system plays an important role in managing a business as it provides the information necessary for decision making and actions.

I.1.1. Supply Chain Management Supply chain management (SCM) is the integration of activities which starts with the procurement materials, continues with the transformation of the material to semi-finished or finished products and extends to the transportation of the product to the final customer. The goal is to establish a chain which provides the greatest value to the customer and in the meantime, to decrease waste considerably. The object of SCM obviously is the supply chain which represents “a network of organizations that are involved, through upstream and downstream linkages, in the different processes ad activities that produce value in the form of products and services in the hands of the ultimate customer”. In a broad sense a supply chain consists of two or more legally separated organizations, being linked to material, information and financial flows. These organizations may be firms producing parts, components and end products, logistic service providers and even the (ultimate) customer himself. A network usually will not only focus on flows within a (single) chain, but usually will have to deal with divergent and convergent flows within a complex network resulting from many different customer orders to be handled in parallel. In order to ease complexity, a given organization may concentrate only on a portion of the overall supply chain. The objective governing all endeavors within a supply chain is seen as increasing competitiveness. This is because no single organizational unit now is solely responsible for the competitiveness of its products and services in the eyes of the ultimate customer, but the supply chain as a whole. Hence, competition has shifted from single companies to supply chains. Obviously to convince an individual company to become a part of supply chain requires a win-win situation for each participant in the long run, while this may not be the case for all entities in the short term. We are able to define the term Supply Chain Management as the task of integrating organizational units along a supply chain and coordinating material, information and financial

1 flows in order to fulfill (ultimate) customer demands with the aim of improving competitiveness of a supply chain as a whole.

I.1.2. Performance Measurement Performance measures have two central effects and work in two directions. First of all they can be used to describe the past and present of the process being considered. On the other hand they can be used to set performance goals. This allows establishing a focus on the future. By fixing a will-be value or target of a performance measure it is possible to watch the progress in reaching the target and the success in achieving the target itself. Organizations measure their performance in order to monitor their employees and departments, to direct them, to provide feedback for being able to carry on their goals and to assess the performance of the organization vis-à-vis the strategic and continuous improvement goals. Organizations need both short term and long term performance assessment. Traditional performance measurement systems rely on static metrics that are easier to measure, to gather and to quantify. However, the focus in the contemporary business environment has shifted from the present to the future. Thus dynamic metrics which show the movement of a variable toward a target ratio are more meaningful.

I.2. OBJECTIVE The objective of this thesis is to explore the role of performance measurement in SCM and develop a framework and a right set of Key Performance Indicators (KPIs) for SC performance measurement. The literature analysis shows that there are not sufficient frameworks for performance measurement. In addition to that, to measure performance measurement is not standard, how can we measure? What KPIs do we need? This thesis also answers these types of questions.

I.2.1. Relevance of Performance Measurement in SCM In recent years, a number of firms realized the potentials of SCM. However, they often lack the insight for the development of effective performance measures and metrics needed to achieve a fully integrated supply chain. Moreover, such measures and metrics are needed to test and reveal the viability of strategies without which a clear direction for improvement and realization of goals would be highly difficult. Lee and Billington, 1992 argue that discrete sites in a supply chain do not lead to an improved productivity if each is to pursue its goals independently, which has been the traditional practice. There is, however, a greater need to study the measures and metrics in the context for the following two reasons: The first one is that lack of a balanced approach and the second one is that lack of a clear distinction between metrics at strategic, tactical, and operational levels. The second reason is that lack of a clear distinction between metrics at strategic, tactical, and operational levels. Metrics that are used in performance measurement influence the decisions to be made at strategic, tactical, and operational levels. However, we fail to come across any such classification for supply chain management. Using a classification based on these three levels, each metric can be assigned to a level where it would be most appropriate. Therefore, it is clear that for effective management in a supply chain, measurement goals must consider the overall supply chain goals and the metrics to be used. (Gunasekaran et all, 2001)

PART II

GENERAL BACKGROUND

II.1. PERFORMANCE MEASUREMENT It is suggested that the performance measurement system be designed as a part of the strategy implementation plan, that each metric’s effect on the firm’s goal be determined and that the performance measurement system be conformable with the firm’s cultural values. Measurement systems should focus on information rather than control, and should be revised continuously. The goal of performance measurement is to improve business processes and that one should be careful when selecting the performance metrics. Performance measurement is a time consuming and costly process. One should determine how performance measurement will improve business processes. In this way, it is possible to identify the minimum number of performance metrics which will provide maximum benefit during implementation. Organizations measure their performance in order to monitor their employees and departments, to direct them, to provide feedback for being able to carry on their goals and to assess the performance of the organization vis-à-vis the strategic and continuous improvement goals. Organizations need both short term and long term performance assessment. Traditional performance measurement systems rely on static metrics that are easier to measure, to gather and to quantify. However, the focus in the contemporary business environment has shifted from the present to the future. Thus, dynamic metrics which show the movement of a variable toward a target ratio are more meaningful. (Tarr, 1996). It is suggested that the performance measurement system be designed as a part of the strategy implementation plan, that each metric’s effect on the firm’s goal be determined and that the performance measurement system be conformable with the firm’s cultural values. According to Tarr, 1996, measurement systems should focus on information rather than control, and should be revised continuously. Robson, 2004, underlines that the goal of performance measurement is to improve business processes and that one should be careful when selecting the performance metrics. Performance measurement is a time consuming and costly process. One should determine how performance measurement will improve business processes. In this way, it is possible to identify the minimum number of performance metrics which will provide maximum benefit during implementation. (Muratoglu, 2008) Also, there is a figure about the historical development of Performance Measurement. Also, this history is shown in Figure II.1. Performance Measurement starts in 1450’s. These start with the basic measurement of financial transactions, an element that is still in evidence today and which is focused on the traditional “buy cheap – sell dear – make profit” perspective. It can be reasonably argued that these internally focused perspectives pervaded the thinking of management for a long time, perhaps until the end of the World War II, and the subsequent steady rise of the “quality revolution”. Although starting at a very low-level in the 1950s, by the 1970s and 1980s the quality revolution was in full swing. The fourth phase of performance measurement emerged in which the financial measures began to be regarded as part of an integrated performance measurement system. The Balanced Scorecard, probably the most widely evaluated and discussed performance measurement system of all time, was introduced to the world by Kaplan and Norton, 1996. The final and current phase is one in which the importance of the supply chain

3 emerges. From a philosophical point of view this represents a significant shift away from the unitary to the pluralist perspective. It recognizes that customer satisfaction can only come from the supply chain functioning effectively in totality (both processes and process interfaces). This closely follows the logic “Theory of Constraints” model as focused on intra- organizational activities, and its subsequent extrapolation to a wider inter-organizational perspective. However, success in meeting customers’ needs requires an increasing international perspective from the supply network and this introduces a new vector of pan- cultural into the performance measurement perspective. (Morgan, 2007)

