Postponement Strategies in Supply Chain Management / Edition 1

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More About This Textbook


Within supply chain management (SCM), Postponement is a deliberate action to delay final manufacturing or distribution of a product until receipt of a customer order. This reduces the incidence of wrong manufacturing or incorrect inventory deployment. Postponement strategies and practices serve to reduce the anticipatory risk in a supply chain. It can be fine-tuned or staged so that only the generic parts shared by a firm's various end products are warehoused, used only once orders come in for whichever products are selling, and will reduce inventory pressures throughout the firm. Despite much existing research in the area, no one book devoted solely to postponement has been published.

At is core, Postponement Strategies in Supply Chain Management analyzes how both pull postponement strategy and form postponement strategy can be leveraged to yield substantial benefits to adopting firms in different competitive environments. This book is intended for researchers in supply chain management interested in conducting in-depth studies on postponement strategies. It is also intended for practitioners trying to understand the workings of postponement strategies and looking for guidance and decision support for the implementation of postponement strategies. Therefore, the book can be useful not only for researchers but also for practitioners and graduate students in operations management, management science, and business administration.

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Product Details

Table of Contents

1 Introduction 1

1.1 From Product Variety to Postponement 1

1.1.1 Product Variety 1

1.1.2 Mass Customization 2

1.1.3 Postponement Strategy 3

1.2 Classification of Postponement 3

1.2.1 Pull Postponement 4

1.2.2 Logistics Postponement 6

1.2.3 Form Postponement 7

1.2.4 Price Postponement 8

1.2.5 Implications 8

1.2.6 Advantages and Disadvantages of Postponement 9

1.2.7 Prerequisites for Postponement Strategy Development 10

1.3 Cost Models for Analyzing Postponement Strategies 11

1.3.1 Stochastic Models 11

1.3.2 Heuristic Models 12

1.3.3 Descriptive Models 13

1.3.4 Performance Measures 14

1.4 A Literature Review for Model Development 14

1.4.1 EOQ and EPQ Models 15

1.4.2 Lot Size-Reorder Point Model 16

1.4.3 Markov Chain 16

1.5 Concluding Remarks 17

2 Analysis of Pull Postponement by EOQ-based Models 19

2.1 Postponement Strategy for Ordinary (Imperishable) Items 19

2.1.1 Proposed Model and Assumptions 19

2.1.2 Case 1: Same Backorder Cost 22

2.1.3 Case 2: Different Backorder Costs 26

2.1.4 A Numerical Example 30

2.2 Postponement Strategy for Perishable Items 32

2.2.1 Notation and Assumptions 33

2.2.2 Model Formulation 34

2.2.3 The Postponement and Independent Systems 38

2.2.4 Numerical Examples 39

2.3 Concluding Remarks 41

3 Analysis of Postponement Strategy by EPQ-based Models 43

3.1 Analysis of Postponement Strategy by an EPQ-based Model without Stockout 43

3.1.1 Proposed Model and Assumptions 43

3.1.2 2 Machines for 2 End-Products 46

3.1.3 n Machines for n End-Products 56

3.2 Analysis of Postponement Strategy by an EPQ-based Model with Planned Backorders 62

3.2.1 Proposed Model and Assumptions 63

3.2.2 Demands Are Met Continuously 65

3.2.3 Demands Are Met After Production Is Complete 71

3.3 Concluding Remarks 78

4 Evaluation of a Postponement System with an (r, q) Policy 81

4.1 The Proposed Models and Assumptions 81

4.2 System Dynamics for a Non-postponement System 83

4.3 The Algorithm for Finding a Near Optimal Total Average Cost of an (r, q) Policy 84

4.3.1 The Markov Chain Development 84

4.3.2 The Algorithm for Finding a Near Optimal Total Average Cost 99

4.4 System Dynamics for a Postponement System 102

4.5 Average Cost Comparison of the Two Systems When L = 0 103

4.6 Average Cost Comparison of the Two Systems When L ≥ 1 104

4.6.1 An Overview of the Simulation Results 104

4.6.2 Impacts of Parameters on Average Cost 106

4.7 Concluding Remarks 107

5 Simulation of a Two-End-Product Postponement System 109

5.1 Proposed Model and Assumptions 110

5.1.1 Notation 111

5.1.2 Model Assumptions 111

5.2 Methodology 112

5.2.1 System Dynamics 112

5.2.2 The Simulation Model 114

5.2.3 Customer Demand Distribution 114

5.2.4 Order Quantity and Reorder Point 115

5.2.5 Summary of Parameters 115

5.2.6 Initial Conditions 115

5.3 Simulation Results for Non-cost Parameters 117

5.3.1 Uniform Distribution 117

5.3.2 Poisson Distribution 118

5.3.3 Normal Distribution I 119

5.3.4 Normal Distribution II 120

5.4 Simulation Results for Cost Parameters 121

5.5 Concluding Remarks 123

6 Application of Postponement: Examples from Industry 125

6.1 A Case Study from Hong Kong 125

6.1.1 An Overview of the Company 126

6.1.2 Implementation of Postponement 126

6.1.3 Benefits of Using Postponement 127

6.1.4 Implications 128

6.2 The Case of Taiwanese Information Technology Industry 129

6.2.1 The Hypothesis 129

6.2.2 Methodology 130

6.2.3 Results 131

6.2.4 Implications 131

6.3 Concluding Remarks 132

7 Conclusions, Implications and Future Research Directions 133

7.1 Conclusions 133

7.2 Implications and Further Research Directions 134

A Simulation Results (Uniform Distribution) 137

B Simulation Results (Poisson Distribution) 141

C Simulation Results (Normal Distribution I) 147

D Simulation Results (Normal Distribution II) 151

E Simulation Results for Cost Analysis 155

References 157

About the Authors 163

Index 165

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