Forecasting Short Term Demand in Heterogeneous Customer Oriented Demand Management Processes
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POLITECNICO DI MILANO Faculty of Engineering Ph.D. in Management Engineering - XV cycle FORECASTING SHORT TERM DEMAND IN HETEROGENEOUS CUSTOMER ORIENTED DEMAND MANAGEMENT PROCESSES Matteo Kalchschmidt Thesis Advisor: Prof. Roberto Verganti Chair, Ph.D. Program in Management Engineering: Prof. Giuliano Noci December 2002 Acknowledgments Even if this work has been written by a single person, it is the result of the different contributions of a much wider number of people. In first place, I would like to thank the whole research group of Business and Innovation Management within the Department of Industrial Engineering at Politecnico di Milano. In particular a special thank is for Professor Roberto Verganti and Professor Emilio Bartezzaghi that helped me out from this hard work and that made me understand why it was so important. Special thanks to Gianluca, Mariano, Raffaella, Stefano, Tommaso, Federico and Alessio, for letting me participate to their experiences and for participating to mine. A particular remark goes to Giulio Zotteri, which I have to thank and blame for pulling me into this adventure. Special thanks go to my family and my love, that even if not in a scientific perspective, for sure contributed to this work. I would also like to thank all the people belonging to the companies here reported (and to those that are not), that helped me in the development of this work. He cerrado mi balcón por que no quiero oír el llanto pero por detrás de los grises muros no se oye otra cosa que el llanto. Hay muy pocos ángeles que canten, hay muy pocos perros que ladren, mil violines caben en la palma de mi mano. Pero el llanto es un perro inmenso, el llanto es un ángel inmenso, el llanto es un violín inmenso, las lágrimas amordazan al viento, no se oye otra cosa que el llanto. Federico García Lorca (1936) Contents Introduction 1 PART I: LITERATURE REVIEW Chapter 1: Customer Orientation 9 1 Introduction to the Problem 9 2 Customer Orientation 13 3 Customer Orientation in Supply Chain Management 19 3.1 Supply Chain Structure 21 3.2 Industrial Networks 22 3.3 Customer Oriented Supply Chains 23 3.3.1 Mass Customization 24 3.3.2 Postponement 26 3.3.3 Customer Relationship Management 27 3.4 Demand Chain Management 28 3.5 Customer Oriented Inventory Management 30 4 Conclusions 34 I Contents Chapter 2: Demand Forecasting and Customer 35 Orientation 1 Demand Management 35 1.1 Demand Management Process 36 1.2 Uncertainty Management: the case of lumpy demand 39 2 Demand Forecasting 42 2.1 Methods for Stable Demand based on Demand Data 45 2.1.1 Smoothing and Average based Techniques 46 2.1.2 Bivariate and Multivariate models 46 2.1.3 ARIMA methods 47 2.2 Methods for Lumpy Demand based on Demand Data 49 2.2.1 Croston Method 49 2.2.2 Syntetos and Boylan Method 52 2.3 Methods for Lumpy Demand based on Information regarding 54 Demand Generation Process 2.3.1 Lost Sales Estimation 56 2.3.2 Analysis of Reliability 57 2.3.3 Early Sales 58 2.3.4 Order Overplanning 64 2.3.5 Multi Level Supply Control 67 PART II: THEORY AND METHODOLOGY Chapter 1: Research Aims and Methodology 71 1 General Considerations 71 1.1 Factors influencing demand uncertainty and heterogeneity 72 1.2 Effects on Customers’ Structure 78 1.3 Limitations of Actual Forecasting Approaches 82 2 Definition of Research Aims 87 3 Research Methodology 88 3.1 Theoretical Stage 88 3.2 Empirical Stage 90 Chapter 2: Theoretical Framework 93 1 Heterogeneity: a definition 93 1.1 Heterogeneity Dimensions 96 1.2 Heterogeneity Measures 99 2 Impact of Heterogeneity on Demand 103 II Contents 2.1 Customer Size 104 2.2 Purchasing Politics 106 2.2.1 Lot Sizing 106 2.2.2 Reorder Interval 110 2.3 Promotional Politics 111 2.3.1 Number of Promotions 112 2.3.2 Promotion Size 113 3 Heterogeneity and Forecasting 115 4 Conclusions 122 PART III: EMPIRICAL ANALYSIS Chapter 1: Introduction to the Action Research Case 125 Chapter 2: Whirlpool Europe 131 1 Context and Purpose 131 2 Introduction to the company 133 2.1 Whirlpool Europe Spare Parts Centre 136 3 Analysis of the Supply Chain and Demand 138 3.1 The Evolution of the Supply Chain Structure 138 3.2 Impact of the Supply Chain structure on Demand Variability 140 4 Solution Design 146 4.1 Alternative a): Current Solution 146 4.2 Alternative b): Literature model 147 4.2.1 Filtering 147 4.2.2 Forecasting 151 4.3 Alternative c): Ad hoc model 152 4.3.1 Forecasting 152 4.3.2 Inventory Management 157 4.4 Alternative d): improving performances through information 159 5 Performance 161 6 Conclusion 166 Chapter 3: Nestlé Italiana 169 1 Context and Purpose 169 2 Introduction to the company 170 2.1 Nestlè Italiana 172 III Contents 2.2 The Fresh Food Division 173 2.3 Logistic Structure 174 3 Analysis of the Supply Chain and Demand 178 3.1 Analysis of demand