Figure II.1 The phases of Development of Performance Measurement

II.2. SUPPLY CHAINPERFORMANCE MEASUREMENT Cook and Hagey, 2003 suggest that firms should first determine their strategy and after that, they should design their supply chain strategy according to the requirements of the corporate strategy. Tracking the performance of the whole supply chain as well as making use of analyses instead of estimations when setting goals, is essential for effective supply chains. Successful firms are those which align their operations with those of the customers, suppliers and the other parties in the chain, and which know their own performance metrics as well as the performance metrics of other members of the chain. A supply chain performance measurement system should comprise the whole chain and should be congruent with the actual performance measurement systems of the firm. This system should consider the interests of all the business partners, contain financial and non- financial metrics in a balanced way, not focus on functional departments but focus on business processes. Performance metrics should be simple, clear and meaningful, and they should be limited in number. The flexibility of the performance measurement system, the adaptability of the metrics according to the goals is essential. The performance measurement system should contain not only historical data, but it should also have a structure which is capable of covering future and potential developments. Caplice and Sheffi, 1995, mention that a good supply chain performance measurement system should be comprehensive, focus on cause and effect, are both vertically and horizontally integrated, be internally comparable and useful. A comprehensive supply chain performance measurement system is described as having several dimensions such as internal efficiency, customer satisfaction, financial data etc. Supply chain performance measurement should be comparable and conformable to both the firm’s departments and to between the

4 other firms in the supply chain. They suggest metrics such as customer satisfaction/quality, time, costs and assets, which can be followed both in terms of results attained and in terms of diagnosis. In general, traditional performance metrics are financial and they measure return on investment, profitability, cash return etc. These data are absolute and objective. However, these measures are criticized as showing a limited perspective of the firm, being retroactive and not being able to provide strong forecasts about the future. (Parker, 2000). For an efficient performance measurement system, Morgan, 2004 suggests that strategy should drive production activities and determine the goals for the performance measurement system. Performance measurement should lead performance improvement, that if performance measurement is used as feed forward instead of feedback, it would help management to take care of the matters which require strategic improvements. Successful firms are those which have conformable competitive strategies and supply chain strategies. When establishing supply chains, it is suggested to balance responsiveness and efficiency in the best way to provide the competitive strategy. There are four factors which affect supply chain performance in terms of responsiveness and efficiency; inventory, transportation, facilities and information. In the meantime, these four factors determine the conformability of the competitive strategy and supply chain strategy. (Muratoglu, 2008)

II.3. CITATION and CO-CITATION ANALYSIS The challenges posed by performance measurement are enduring. The first ever edition of the Administrative Science Quarterly, published in 1956, contained a paper entitled “Dysfunctional Consequences of Measurement” (Ridgway, 1956). In that paper, Ridgway explored the relative strengths and weaknesses of single, multiple and aggregated performance measures, bemoaning the “strong tendency to state numerically as many as possible of the variables with which management must deal”. A few years earlier – in 1952 – Chris Argyris, in his classic text The Impact of Budgets on People, reported that managers claimed to “feed machines all the easy orders at the end of the month to meet [their] quota” (Argyris, 1952). These two themes – the desire to quantify and the unanticipated consequences of measurement lead that doyenne of management – Peter Drucker – to argue that one potential solution was to introduce “balanced” sets of measures. “Market standing, innovation, productivity, physical and financial resources, profitability, manager performance and development, worker performance and attitude, and public responsibility” are appropriate performance criteria says Drucker in his 1954 publication The Practice of Management (Drucker, 1954). If the clock is turned forward thirty years then we find that the same themes are still being discussed. Power’s book The Audit Society: Rituals of Verification bemoans the rise of the “Audit Society”, arguing that practitioners and policy makers have become obsessed with measurement and regulation (Power, 1997) – the desire to quantify. Hayes and Abernathy explore the unintended consequences of this obsession in “Managing our way to economic decline”. They argue that inappropriate performance measures and poorly designed incentive schemes were partly to blame for a short-term US business culture, which damaged the country’s competitiveness and economic prospects (Hayes and Abernathy, 1980). Johnson and Kaplan expanded these arguments, claiming that not only did measurement systems result in unintended consequences, but also that the measurement systems many firms used were woefully inadequate because they provided managers with redundant information as they were based on assumptions that were grossly outdated given the changing nature of organizational cost structures (Johnson and Kaplan,1987). Alfred Chandler made similar points in The Visible Hand, which emphasized that many of the basic principles of accounting

5 had remained largely unchanged since they were first developed in the 1920s by the DuPont cousins and Donaldson Brown (Chandler, 1977). These recurring themes – the desire to quantify and the unanticipated consequences of quantification – appear to have resulted in frequent “re-discoveries” of Drucker’s 1954 suggestion that balanced measurement systems should be developed (Drucker, 1954). Throughout the 1980s and early 1990s, numerous authors suggested measurement frameworks that might be appropriate – the performance pyramid (Lynch and Cross, 1991), the results-determinants framework (Fitzgerald et al., 1991), the performance measurement matrix (Keegan et al., 1989) and, of course, the balanced scorecard (Kaplan and Norton, 1992). The result was that a dominant research question in the mid-1990s, at least for the operations management community with an interest in performance measurement, was how can these so-called “balanced performance measurement systems” be developed and deployed. There followed a rich stream of work on the design and deployment of performance measurement systems, which reported on research to develop processes for designing measurement systems and barriers to their successful implementation (Bourne et al., 2000; Dixon et al., 1990; Neely et al., 1996). To examine these developments more fully and the basis of empirical evidence a citation/co-citation analysis of research on performance measurement was conducted. Recent advances in information technology and online data storage have considerably eased the process of citation/co-citation analysis. The dataset used in this paper was constructed using the ISI Web of Science database. Every publication that contained the phrase “performance measurement” in its title, keywords or abstract was identified and downloaded. This search identified 1,352 papers published in 546 different journals. The earliest paper included in the dataset was published in 1981 and the most recent in 2005 (84 per cent of publications included in the dataset have been published since January 1995). The data were downloaded using the Sitkis software. Before conducting the analysis a substantive review of the generated dataset was undertaken. Every record that related to the 20 most cited authors was reviewed and confirmed (the top 5 per cent of citations) and the title of every journal in the dataset was checked. Other obvious errors in the dataset were corrected in line with current best practice for bibliometric analysis. The 1,352 papers included in the dataset provide some 31,646 citations, covering 25,040 works and drawing on 16,697 different lead authors. The most frequently cited authors were: Bob Kaplan (398 citations), Andy Neely (153 citations), Rajiv Banker (134 citations), Abraham Charnes (111) citations and Robin Cooper (70 citations). As can be seen from these data, there were only four lead authors whose works were cited more than 100 times and interestingly these four lead authors have somewhat different disciplinary backgrounds – accounting (Kaplan), operations management (Neely), accounting/operations research and information systems (Banker) and mathematics/operations research (Charnes). Of the remaining citations – twelve lead authors were cited between 50 and 100 times, 266 were cited between 10 and 49 times and 11,929 (71.4 per cent) were cited only once. The spread of journals from which citations appeared is interesting. In total, the citations were drawn from 11,443 different journals. The most frequently cited journals were the Harvard Business Review (650 citations), the International Journal of Operations & Production Management (552 citations) and the Journal of the American Medical Association (339 citations). Together these three journals accounted for some 4.9 per cent of citations, while the top ten journals accounted for 10.2 per cent of citations and 73.6 per cent of journals contained only paper that was cited in the dataset. This diversity of source materials – large number of rarely cited Works and journals – is indicative of a widely distributed and relatively immature field of academic study, which has relatively little consensus about its core theoretical foundations.

