Strategic Planning Models for Reverse and Closed-loop Supply Chains

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ISBN: 9781420054781 | 1420054783
Cover: Hardcover
Copyright: 9/22/2008

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The first complete work published on strategic planning of reverse and closed-loop supply chains, this book addresses the issues faced by planners using efficient models in a variety of decision-making situations. It addresses selection of economical used and new products, and the evaluation of collection centers, production and recovery facilities, and marketing strategies. It also looks at the selection of potential second-hand markets, and the achievement of optimal transportation. Written for both professionals and academics, it demonstrates how particular strategic issues can be effectively approached in particular situations, using suitable quantitative techniques or suitable combinations.

Preface	p. xiii
Acknowledgments	p. xv
About the Authors	p. xvii
Introduction	p. 1
Motivation	p. 1
Overview of the Book	p. 5
Outline of the Book	p. 7
Conclusions	p. 9
References	p. 9
Strategic Planning of Reverse and Closed-Loop Supply Chains	p. 11
Introduction	p. 11
Selection of Used Products	p. 12
Evaluation of Collection Centers	p. 12
Evaluation of Recovery Facilities	p. 13
Optimization of Transportation of Goods	p. 13
Evaluation of Marketing Strategies	p. 14
Evaluation of Production Facilities	p. 14
Evaluation of Futurity of Used Products	p. 15
Selection of New Products	p. 15
Selection of Secondhand Markets	p. 16
Synchronization of Supply Chain Processes	p. 16
Supply Chain Performance Measurement	p. 16
Conclusions	p. 17
References	p. 17
Literature Review	p. 19
Introduction	p. 19
Operational Planning of Reverse and Closed-Loop Supply Chains	p. 19
Strategic and Tactical Planning of Reverse and Closed-Loop Supply Chains	p. 24
Conclusions	p. 31
References	p. 31
Quantitative Modeling Techniques	p. 37
Introduction	p. 37
Analytic Hierarchy Process and Eigen Vector Method	p. 37
Analytic Network Process	p. 39
Fuzzy Logic	p. 40
Extent Analysis Method	p. 43
Fuzzy Multicriteria Analysis Method	p. 44
Quality Function Deployment	p. 48
Method of Total Preferences	p. 49
Linear Physical Programming	p. 49
Goal Programming	p. 52
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)	p. 55
Borda's Choice Rule	p. 58
Expert Systems	p. 58
Bayesian Updating	p. 59
Taguchi Loss Function	p. 61
Six Sigma	p. 63
Process Capability Ratio (C[subscript p])	p. 64
Process Capability Index (C[subscript pk])	p. 64
Three Sigma Process	p. 65
4.5 Sigma Process	p. 66
Six Sigma Process	p. 66
Neural Networks	p. 67
Geographical Information Systems	p. 68
Linear Integer Programming	p. 69
Conclusions	p. 69
References	p. 69
Selection of Used Products	p. 73
The Issue	p. 73
First Model (Linear Integer Programming)	p. 73
Nomenclature	p. 74
Model Formulation	p. 74
Modified Cost-Benefit Function	p. 75
Linear Integer Programming Model	p. 76
Numerical Example	p. 77
Second Model (Linear Physical Programming)	p. 78
Model Formulation	p. 78
Class 1S Criteria (Smaller Is Better)	p. 78
Class 2S Criteria (Larger Is Better)	p. 79
Numerical Example	p. 80
Conclusions	p. 80
References	p. 85
Evaluation of Collection Centers	p. 87
The Issue	p. 87
First Model (Eigen Vector Method and Taguchi Loss Function)	p. 88
Evaluation Criteria	p. 88
Model	p. 89
n Value	p. 90
Distance from Residential Area (DH)	p. 91
Distance from Roads (DR)	p. 91
Utilization of Incentives from Local Government (UI)	p. 91
Per Capita Income of People in Residential Area (PI)	p. 91
Space Cost (SC)	p. 92
Labor Cost (LC)	p. 92
Incentives from Local Government (IG)	p. 92
Evaluation Criteria for Second and Third Models	p. 93
Criteria of Consumers	p. 93
Criteria of Local Government Officials	p. 94
Criteria of Supply Chain Company Executives	p. 94
Second Model (Eigen Vector Method, TOPSIS, and Borda's Choice Rule)	p. 95
Phase I (Individual Decision Making)	p. 95
Phase II (Group Decision Making)	p. 101
Third Model (Neural Networks, Fuzzy Logic, TOPSIS, Borda's Rule)	p. 103
Phase I (Derivation of Impacts)	p. 103
Phase II (Individual Decision Making)	p. 106
Phase III (Group Decision Making)	p. 109
Fourth Model (ANP and Goal Programming)	p. 110
Application of ANP	p. 110
Application of Goal Programming	p. 116
Nomenclature for Problem Formulation	p. 116
Problem Formulation	p. 116
Fifth Model (Eigen Vector Method, Taguchi Loss Function, and Goal Programming)	p. 118
Application of Eigen Vector Method and Taguchi Loss Function	p. 118