variability 178 3.2 Actual forecasting performances 182 4 Solution Design 186 4.1 Forecasting for the 22 monitored customers 187 4.2 Residual customers forecasting approach 194 4.2.1 Aggregate Solution 194 4.2.2 Cluster Based Solution 197 5 Cluster analysis simulation 205 5.1 Simulation 1 207 5.2 Simulation 2 208 5.3 Simulation 3 209 5.4 Simulation results 209 5.5 Application to Nestlè 212 6 Conclusions 214 Chapter 4: Ahold 219 1 Context and Purpose 219 2 Introduction to the company 223 2.1 Ahold History 223 2.2 Description of the Supply Chain 225 2.3 Actual Forecasting System 227 3 Solution Development 230 3.1 Disaggregate Model 230 3.2 Mixed Model 231 3.3 Aggregate Model 233 3.4 Cluster Model 234 4 Analysis of results 238 5 Conclusions 244 Chapter 5: Conclusions and Future Developments 249 1 Analysis of the Action Research results 249 1.1 Demand management approaches within heterogeneous 250 contexts 1.2 Main elements to be considered 256 2 Generalization of findings 260 3 Final remarks 262 4 Future developments 265 IV Contents References 267 V Introduction In many industrial contexts firms are dealing with a demand that is ever more uncertain. The reasons tied to this phenomenon are many, however, a major change that is spreading among different sectors is the ever growing attention towards customers. In fact, companies have identified that customers are critical for their businesses not only because they influence directly the success of a specific product or firm, but also because their role is fundamental within many different internal processes. As a matter of fact, firms are paying a ever greater attention towards customers’ influence on business process. Attention is typically given in the product development field, where literature claims that customers have to be involved in the early stage of the product development. Customer orientation is a relevant issue in the marketing-related theory and practice, where attention by both researchers and practitioners has been paid on the proper interaction with customers, thus leading to the so-called Interactive or Direct Marketing. Even if the customer role within business process have been deeply analyzed, some research field have not yet exploited this issue completely. Attention in this work is given towards demand forecasting and the role of customer when dealing with heterogeneous contexts. In particular, our main attention is paid towards contexts where companies have to manage different customers influenced by different factors and that react differently towards similar external variables. 1 Introduction As a matter of fact, companies’ attention also within the forecasting issue is changing towards the problem of dealing with heterogeneous customers. Many firms are aware that due to the changing environment, customers tend to behave differently and that serving properly market with suited solutions is becoming fundamental to properly compete. However, managing heterogeneous contexts becomes critical since it tends to make more difficult understanding and foretelling future market requirements, thus claiming that contributions towards forecasting matters are required. In particular, this work focuses on short term demand forecasting, thus attention is given towards estimating future market requirements for planning purposes. Within the described context, this research aims at studying the impact of heterogeneity among customers’ requests and to design both a proper methodology and specific approaches to cope with demand in heterogeneous contexts. In particular, three main research questions are considered in this work. How does heterogeneity in customers’ purchasing processes influence demand variability and forecasting effectiveness? How should demand be managed when dealing with heterogeneous contexts? What are the main elements that should be taken into account when dealing with heterogeneous contexts? To properly answer these questions, this work has been structured in three stages corresponding to the different parts in which the work is structured. First of all a wide analysis of current literature has been conducted and contributions have been deeply reviewed and analyzed. In particular attention has been paid towards from one side customer-orientation and its impact within business processes, while, from the other, demand management and forecasting contributions have been considered. This analysis let us define the problem addressed and state some of the research hypothesis this work addresses. Given this framework the empirical methodological can be applied. In particular, the research is based on a two-stage process; the first stage aims at analyzing heterogeneity and its impact on demand variability and forecasting accuracy. We conducted this analysis 2 Introduction by means of simulation, in particularly by simulating different sorts of heterogeneity and measuring the impact on demand variability.