II.3.1. Performance measurement research: analysis of citations At a more detailed level, it is possible to explore the frequency of citations for individual pieces of work. Once again the pattern of citations is diverse, further supporting the suggestion that the field of performance measurement is immature with little consensus. Only 10 works are cited more than 30 times (Table II.1). Eighty-seven per cent are cited only once and 99 per cent are cited less than 5 times. The most striking observation about the data included in Table I is the dominance of Bob Kaplan and David Norton and the balanced scorecard. Given that research data suggest that between 30 and 60 per cent of firms have adopted the balanced scorecard (Rigby, 2001; Silk, 1998; Williams, 2001; Speckbacher et al., 2003, Marr et al., 2004), this dominance is not surprising, but it is interesting, especially when one bears in mind the relative paucity of empirical research into the performance impact of measurement frameworks, including the balanced scorecard. (Neely, 2005)

Table II.1 Most Frequently cited performance measurement works

II.4. RESEARCH METHODOLOGY Research Methodology of this work, is shown in the Figure II.2. After Literature Review and analysis, frameworks about Performance Measurement and Supply Chain in literature were investigated. Then, strengths and weaknesses of each framework were defined. After this identification, common good characteristics that many authors mentioned about were grouped. By considering strengths of frameworks and good characteristics, the new framework for performance measurement in Supply Chain was created.

Figure II.2 Research Methodology

II.4.1. Performance Measurement Literature Review In literature review part, main journal and books debating Performance Measurement and Supply Chain topics have been reviewed through the electronic libraries of Perugia University and Bradford University. Large information is also available on the web.

II.4.2. Performance Measurement Literature Analysis In this section, the literature review carried out is analyzed different perspective. 3 key words, “performance and measurement” and “supply chain”, were investigated through ISI Web of Knowledge. By means of this research, 231 articles were found. With these 231 articles, citation and co-citation analysis were done and the next section will explain the results of this analysis.

II.4.2.1. Citation / Co-Citation Analysis The earliest paper about Performance Measurement and Supply Chain included in the data set was published 1998 and the most recent in 2009. On the other hand the most number of papers were published in 2007 is shown in Figure II.3. From this figure, it is obvious that in 2008 there is less articles were published then in 2008 did. Furthermore, from the figure it is understandable after 2000; there is a really big increase especially in 2003. This means that after 2000’s the importance of PM was understood by academicians, although they say that it is still not enough for development of PM. This search identified 231 papers published in 10 different journals. In this research, the most frequently used article is International Journal of Production Economies shown in Figure II.4. The next ones are International Journal of Supply Chain Management and Production Planning and Control.

Figure II.3 Published Items per Year

Figure II.4 Number of Articles per Source Title

There are not so much authors who are interested in PM. Figure II.5 shows that the most frequently used authors are Gunasekaran A. and Reiner G. The articles of Gunasekaran A. will be used more frequently in this paper. His frameworks and his perspective helped me to create new frameworks for measuring PM. The main problem is in PM that there are few authors who write articles about PM. Therefore, all articles have similar point of view. On the other hand, the most used document type of papers is meeting and article is shown in Figure II.6.

Figure II.5 Number of Articles per each Author

Figure II.6 Number of Articles per Document Type

There are several possible explanations for why the field of performance measurement has not professionalized from an academic perspective. The main explanation is that performance measurement is not and never can be a field of academic study because its diversity. From literature survey, it is obviously seen that authors come from a variety of different disciplinary backgrounds. Figure II.7 shows us the main classes of articles are Science and Technology and Social Science which are not so close to each other. Besides, Figure II.8 shows us that this variety. The most frequently used articles based on Engineering, Business and Economics, Computer Science and Operations Research and Management Science. The main point is to combine these different disciplinarians under the Performance Measurement title.

Figure II.7 Number of Articles per General Categories

Figure II.8 Number of Articles per Subject Area

II.4.3. Literature Review and Analysis Findings

The original set of 231 articles has been reviewed. About 50% of the papers have been excluded after abstract reading. The remaining articles have been read and 47 articles has been identified has determinants for this research.

PART III

THESIS

III.1. FRAMEWORKS IDENTIFICATION This section shows that the frameworks that were found in literature among 47 articles. Each framework is explained shortly and also there are figures of all them. At the end, there is a compassion section between these frameworks according to their strengths and weaknesses.

III.1.1. Frameworks in Literature This thesis will mention five frameworks which can be used for measuring SC performance, will be analyzed in next part. These frameworks for performance measurement of Supply Chain are shown in chronological order in Table III.1.

Table III.1 Frameworks for Performance Measurement in Supply Chain

Name of Framework Year

Metrics for the Performance Evaluation of a Supply Chain 2001 Supply Chain Performance Metrics Framework 2004 Measuring Performance in New Enterprise 2005 Measurement of Quality of Service in Supply Chain 2006

Risk Management Framework 2007

III.1.1.1. Metrics for the Performance Evaluation of a Supply Chain This framework established by Gunasekaran et. al., 2001, to measure the strategic, tactical and operational level performance in a supply chain. This has been done so as to assign them where they can be best dealt with by the appropriate management level, and for fair decisions to be made. The metrics are also distinguished as financial and non-financial so that a suitable costing method based on activity analysis can be applied. In some cases, a metric is classified as both financial and non-financial. For example, the buyer-supplier relationship can be qualified in terms of financial performance achieved, such as cost savings, and in terms of tangible and intangible benefits, like quality, flexibility and deliverability. And this framework is shown in Figure III.1.

III.1.1.2. Supply Chain Performance Metrics - Framework This framework is based in part of a theoretical framework discussed by Gunasekaran et. al., 2004 which is first framework that analyzed in this thesis. A framework for performance measures and metrics are presented considering the four major supply chain activities/processes (plan, source, make/assemble and deliver) shown as in Figure III.2. These metrics also are classified at strategic, tactical, and operational to clarify the appropriate level of management authority and responsibility for performance. Measures are grouped in cells at

11 the intersection of the supply chain activity and planning level. The items in each cell are listed in the order of importance based on percentage importance ratings.

Figure III.1 Metrics for the Performance Evaluation of a Supply Chain

Some measures appear in more than one cell, indicating that measures may be appropriate at more than one management level. Measures used at different management levels will most assuredly require adjustment to tailor them to planning and control needs of the different levels. There is nothing novel about this approach, as it has been used for years in management planning and control systems. This framework should be regarded as a starting point for an assessment of the need for supply chain performance measurement.

III.1.1.3. Measuring Performance in New Enterprise Figure III.3 provides examples of value creation areas, critical success factors (CSF), performance measures and CSF drivers. It is framework for relating value creating areas to CSFs and CSF drivers, and to performance measures. This framework does not, and could not possibly contain all value creating areas, CSFs, performance measures, or CSF drivers, because they are likely to vary from firm to firm, and from business model to business model.