Application of Goal Programming	p. 121
Nomenclature Used in the Methodology	p. 122
Problem Formulation	p. 122
Conclusions	p. 124
References	p. 124
Evaluation of Recovery Facilities	p. 125
The Issue	p. 125
First Model (Analytic Hierarchy Process)	p. 126
Three-Level Hierarchy	p. 126
Numerical Example	p. 128
Second Model (Linear Physical Programming)	p. 130
Nomenclature for LPP Model	p. 130
Criteria for Identification of Efficient Recovery Facilities	p. 131
Class 1S Criteria (Smaller is Better)	p. 131
Class 2S Criteria (Larger Is Better)	p. 131
Numerical Example	p. 132
Evaluation Criteria for Third and Fourth Models	p. 132
Criteria of Consumers	p. 134
Criteria of Local Government Officials	p. 134
Criteria of Supply Chain Company Executives	p. 135
Third Model (Eigen Vector Method, TOPSIS, and Borda's Choice Rule)	p. 135
Phase I (Individual Decision Making)	p. 135
Phase II (Group Decision Making)	p. 140
Fourth Model (Neural Networks, Fuzzy Logic, TOPSIS, Borda's Choice Rule)	p. 140
Phase I (Derivation of Impacts)	p. 141
Phase II (Individual Decision Making)	p. 145
Phase III (Group Decision Making)	p. 147
Fifth Model (Two-Dimensional Chart)	p. 148
Conclusions	p. 151
References	p. 151
Optimization of Transportation of Products	p. 153
The Issue	p. 153
First Model (Linear Integer Programming)	p. 154
Nomenclature	p. 154
Model Formulation	p. 155
Numerical Example	p. 157
Second Model (Linear Physical Programming)	p. 158
Model Formulation	p. 158
Numerical Example	p. 160
Third Model (Goal Programming)	p. 161
Nomenclature	p. 161
Model Formulation	p. 162
Numerical Example	p. 166
Fourth Model (Linear Physical Programming)	p. 168
Model Formulation	p. 168
Numerical Example	p. 171
Fifth Model (Fuzzy Goal Programming)	p. 173
Model Formulation	p. 173
Numerical Example	p. 178
Conclusions	p. 179
References	p. 179
Evaluation of Marketing Strategies	p. 181
The Issue	p. 181
First Model (Fuzzy Logic and TOPSIS)	p. 182
Drivers of Public Participation	p. 182
Methodology	p. 183
Second Model (Fuzzy Logic, Quality Function Deployment, and Method of Total Preferences)	p. 188
Performance Aspects and Enablers	p. 188
Numerical Example	p. 190
Third Model (Fuzzy Logic, Extent Analysis Method, and Analytic Network Process)	p. 192
Main Criteria and Subcriteria	p. 193
Numerical Example	p. 193
Conclusions	p. 198
References	p. 199
Evaluation of Production Facilities	p. 201
The Issue	p. 201
First Model (Fuzzy Logic and TOPSIS)	p. 202
Evaluation Criteria	p. 203
Environmentally Conscious Design (ECD)	p. 203
Environmentally Conscious Manufacturing (ECM)	p. 203
Attitude of Management (AMT)	p. 204
Potentiality (POT)	p. 204
Cost (COS)	p. 204
Customer Service (CSE)	p. 204
Numerical Example	p. 205
Second Model (Fuzzy Logic, Extent Analysis Method, and Analytic Network Process)	p. 212
Third Model (Fuzzy Multicriteria Analysis Method)	p. 215
Conclusions	p. 226
References	p. 226
Evaluation of Futurity of Used Products	p. 227
The Issue	p. 227
Usage of Fuzzy Logic	p. 229
Rules Used in Bayesian Updating	p. 230
Bayesian Updating	p. 231
FLEX-Based Expert System	p. 232
Conclusions	p. 232
References	p. 233
Selection of New Products	p. 235
The Issue	p. 235
Assumptions	p. 236
Nomenclature	p. 236
Formulation of Fuzzy Cost-Benefit Function	p. 238
Total New Product Sale Revenue per Period (SR)	p. 238
Total Reuse Revenue per Period (UR)	p. 238
Total Recycle Revenue per Period (CR)	p. 239
Total New Product Production Cost per Period (MC)	p. 239
Total Collection Cost per Period (CC)	p. 239
Total Reprocessing Cost per Period (RC)	p. 239
Total Disposal Cost per Period (DC)	p. 240
Loss-of-Sale Cost per Period (LC)	p. 240
Investment Cost (IC)	p. 240
Model	p. 241
Numerical Example	p. 241
Conclusions	p. 243
References	p. 244
Selection of Secondhand Markets	p. 245
The Issue	p. 245
Performance Aspects and Enablers for Application of QFD	p. 245
Selection of Potential Secondhand Markets	p. 246
Conclusions	p. 250
Design of a Synchronized Reverse Supply Chain	p. 251
The Issue	p. 251
Model (Two Design Experiments)	p. 251
First Experiment (Determination of Nominal Pool)	p. 251
Second Experiment (Determination of Variance Pool)	p. 253
Conclusions	p. 254
References	p. 255
Performance Measurement	p. 257
The Issue	p. 257
Application of LPP to QFD Optimization	p. 258
First Step	p. 258
Second Step	p. 260
Reverse/Closed-Loop Supply Chain Performance Measurement	p. 261
Performance Aspects and Enablers	p. 261
Numerical Example	p. 263
Conclusions	p. 269
References	p. 269
Conclusions	p. 271
Author index	p. 275
Subject Index	p. 279
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