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Postponement Strategies in Supply Chain Management 2010 [Hardback]

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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 its 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. The 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.
1 Introduction
1(18)
1.1 From Product Variety to Postponement
1(2)
1.1.1 Product Variety
1(1)
1.1.2 Mass Customization
2(1)
1.1.3 Postponement Strategy
3(1)
1.2 Classification of Postponement
3(8)
1.2.1 Pull Postponement
4(2)
1.2.2 Logistics Postponement
6(1)
1.2.3 Form Postponement
7(1)
1.2.4 Price Postponement
8(1)
1.2.5 Implications
8(1)
1.2.6 Advantages and Disadvantages of Postponement
9(1)
1.2.7 Prerequisites for Postponement Strategy Development
10(1)
1.3 Cost Models for Analyzing Postponement Strategies
11(3)
1.3.1 Stochastic Models
11(1)
1.3.2 Heuristic Models
12(1)
1.3.3 Descriptive Models
13(1)
1.3.4 Performance Measures
14(1)
1.4 A Literature Review for Model Development
14(3)
1.4.1 EOQ and EPQ Models
15(1)
1.4.2 Lot Size-Reorder Point Model
16(1)
1.4.3 Markov Chain
16(1)
1.5 Concluding Remarks
17(2)
2 Analysis of Pull Postponement by EOQ-based Models
19(24)
2.1 Postponement Strategy for Ordinary (Imperishable) Items
19(13)
2.1.1 Proposed Model and Assumptions
19(3)
2.1.2 Case 1: Same Backorder Cost
22(4)
2.1.3 Case 2: Different Backorder Costs
26(4)
2.1.4 A Numerical Example
30(2)
2.2 Postponement Strategy for Perishable Items
32(9)
2.2.1 Notation and Assumptions
33(1)
2.2.2 Model Formulation
34(4)
2.2.3 The Postponement and Independent Systems
38(1)
2.2.4 Numerical Examples
39(2)
2.3 Concluding Remarks
41(2)
3 Analysis of Postponement Strategy by EPQ-based Models
43(38)
3.1 Analysis of Postponement Strategy by an EPQ-based Model without Stockout
43(19)
3.1.1 Proposed Model and Assumptions
43(3)
3.1.2 2 Machines for 2 End-Products
46(10)
3.1.3 n Machines for n End-Products
56(6)
3.2 Analysis of Postponement Strategy by an EPQ-based Model with Planned Backorders
62(16)
3.2.1 Proposed Model and Assumptions
63(2)
3.2.2 Demands Are Met Continuously
65(6)
3.2.3 Demands Are Met After Production Is Complete
71(7)
3.3 Concluding Remarks
78(3)
4 Evaluation of a Postponement System with an (r, q) Policy
81(28)
4.1 The Proposed Models and Assumptions
81(2)
4.2 System Dynamics for a Non-postponement System
83(1)
4.3 The Algorithm for Finding a Near Optimal Total Average Cost of an (r, q) Policy
84(18)
4.3.1 The Markov Chain Development
84(15)
4.3.2 The Algorithm for Finding a Near Optimal Total Average Cost
99(3)
4.4 System Dynamics for a Postponement System
102(1)
4.5 Average Cost Comparison of the Two Systems When L = 0
103(1)
4.6 Average Cost Comparison of the Two Systems When L ≥ 1
104(3)
4.6.1 An Overview of the Simulation Results
104(2)
4.6.2 Impacts of Parameters on Average Cost
106(1)
4.7 Concluding Remarks
107(2)
5 Simulation of a Two-End-Product Postponement System
109(16)
5.1 Proposed Model and Assumptions
110(2)
5.1.1 Notation
111(1)
5.1.2 Model Assumptions
111(1)
5.2 Methodology
112(5)
5.2.1 System Dynamics
112(2)
5.2.2 The Simulation Model
114(1)
5.2.3 Customer Demand Distribution
114(1)
5.2.4 Order Quantity and Reorder Point
115(1)
5.2.5 Summary of Parameters
115(1)
5.2.6 Initial Conditions
115(2)
5.3 Simulation Results for Non-cost Parameters
117(4)
5.3.1 Uniform Distribution
117(1)
5.3.2 Poisson Distribution
118(1)
5.3.3 Normal Distribution I
119(1)
5.3.4 Normal Distribution II
120(1)
5.4 Simulation Results for Cost Parameters
121(2)
5.5 Concluding Remarks
123(2)
6 Application of Postponement: Examples from Industry
125(8)
6.1 A Case Study from Hong Kong
125(4)
6.1.1 An Overview of the Company
126(1)
6.1.2 Implementation of Postponement
126(1)
6.1.3 Benefits of Using Postponement
127(1)
6.1.4 Implications
128(1)
6.2 The Case of Taiwanese Information Technology Industry
129(3)
6.2.1 The Hypothesis
129(1)
6.2.2 Methodology
130(1)
6.2.3 Results
131(1)
6.2.4 Implications
131(1)
6.3 Concluding Remarks
132(1)
7 Conclusions, Implications and Future Research Directions
133(24)
7.1 Conclusions
133(1)
7.2 Implications and Further Research Directions
134(3)
A Simulation Results (Uniform Distribution)
137(4)
B Simulation Results (Poisson Distribution)
141(6)
C Simulation Results (Normal Distribution I)
147(4)
D Simulation Results (Normal Distribution II)
151(4)
E Simulation Results for Cost Analysis
155(2)
References 157(6)
About the Authors 163(2)
Index 165