Organizations could develop their own matrices for relating the elements of Performance Based Costing (PBC)-matrices based on their unique needs. Inter-organizational teams of Virtual business partner employees or from supply chain partners could use this methodology to develop PBCs for assessing value system performance from end to end. Such a system wide approach to PBC could provide a starting place for the internal PBC of individual partners. (Gunasekaran et al., 2005)

Figure III.2 Supply Chain Performance Metrics Framework

Figure III.3 Measuring Performance in New Enterprise

III.1.1.4. Measurement of Quality of Service in Supply Chain This frame work which is presented in Figure III.4 focuses on quality service in Supply Chain. A framework for the modeling and measurement of Quality of Services (QoS) in supply chain the framework is divided in two parts: (1) Model development (Part A) (2) Measurement methodology (Part B) In part A, the conceptualization of the model is based on gap analysis. Gaps covered in this framework are divided into two types. One gap type is forward which is defined as basic supply chain direction (direction of movement of product). The other gap type is reverse which is considered as reversed direction of the basic supply chain process (reverse to the physical movement of the product) A typically supply chain is influenced by a variety of external environmental factors such as economic, political, legal which may play important role in the context of a global economy, and affect the supply chain sourcing, distribution, plant location and other operational decisions. In part B, the measurement of service quality of supply chain is considered as quantitative and qualitative aspects. There is two phases in part B first one is data collection and the second one is data analysis. Data collection is made by surveys, expert interviews and field observation. Data analysis are made by statistical analysis, quality loss function (QLF), and data envelopment analysis (DEA) (Seth et al., 2006)

Figure III.4 Measurement of Quality of Service in Supply Chain

III.1.1.5. Risk Management Framework The schematic representation which is shown in Figure III.5 identifies the five major components of the framework. In many respects, this is a fairly generic framework that may apply across a number of business settings. A number of typical elements have been identified within each of the five major components. There are features of the framework which are intended to suggest, that it is more than simply an aggregation of the five components. The linear sequential process can be explained like this. Changes in desired performance outcomes may trigger a sequence of actions to reconsider the perceptions of the risks involved, asses the impact on the portfolio being managed while simultaneously reviewing the evidence of the

14 primary risk drivers involved. The essence of risk perceptiveness pervaded the whole framework. This framework also gives different functions for each component which are included own determinants. (Ritchie and Brindley, 2007)

Figure III.5 Risk Management Framework

III.1.2. Comparison This comparison part gives us table about the strengths and weaknesses of all frameworks that we mentioned above. This table also shows the year of foundation of frameworks and by whom. By using this table, the compassion among the frameworks of Supply Chain Performance Measurement can be done easily. When building new framework, strengths of each framework were considered, we tried to avoid weaknesses. For example, the first two frameworks have the same strength feature is having managerial aspects. From these, it is obvious that to develop framework having managerial aspects will be better. Also, we can understand that to develop framework which has changeable features from business to business will not be so efficient framework. To sum up with, this table is like our guide while creating new frameworks. We will use this, to follow positive sides of frameworks that crated in the past also to avoid negative sides of them. It is also impossible to add all strengths into one framework and it is also impossible to develop framework which has no weaknesses. On the other hand, we want to reach the aim of this work which is to develop a framework and right sets pf KPIs for SC performance measurement. It is important to choose proper KPIs. While selecting the KPIs, we consider the KPIs that other authors used while creating their frameworks.

Table III.2 Strengths and Weaknesses of Frameworks

III.2. GROUPING of GOOD CHARACTERISTICS In this section, there is a grouping about the key elements that different authors wrote in their article about same subject. There are seven main key elements that in the past authors has mentioned.

III.2.1. Lack of a balanced approach between financial and non- financial indicators

Gunasekaran et. al., 2001 o Many companies have realized the importance of financial and non-financial performance measures. They failed to understand them in a balanced framework. o For a balanced approach, companies should bear in mind that, while financial performance measurements are important for strategic decisions and external reporting, day-to-day control of manufacturing and distribution operations is better handled with non-financial measures

III.2.2. Clear distinction between levels Gunasekaran et. al., 2001 o Metrics that are used in performance measurement influence the decisions to be made o at strategic, tactical and operational levels. o However, we fail to come across any such classification for supply chain management. o Using classification based on these three levels, each metric can be assigned to a level where it would be most appropriate. Cuthbertson and Piotrowicz, 2008 o A supply chain dimension was lacking. The cases concentrated mainly on internal o issues at a company level, not on the whole supply chain o Operational benefits dominated, while the strategic impact was often ignored Angerhorfer et. al., 2006 o The decision on which level(s) collaboration is suitable and beneficial is determined by the market environment and business strategy o The operational level may take the form of a routine task such as transportation scheduling o At managerial level could lead to better planning and forecasting o At strategic level may involve decisions that will have medium-to-long term effects.

III.2.3. Capacity Gunasekaran et. al., 2001 o The role of capacity in determining the level of all supply chain activities is clear. This highlights the importance of measuring and controlling the capacity utilization o By measuring capacity, gains flexibility, lead-time and deliverability Chan and Qi, 2003 o The ability of one specific activity to fulfill a task or perform a required function o This dimension mainly concerns the maximum amount of tasks that a process or an activity can complete under the normal conditions. (Production and transport) III.2.4. Flexibility Gunasekaran et. al., 2004 o Flexibility in meeting a particular customer delivery requirement at an agreed place, agreed mode of delivery and with agreed upon customized packaging o This type of flexibility can influence the decision of customers to place orders.

Theeranuphattana and Tang, 2007 o The agility of a SC in responding to marketplace changes to gain or maintain competitive advantage Chan and Qi, 2003 o The ability of one specific activity to adapt to the varying functional requirements or respond to the changes. o Flexibility is based on the range of a variable capacity of tasks, processes or activities that can be completed in the specific period of time and at a reasonable cost. Beamon, 1999 o Reductions in the number of backorders. o Reductions in the number of lost sales. o Reductions in the number of late orders. o Increased customer satisfaction. o Ability to respond to and accommodate demand variations, such as seasonality. o Ability to respond to and accommodate periods of poor manufacturing performance (machine breakdowns). o Ability to respond to and accommodate periods of poor supplier performance. o Ability to respond to and accommodate periods of poor delivery performance. o Ability to respond to and accommodate new products, new markets, or new competitors. o Flexibility, which is seldom used in supply chain analysis, can measure a system's ability to accommodate volume and schedule fluctuations from suppliers, manufacturers, and customers.

