Li / Wang Postponement Strategies in Supply Chain Management

1. Auflage 2010
ISBN: 978-1-4419-5837-2
Verlag: Springer-Verlag
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 166 Seiten

Postponement strategy is one of the major supply chain management (SCM) pr- tices that has a discernible impact on rms' competitive advantage and organi- tional performance. Postponement is a mass customization strategy that captures the advantages of both mass production and mass customization. Recent research studies have identi ed four common postponement strategies, namely pull, logistics, form and price postponement. The former three postponement strategies are linked to production and manufacturing, while the last one is a pure pricing strategy. They aim at balancing the costs and bene ts of mass production and mass customization. Practical examples of postponement can be found in the high-tech industry, food industry and other industries that require high differentiation. However, empirical studies have found that postponement may not be an evident SCM practice compared to the other practices. In addition, postponement has both positive and negative impacts on a supply chain. The advantages include following the JIT principles, reducing end-product inventory, making forecasting easier and pooling risk. The high cost of designing and manufacturing generic components is the main drawback of postponement. Thus, the evaluation of postponement strategy is an important research issue and there have been many qualitative and quantitative models for analyzing postponement under different scenarios.

Autoren/Hrsg.

Li, Jian

Wang, Shouyang

Inhaltsverzeichnis

1;Abstract;6
2;Preface;8
3;Contents;12
4;List of Figures;16
5;List of Tables;18
6;1 Introduction;20
6.1;1.1 From Product Variety to Postponement;20
6.1.1;1.1.1 Product Variety;20
6.1.2;1.1.2 Mass Customization;21
6.1.3;1.1.3 Postponement Strategy;22
6.2;1.2 Classification of Postponement;22
6.2.1;1.2.1 Pull Postponement;23
6.2.2;1.2.2 Logistics Postponement;25
6.2.3;1.2.3 Form Postponement;26
6.2.4;1.2.4 Price Postponement;27
6.2.5;1.2.5 Implications;27
6.2.6;1.2.6 Advantages and Disadvantages of Postponement;28
6.2.7;1.2.7 Prerequisites for Postponement Strategy Development;29
6.3;1.3 Cost Models for Analyzing Postponement Strategies;30
6.3.1;1.3.1 Stochastic Models;30
6.3.2;1.3.2 Heuristic Models;31
6.3.3;1.3.3 Descriptive Models;32
6.3.4;1.3.4 Performance Measures;33
6.4;1.4 A Literature Review for Model Development;33
6.4.1;1.4.1 EOQ and EPQ Models;34
6.4.2;1.4.2 Lot Size-Reorder Point Model;35
6.4.3;1.4.3 Markov Chain;35
6.5;1.5 Concluding Remarks;36
7;2 Analysis of Pull Postponement by EOQ-based Models ;37
7.1;2.1 Postponement Strategy for Ordinary (Imperishable) Items;37
7.1.1;2.1.1 Proposed Model and Assumptions;37
7.1.2;2.1.2 Case 1: Same Backorder Cost;40
7.1.3;2.1.3 Case 2: Different Backorder Costs;44
7.1.4;2.1.4 A Numerical Example;48
7.2;2.2 Postponement Strategy for Perishable Items;50
7.2.1;2.2.1 Notation and Assumptions;51
7.2.2;2.2.2 Model Formulation;52
7.2.3;2.2.3 The Postponement and Independent Systems;56
7.2.4;2.2.4 Numerical Examples;57
7.3;2.3 Concluding Remarks;59
8;3 Analysis of Postponement Strategy by EPQ-based Models;60
8.1;3.1 Analysis of Postponement Strategy by an EPQ-based Model without Stockout;60
8.1.1;3.1.1 Proposed Model and Assumptions;60
8.1.2;3.1.2 2 Machines for 2 End-Products;63
8.1.3;3.1.3 n Machines for n End-Products;73
8.2;3.2 Analysis of Postponement Strategy by an EPQ-based Model with Planned Backorders;79
8.2.1;3.2.1 Proposed Model and Assumptions;80
8.2.2;3.2.2 Demands Are Met Continuously;82
8.2.3;3.2.3 Demands Are Met After Production Is Complete;88
8.3;3.3 Concluding Remarks;95
9;4 Evaluation of a Postponement Systemwith an (r,q) Policy;97
9.1;4.1 The Proposed Models and Assumptions;97
9.2;4.2 System Dynamics for a Non-postponement System;99
9.3;4.3 The Algorithm for Finding a Near Optimal Total Average Cost of an (r,q) Policy;100
9.3.1;4.3.1 The Markov Chain Development;100
9.3.2;4.3.2 The Algorithm for Finding a Near Optimal Total Average Cost;115
9.4;4.4 System Dynamics for a Postponement System;118
9.5;4.5 Average Cost Comparison of the Two Systems When L=0;119
9.6;4.6 Average Cost Comparison of the Two Systems When L1;120
9.6.1;4.6.1 An Overview of the Simulation Results;120
9.6.2;4.6.2 Impacts of Parameters on Average Cost;122
9.7;4.7 Concluding Remarks;123
10;5 Simulation of a Two-End-Product Postponement System;125
10.1;5.1 Proposed Model and Assumptions;126
10.1.1;5.1.1 Notation;127
10.1.2;5.1.2 Model Assumptions;127
10.2;5.2 Methodology;128
10.2.1;5.2.1 System Dynamics;128
10.2.2;5.2.2 The Simulation Model;130
10.2.3;5.2.3 Customer Demand Distribution;130
10.2.4;5.2.4 Order Quantity and Reorder Point;131
10.2.5;5.2.5 Summary of Parameters;131
10.2.6;5.2.6 Initial Conditions;131
10.3;5.3 Simulation Results for Non-cost Parameters;133
10.3.1;5.3.1 Uniform Distribution;133
10.3.2;5.3.2 Poisson Distribution;134
10.3.3;5.3.3 Normal Distribution I;135
10.3.4;5.3.4 Normal Distribution II;136
10.4;5.4 Simulation Results for Cost Parameters;137
10.5;5.5 Concluding Remarks;139
11;6 Application of Postponement: Examples from Industry;140
11.1;6.1 A Case Study from Hong Kong;140
11.1.1;6.1.1 An Overview of the Company;141
11.1.2;6.1.2 Implementation of Postponement;141
11.1.3;6.1.3 Benefits of Using Postponement;142
11.1.4;6.1.4 Implications;143
11.2;6.2 The Case of Taiwanese Information Technology Industry ;144
11.2.1;6.2.1 The Hypothesis;144
11.2.2;6.2.2 Methodology;145
11.2.3;6.2.3 Results;146
11.2.4;6.2.4 Implications;146
11.3;6.3 Concluding Remarks;147
12;7 Conclusions, Implications and Future Research Directions;148
12.1;7.1 Conclusions;148
12.2;7.2 Implications and Further Research Directions;149
13;A Simulation Results (Uniform Distribution);152
14;B Simulation Results (Poisson Distribution);156
15;C Simulation Results (Normal Distribution I);162
16;D Simulation Results (Normal Distribution II);166
17;E Simulation Results for Cost Analysis;170
18;References;172
19;About the Authors;178
20;Index;180