III.2.5. Costing System Gunasekaran et al., 2004 o To deal with distribution cost, measuring individual cost elements together with their impact on customer service encourages tradeoffs that lead to a more effective and efficient distribution system Gunasekaran et. al., 2005 o Activities are difficult to trace because of the distributed nature of the virtual enterprise or supply chain environment o Many indirect costs will become direct costs and many direct costs will become indirect costs; Logistics costs are a major portion of the total cost o Many costs are hidden, and thus difficult to measure o Knowledge management and information technology costs will be major costs in the virtual enterprise or supply chain environment o A complex cost system will not likely work with the supply chain/virtual enterprise—a cost system similar to back flush costing may be suitable for new enterprise models III.2.6. Lacks and Limitations Cousins et. al., 2008 o A cross-sectional study is limited in its ability to study a concept, such as socialization and socialization mechanisms, which involve multiple actors over time. The lack of a longitudinal perspective means they cannot gain a detailed understanding of the effect of these variables over the life cycle of the supply relationship. o The focus of our research on strategic relationships between buyer-supplier firms limits the extent to which the findings can be generalized to this context. Future research could consider a broader relationship approach examining the interplay of performances measures and socialization across a range of inter-firm relationships, such as alliance partners.

Lai et. al., 2001 o The sample of respondents is all transport logistics service providers. The study assesses information only from the perspectives of transport logistics service providers. Consequently, it offers a self-reported, one-dimensional focus. o Respondents are asked to report the perceived Supply Chain Performance (SCP) of their companies as compared to the competition at a single point in time. Therefore, SCP in transport logistics on a temporal dimension cannot be measured. Cai J. et. al., 2009 o If the environment is changing drastically and frequently, mutually dependent relationships of the KPIs accomplishment may change dramatically and influence the accuracy of KPI cost transformation matrix (PCTM). o The procedural framework and PCTM analysis approach are applied in enterprises where supply chain management has already been actively deployed. o Results from PCTM should not be adopted as direct decisions, but as supporting information for decision-making. o It cannot influence the details of mechanisms of KPIs accomplishment. x Lockamy and McCormack, 2004 o It is not possible to make cross industry comparisons or to draw general conclusions about this relationship for all supply chain populations based on the presented results.

III.2.7. Supply Chain Metrics

Lambert and Pohlen, 2008 Problems o Many measures identified as supply chain metrics are actually measures of internal logistic operations as opposed to measures of supply chain management. o The majority are single firm logistics measures such as fill rate, lead time on-time performance of the supply chain. WHY? o The lack of measures that capture performance across the entire supply chain o The requirement to go beyond internal metrics and take a supply chain perspective o The need to determine the interrelationship between corporate and supply chain performance o The complexity of supply chain management o The requirement to align activities and share joint information measurement information to implement strategy that activities supply chain objectives. o The desire to expand the line of sight with in supply chaiThe requirement to allocate benefits and burdens resulting from functional shifts with in supply chain. o The need to differentiate the supply chain to obtain a competitive advantage o The goal of encouraging cooperative behavior across corporative functions and across firms in the supply chain Lohman et. al., 2004 o The scorecard does not anymore support the control of (a part of) the business––During performance review sessions it can appear that business areas or current or new challenges are not covered in the scorecard. Then additional requirements are formulated for the next edition of the scorecard. o The organizational objectives change––Since performance metrics are aimed at tracking the performance towards the organizational objectives, a change in strategy hits the heart of the scorecard.

Cuthbertson and Piotrowicz, 2008 o There was a lack of consensus regarding the measures used, so there was a lack of common measures. o Economic measures dominated, while social and environmental aspects were often ignored. Saad and Patel, 2006 Should be o Based on organization’s strategy and needs o Applicable to concepts such as Just in Time (JIT), Total Quality Management (TQM), SCM and other approaches used by the organizations o Intended for all employees o Lead to employee satisfaction o Flexible o Vary between locations of the organizations Otto and Kotzab, 2003 o Six complementary ways to measure System Dynamics, Operations and Research Perspective, Logistics, Marketing, Form an Organization point, Strategy

III.3. DESIGN of a NEW FRAMEWORK

III.3.1. First Framework The first framework was established by considering the five frameworks and good common characteristics that we found in literature analysis. In addition to this information, we add the Performance Attributes to this framework. In the following, we explained what performance attributes are. There is a detailed explanation about how the first framework was created and what its strengths and weaknesses are.

III.3.1.1. Performance Attributes There is a concept about “Performance Attributes” which were found in e-books that are existed in www.suuply-chain.org Performance Attributes are used to benchmark with competitors of companies. These are reliability, responsiveness, flexibility, cost and assets. In Figure III.6 also there is a definition of each attributes; this was taken from SCOR 8.0 Strategic-Operational Metrics e-book.

Figure III.6 Performance Attributes at Level 1

III.3.1.2. First Framework Developed After literature view and compassion frameworks that have been created in the literature, we wanted to take positive sides of frameworks. In addition to these, according to literature grouping key elements, we want to add some characteristics to the framework. To combine everything that we mentioned above, the first frameworks was created. In this framework, the company was divided in to two groups, the first one is internal of the company and the other one is external company. Also internal company was divided first according to performance attributes then each performance attribute was divided according to management levels which are shown in Figure III.7. Some of KPIs were taken from the other frameworks that will be explained deeply above and some of them were created for this framework. In this framework, most of internal KPIs were taken from Metrics for the Performance Evaluation of a Supply Chain (Gunasekaran et. al., 2001) and Supply Chain Performance Metrics Framework (Gunasekaran et. al., 2004). According to articles, we created for external KPIs.

Figure III.7 First framework

There is a detailed explanation of all internal KPIs: The first internal KPIs, which are under Reliability attributes, are Order Fulfillment, Delivery Reliability Performance and DriverReliability for Performance, respectively for Strategic, Tactical and Operational Management Level. Order Fulfillment was mentioned in SCOR model in Level 1. Gunasekaran et al., 2004 mentioned Delivery Reliability Performance in his “Supply Chain Performance Metrics Framework”. The operational one was mentioned in Metrics for the Performance Evaluation of a Supply Chain by Gunasekaran et. al., 2001. The second internal KPIs, which are under Responsiveness attributes, are Order Fulfillment Cycle Time, Responsiveness to urgent deliveries and Efficiency of purchase order cycle time, respectively Strategic, Tactical and Operational Management Level. Order Fulfillment Cycle Time mentioned in SCOR model in Level 1. Responsiveness to urgent deliveries and Efficiency of purchase order cycle time were mentioned in the article by Gunasekaran et. al., 2001. The third internal KPIs, which are under Flexibility attributes, are Upside Supply Chain Flexibility, Flexibility of service system to meet customer needs and Frequency of delivery, respectively Strategic, Tactical and Operational Management Level. Upside Supply Chain Flexibility was mentioned in SCOR model in Level 1. Flexibility of service system to

20 meet customer needs was mentioned in figure by Gunasekaran et. al., 2004. The last operational was mentioned in article by Gunasekaran et. al., 2001. The forth internal KPIs, which are under Cost attributes, are Cost of Goods Sold, Utilization of economic order quantity and Cost per operation hour, respectively Strategic, Tactical and Operational. Cost of Goods Sold was mentioned in SCOR model in Level 1. Utilization of economic order quantity was mentioned in figure by Gunasekaran et. al., 2004. The last operational one was mentioned in article by Gunasekaran et. al., 2001. The fifth internal KPIs, which are under Asset attributes, are Rate of return investment, Percentage of defects and Inventory days of supply. Rate of return investment was mentioned in article by Gunasekaran et. al., 2001. Percentage of defects was mentioned in article by Gunasekaran et. al., 2004. The last operational one was created for this framework. The external KPIs were created for this framework because in literature there is no framework which is included external perspective. It was easier to find KPIs for Employers, Customers and Suppliers rather than Investors, Partners and Regulations. KPI for Employers is Employers Satisfaction, for Customers, it is Customer Satisfaction and Loyalty and for Suppliers, it is Time to delivery product. After making brainstorming with my supervisor we decided to KPIs for the rest. For Investors KPI is Condition shares in the market. The most difficult one is for Partners because we could not decide whether the company is manufacturing or service. Then, we decided to focus on normal manufacturing company. Therefore, KPI of Partners is Number of product developed with partners. The last one is for Regulators is Sustainability of business. To sum up with, this framework has some positive sides such as: It considers management levels which authors mentioned about it in the literature as a strong feature, also external and internal perspective. The main difference between this framework and the other which are existed in literature is that this framework has also Performance Attributes. On the other hand, it can not give anything about SC; by this framework we can only consider our company. We have no idea about the relationship with the other parts of SC. In addition to that, a major holistic approach is needed. Because of these weaknesses, we deiced to add this framework SC aspect.

III.3.2. Second Framework The aim of this work is to develop a framework for SC performance measurement. However, the first framework has no issue about SC. Because of this, we wanted to add SC aspect to the first framework. One of the frameworks in literature called “SC Performance Metrics Frameworks” was built by Gunasekaran in 2004; he used the processes of Supply-Chain Operations Reference (SCOR) Model, make, plan, source and deliver. To focus on SC, following section gives the SCOR Model explanation.

III.3.2.1. SCOR Model

SCOR Model is the product of the Supply-Chain Council (SCC), an independent, not- for-profit, global corporation with membership open to all companies and organizations interested in applying and advancing the state-of-the-art in supply-chain management systems and practices. The SCOR-model captures the Council’s consensus view of supply chain management. While much of the underlying content of the Model has been used by practitioners for many years, the SCOR-model provides a unique framework that links business process, metrics, best practices and technology features into a unified structure to support communication among supply chain partners and to improve the effectiveness of supply chain management and related supply chain improvement activities.

The SCOR is a process reference model that has been developed and endorsed by the Supply-Chain Council as the cross-industry standard diagnostic tool for supply-chain management. SCOR enables users to address, improve and communicate supply-chain management practices within and between all interested parties. SCOR is a management tool. It is a process reference model for supply-chain management, spanning from the supplier's supplier to the customer's customer. The SCOR- model has been developed to describe the business activities associated with all phases of satisfying a customer's demand. By describing supply chains using process building blocks, the Model can be used to describe supply chains that are very simple or very complex using a common set of definitions. As a result, disparate industries can be linked to describe the depth and breadth of virtually any supply chain. The Model has been able to successfully describe and provide a basis for supply chain improvement for global projects as well as site-specific projects. The process reference model integrates the well-known concepts of business process re-engineering, benchmarking, and process measurements into a cross-functional framework. Each of the four processes at the top level is successively divided into sub-processes, first at a configuration level, then at a process element level as shown in Figure III.8. Finally, at the fourth level and beyond the scope of the SCOR model, activities are defined by companies individually. Measures are defined for all processes at the three top levels, and firms provide information about how they perform while receiving a benchmark in return against which they can compare their own performance. This model provides not only an opportunity to see how the firm is doing, but also a common frame of reference and a common language across the supply chain.

III.3.2.1.1. Features of SCOR 1.a SCOR Spans o All customer interactions, from order entry through paid invoice o All product (physical material and service) transactions, from your supplier’s supplier to your customer’s customer, including equipment, supplies, spare parts, bulk product, software, etc. o All market interactions, from the understanding of aggregate demand to the fulfillment of each order

Figure III.8 The SCOR Model described at different level of details

1.b SCOR does not attempt to describe every business process or activity, including o Sales and marketing (demand generation)

o Research and technology development o Product development o Some elements of post-delivery customer support Links can be made to processes not included within the model’s scope, such as product development, and some are noted in SCOR.

1.c SCOR assumes but does not explicitly address o Training o Quality o Information Technology (IT) o Administration (non SCM)

III.3.2.1.2. Processes of SCOR SCOR is based on Five Distinct Management Processes, PLAN, SOURCE, MAKE, DELIVER and RETURN, as shown in Figure III.9.

Figure III.9 SCOR model

2.a. PLAN: Demand/Supply Planning and Management PLAN can be processes that balance aggregate demand and supply to develop a course of action which best meet sourcing, production and delivery requirements. o Balance resources with requirements and establish/communicate plans for the whole supply chain, including Return, and the execution processes of Source, Make, and Deliver. o Management of business rules, supply chain performance, data collection, inventory, capital assets, transportation, planning configuration, regulatory requirements and compliance, and supply chain risk. o Align the supply chain unit plan with the financial plan.

2.b. SOURCE: Sourcing Stocked, Make-to-Order, and Engineer-to-Order Product SOURCE can be processes that procure goods and services to meet planned or actual demand o Schedule deliveries; receive, verify, and transfer product; and authorize supplier payments. o Identify and select supply sources when not predetermined, as for engineer-to-order product. o Manage business rules, assess supplier performance, and maintain data. o Manage inventory, capital assets, incoming product, supplier network, import/export requirements, supplier agreements, and supply chain source risk.

2.c. MAKE: Make-to-Stock, Make-to-Order, and Engineer-to-Order Production MAKE can be processes that transform product to a finished state to meet planned or actual demand. (Execution) o Schedule production activities, issue product, produce and test, package, stage product, and release product to deliver. With the addition of Green to SCOR, there are now processes specifically for Waste Disposal in MAKE. o Finalize engineering for engineer-to-order product. o Manage rules, performance, data, in-process products (WIP), equipment and facilities, transportation, production network, regulatory compliance for production, and supply chain make risk

2.d. DELIVER: Order, Warehouse, Transportation, and Installation Management for DELIVER can be processes that provide finished goods and services to meet planned or actual demand, typically including order management, transportation management, and distribution management. (Stocked, Make-to-Order, and Engineer-to-Order Product) o All order management steps from processing customer inquiries and quotes to routing shipments and selecting carriers. o Warehouse management from receiving and picking product to load and ship product. o Receive and verify product at customer site and install, if necessary. o Invoicing customer. o Manage Deliver business rules, performance, information, finished product inventories, capital assets, transportation, product life cycle, import/export requirements, and supply chain deliver risk.

2.e. RETURN: Return of Raw Materials and Receipt of Returns of Finished Goods RETURN can be processes associated with returning or receiving returned products for any reason. These processes extend into post-delivery customer support. o All Return Defective Product steps from source – identify product condition, disposition product, request product return authorization, schedule product shipment, and return defective product – and deliver – authorized product return, schedule return receipt, receive product, and transfer defective product. o All Return Maintenance, Repair, and Overhaul product steps from source – identify product condition, disposition product, request product return authorization, schedule product shipment, and return MRO product – and deliver – authorize product return, schedule return receipt, receive product, and transfer MRO product. o All Return Excess Product steps from source – identify product condition, disposition product, request product return authorization, schedule product shipment, and return excess product – and deliver – authorize product return, schedule return receipt, receive product, and transfer excess product. o Manage Return business rules, performance, and data collection, return inventory, capital assets, transportation, network configuration, regulatory requirements and compliance, and supply chain return risk.

III.3.2.2. Second Framework Developed By adding the first framework to SCOR Model, we got the second framework which is shown in Figure III.10. In this framework, we put the first framework in the middle of SCOR Model as representing the inside of company. It is seen that the performance Attributes, management Levels and its KPIs inside the company. We also put the first framework to the inside of all parts in SC to show that every part of SC is another company as themselves. Make which is all process inside the company. Return and Source link the company to its

Supplier. Also, Deliver and Return link the company to its Customers. The last one is Plan can combine every part of SC. These processes come from SCOR Model which is mentioned above. Outside the company there are external perspectives like Partners, Investors, Employers and Regulators. Their KPIs are shown under them. When focusing side of Suppliers, it is obviously seen Supplier’s KPI and its Performance Attributes. On the other hand, it is understandable from its Make, Return, Source and Deliver processes that Supplier is also another company inside. These are same for the Supplier’sSupplier. When focusing on the side of Customers, it is obviously seen Customer’s KPI and its Performance Attributes. Being another company in SC is the same for Customer and Customer’s Customer. Even if every part of SC is own features inside the companies, when looking the big screen there is a huge picture. PLAN considers the whole SC because to reach the most beneficial SC, it is better to plan everything according to every part of SC. After considering the picture, it is beneficial to link each Performance Attributes for each company under Global Supply (GS) title. That is why the collection each attributes under Global leads to see Global level of SC. For increasing the efficiency of framework, addition of GS Reliability, GS Responsiveness, GS Flexibility, GS Cost and GS Assets shows the condition of whole supply chain for making better benchmarking. The main strength of the second frameworks is considering whole SC, which we wanted to reach before building framework. Also with this framework we can find Global Performance Attributes which is really creative when we compare other frameworks. On the other hand, the framework has not been tested so validation with case studies is needed.

Figure III.10 Second Framework with SCOR model

PART IV

RESULTS

Furthermore, in thesis part after reading articles common characteristics of articles were grouped according to their authors. Then, literature analysis was made by analyzing five main frameworks were created in different years. The table about the comparison of these frameworks was created according to their strengths and weaknesses. In conclusion, relevant literature on PM and SCM had been reviewed and analyzed. By investigating each framework in literature, we created strengths and weaknesses of each of them. After according to this table, by considering strengths at the same time avoiding weaknesses, we created the first framework. The first framework was created by using management level and internal and external aspects. Also, we used performance attributes, Reliability, Responsiveness, Flexibility, Cost and Asset in this framework. But this framework was not considering the SC parts. Then we combined this framework with SCOR Model and we got the last framework which can show us whole SC and Global Performance Attributes is shown in Figure III.8. Also while building the frameworks, we found proper KPIs to measure SC performance. The result of this paper is to have a new framework for general manufacturing company by using internal and external aspects, management level, performance attributes and SCOR Model. Also academic paper about this work was written.

PART V

DISCUSSONS AND EVALUATIONS

V.1. DISCUSSIONS When considering the final framework, mangers can see the big picture. For example, Global supply Reliability, Responsiveness, Flexibility, Cost and Asset give the opportunity of reaching whole aspects of supply chain. These attributes come from not only the own company but also supplier and supplier’s supplier and customer and customer’s customer. In addition to that, framework can give us not only internal aspects but also external aspects like its partners, regulators, employers and investors. Also inside the company, it is shown that KPIs were classified according to performance attributes and management levels. In this framework there are many creative things. Firstly, in literature there is no framework which considers management level at the same time performance attributes. Secondly, there is no framework which considers external issues like Partners, Investors, Regulators and Employers at the same time in SCOR Model. Finally, it is really extraordinary framework that combines all SC parts at the same time measures each Performance Attributes for whole supply chain called GS Performance Attributes.

V.2. EVALUATIONS In this part, there is an evaluation of the created framework in this paper by making comparison with the other frameworks that have mentioned above. The main positive side of this framework is to have many different features at the same time. For example, SCOR Model is found by many companies as a useful tool. But they cannot measure Performance Attributes when they use SCOR Model. In this model, it is easy to reach to all part of the SC which is the aim of this paper to measure its performance. On the other hand, when considering the other frameworks in literature, they focus on some specific aspect such as financial and non-financial aspects at the same time management levels. Or, one of them focused on management level and SCOR Model process. These are simpler frameworks when making comparison. The only negative side of this framework is that it has not been tested yet. Also, before using this framework, managers should pay more attention in details. Because there is no validation of framework, this framework will not be preferred firstly by managers. However, after using they can realize that this framework is more useful than the others.

V.3. SUGGESTIONS The paper suggests that is better use the framework in general manufacturing company. My suggestion is that for the first time using the frameworks in a small company should be more effective

V.4. FUTURE WORKS For future works this framework will be used in general manufacturing company and multiple case studies is need for validation of the framework. By means of using this in real company, the convenience of framework will be considered. After case study results, some changes can be done for making more convenient for real companies.

REFERENCES

BOOKS 1. Argyris C.: “The Impact of Budgets on People, Controllership Foundation”, New York, NY. (1952) 2. Chandler A.D.: “The Visible Hand - Managerial Revolution in American Business”, Harvard University Press, Boston, MA. (1977) 3. Dixon J., Nanni A.; Vollmann T.: “The New Performance Challenge, Business OneIrwin”, Burr Ridge, IL. (1990) 4. Drucker P.: “The Practice of Management, Harper”, New York, NY. (1954) 5. Fitzgerald L.; Johnston R.; Brignall S.; Silvestro R.; Voss C.: “Performance Measurement in Service Business”, CIMA, London (1991) 6. Johnson H.T., Kaplan R.S.: “Relevance Lost – The Rise and Fall of Management Accounting”, Harvard Business School Press, Boston, MA. (1987) 7. Lynch R.L.; Cross K.F.: “Measure up!”, Blackwell Publishers, Cambridge, MA. (1991) 8. Marr B.; Neely A.D.; Franco M.; Wilcox M.; Adams C.; Mason S.: “Business performance measurement: what is the state of the art”, Proceedings of the 4th International Conference on Performance Measurement, Edinburgh (2004) 9. Neely A.D.; Mills J.F.; Gregory M.J.; Richards A.H.; Platts K.W.; Bourne M.C.S.: “Getting the Measure of your Business”, Findlay Publications, Horton Kirby. (1996) 10. Power M.: “The Audit Society: Rituals of Verification”, Oxford University Press, Oxford (1997) 11. Stadtler H.; Kilger C.: “Supply Chain Management and Advanced Planning”, Springer – Verlag Berlin, Germany (2002) 7-9

WEB SITES 1. www.supply-chain.org (April 2009)

E-BOOKS 1. SCOR 8.0 Strategic-Operational Metrics 2. SCOR 9.0 Overview Booklet

THESIS 1. Angerhofer J.B.; Angelides M.C.: “A model and a performance measurement system for collaborative supply chains”, Decision Support System, Vol.42 Isuue.1 (2006) 238-301 2. Beamon B.M.: “Measuring Supply Chain Performance”, International Journal of Operations and Production Management, Vol.19 Issue.3 (1999) 275-292 3. Bourne M.; Mills J.; Wilcox, M.; Neely A.; Platts K.: “Designing, implementing and updating performance measurement systems”, International Journal of Operations and Production Management, Vol.20 Issue.7 (2000) 754-771.

4. Cai J.; Liu X.; Xiao Z.; Liu J.: “Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment”, Decision Support Systems, Vol.46 Issue.2 (2009) 512-521 5. Caplice C.; Sheffi Y.: “A Review and Evaluation of Logistics Performance Measurement Systems”, The International Journal of Logistics Management, Vol.6 Issue.1 (1995) 61-74 6. Chan F.T.S.; Qi H.J: “Feasibility of performance measurement system for supply chain: a process-based approach and measures”, Integrated Manufacturing Systems, Vol.14 Isuue.3 (2003) 179-190 7. Cook M.; Hagey R.: “Why Companies Flunk Supply Chain 101: Only 33 Percent Correctly Measure Supply Chain Performance; Few Use the Right Incentives”, Journal of Business Strategy, Vol.24 Issue.4 (1998) 35-42 8. Cousins P.D.; Lawson B.; Squire B.: “Performance measurement in strategic buyer- supplier relationships The mediating role of socialization mechanisms”, International Journal of Operations and Production Management, Vol.3 Issue.28 (2008) 238-258 9. Cuthbertson R.; Piotrowicz W.: ”Supply chain best practices – identification and categorization of measures and benefits”, International Journal of Productivity and Performance Management, Vol.57 Issue.5 (2008) 389-404 10. Gunasekaran A.; Patel C.; Tirtiroglu E.: “Performance measures and metrics in a supply chain environment”, International Journal of Operations and Production Management, Vol.21 Issue.1/2 (2001) 71-87 11. Gunasekaran A.; Patel C.; Mc Gaughey R.E.: “A framework for supply chain performance measurement”, International Journal of Production Economics, Vol.87 Issue.3 (2004) 333-347 12. Gunasekaran A.; Williams H.J; Mc Gaughey R.E.: “Performance measurement and costing system in new enterprise”, Technovation, Vol.25 Isse.5 (2005) 523-533 13. Hayes R.H.; Abernathy W.J.: “Managing our way to economic decline”, Harvard Business Review, July-August (1980) 67-77 14. Kaplan R.S.; Norton D.P.: “The balanced scorecard: measures that drive performance”, Harvard Business Review, January-February, (1992) 71-79. 15. Kaplan R.S.; Nortan D.P.: “Using the balanced scorecard as a strategic management system”, Harvard Business Review, Vol.74, Issue.1 (1996) 20-25 16. Keegan D.P.; Eiler R.G.; Jones C.R.: ”Are your performance measures obsolete?”, Management Accounting, June, (1989) 45-50 17. Lai K.; Ngai E.W.T.; Cheng T.C.E.: “Measures for evaluating supply chain performance in transport logistics”, European Journal of Operational Research, Vol.133 Issue.1 (2001) 439-456 18. Lambert M.D.; Pohlen T.L.: “Supply Chain Metrics”, International Journal of Logistic Management, Vol.12 Issue.1 (2001) 1-19 19. Lee H.; Billington C.: “Managing supply chain inventory: pitfalls and opportunities”, Sloan Management Review, Spring (1992) 65-73 20. Lockamy A.; McCormack K.: ”Linking SCOR planning practices to supply chain performance: An exploratory study”, International Journal of Operations and Production Management, Vol.24 Issue.12 (2004) 1192-1218 21. Lohman C.; Fortuin L.; Wouters M.,: “Designing a performance measurement system A Case Study”, European Journal of Operational Research, Vol.156 Issue.2 (2004) 267-286 22. Morgan C.: “Structure, Speed and Salience: Performance Measurement in the Supply Chain”, Journal of Business Process Measurement, Vol.10 Issue.5 (2004) 522-536 23. Morgan C.: “Supply network performance measurement: future challenges?”, International Journal of Logistic Management, Vol.1 Issue.2 (2007) 255-273

24. Muratoglu K.: “Performance Measurement of Supply Chain Via Balanced Scorecard: The Case of a Brewing Group”, The Business Review Cambridge, Vol.10 Issue.1 (2008) 330-337 25. Neely A: “The evolution of performance measurement research: Developments in the last decade and research agenda for the next”, International Journal of Operations and Production Management, Vol.25 Issue.12 (2005) 1264-1277 26. Otto A.; Kotzab H.: “Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain”, European Journal of Operational Research, Vol.114 Isuue.2 (2003) 306-320 27. Parker C.: “Performance Measurement”, Work Study, Vol.49 Issue.2 (2000) 63-66 28. Rigby D.; “Management tools and techniques: a survey”, California Management Review, Vol.43 Issue. 2 (2001) 139-60. 29. Ritchie B.; Brindley C.: “An emergent framework for supply chain risk management”, The Journal of the Operational Research Society, Vol.58 Issue.11 (2007) 1398-1411 30. Robson I.: “From Process Measurement to Performance Improvement”, Journal of Business Process Management, Vol.10, Issue.5 (2004) 50-512 31. Ridgway V.F.: “Dysfunctional consequences of performance measurements”, Administrative Science Quarterly, Vol.1 Issue.2 (1956) 240-247. 32. Saad M.; Patel B.: “An investigation of supply chain performance measurement in the Indian automotive sector”, International Journal of Benchmarking, Vol.13 Isuue.1/2 (2006) 36-53 33. Seth N.; Deshmukh S.G.; Vrat P.: ”A framework for measurement of quality of service in supply chains”, International Journal of Supply Chain Management, Vol.11 Issue.1 (2006) 82-94 34. Silk S.: “Automating the balanced scorecard”, Management Accounting, Vol.79 Issue.11, (1998) 38-44. 35. Speckbacher G.; Bischof J.; Pfeiffer T.: “A descriptive analysis of the implementation of balanced scorecards in German speaking countries”, Management Accounting Research, Vol.14 (2003) 361-387 36. Tarr J.D: “Performance Measurements for a Continuous Improvement Strategy”, Hospital Material Management Quarterly, Vol.18 Issue.2 (1996) 77-85 37. Theeranuphattana A.; Tang J.C.S.: “A conceptual model of performance measurement for supply chains alternative considerations”, Journal of Manufacturing Technology Management, Vol.19 Issue.1 (2007) 125-148 38. Williams M.S.; “Are intellectual capital performance and disclosure practices related?”, Journal of Intellectual Capital, Vol. 2 Issue. 3, (2001) 192-203