Strategic planning models for reverse and closed-loop supply chains

The rapid technological development of new products, coupled with the growing consumer desire for the latest technology, has led to a new environmental problem: products that are discarded prematurely. But behind every problem lies an opportunity. Many of these products can be reprocessed, leading to savings in natural resources, energy, landfill space, and ultimately, time and money. Strategic Planning Models for Reverse and Closed-Loop Supply Chains addresses complex issues caused by the inherent uncertainty involved in every stage of a closed-loop supply chain.

The book presents quantitative models for the many multifaceted issues faced by strategic planners of reverse and closed-loop supply chains amid the challenges of uncertainty in supply rate of used products, unknown condition of used products, and imperfect correlation between supply of used products and demand for reprocessed goods.

The models proposed in this book provide understanding of how a particular issue can be effectively approached in a particular decision-making situation using a suitable quantitative technique or suitable combination of two or more quantitative techniques. This information then translates into decision-making strategies and guidance for reverse and closed-loop supply chain management.

Author Notes

Pochampally, Kishore K.; Nukala, Satish; Gupta, Surendra M.

Preface	p. xiii
Acknowledgments	p. xv
About the Authors	p. xvii
1 Introduction	p. 1
1.1 Motivation	p. 1
1.2 Overview of the Book	p. 5
1.3 Outline of the Book	p. 7
1.4 Conclusions	p. 9
References	p. 9
2 Strategic Planning of Reverse and Closed-Loop Supply Chains	p. 11
2.1 Introduction	p. 11
2.2 Selection of Used Products	p. 12
2.3 Evaluation of Collection Centers	p. 12
2.4 Evaluation of Recovery Facilities	p. 13
2.5 Optimization of Transportation of Goods	p. 13
2.6 Evaluation of Marketing Strategies	p. 14
2.7 Evaluation of Production Facilities	p. 14
2.8 Evaluation of Futurity of Used Products	p. 15
2.9 Selection of New Products	p. 15
2.10 Selection of Secondhand Markets	p. 16
2.11 Synchronization of Supply Chain Processes	p. 16
2.12 Supply Chain Performance Measurement	p. 16
2.13 Conclusions	p. 17
References	p. 17
3 Literature Review	p. 19
3.1 Introduction	p. 19
3.2 Operational Planning of Reverse and Closed-Loop Supply Chains	p. 19
3.3 Strategic and Tactical Planning of Reverse and Closed-Loop Supply Chains	p. 24
3.4 Conclusions	p. 31
References	p. 31
4 Quantitative Modeling Techniques	p. 37
4.1 Introduction	p. 37
4.2 Analytic Hierarchy Process and Eigen Vector Method	p. 37
4.3 Analytic Network Process	p. 39
4.4 Fuzzy Logic	p. 40
4.5 Extent Analysis Method	p. 43
4.6 Fuzzy Multicriteria Analysis Method	p. 44
4.7 Quality Function Deployment	p. 48
4.8 Method of Total Preferences	p. 49
4.9 Linear Physical Programming	p. 49
4.10 Goal Programming	p. 52
4.11 Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)	p. 55
4.12 Borda's Choice Rule	p. 58
4.13 Expert Systems	p. 58
4.14 Bayesian Updating	p. 59
4.15 Taguchi Loss Function	p. 61
4.16 Six Sigma	p. 63
4.16.1 Process Capability Ratio (C[subscript p])	p. 64
4.16.2 Process Capability Index (C[subscript pk])	p. 64
4.16.2.1 Three Sigma Process	p. 65
4.16.2.2 4.5 Sigma Process	p. 66
4.16.2.3 Six Sigma Process	p. 66
4.17 Neural Networks	p. 67
4.18 Geographical Information Systems	p. 68
4.19 Linear Integer Programming	p. 69
4.20 Conclusions	p. 69
References	p. 69
5 Selection of Used Products	p. 73
5.1 The Issue	p. 73
5.2 First Model (Linear Integer Programming)	p. 73
5.2.1 Nomenclature	p. 74
5.2.2 Model Formulation	p. 74
5.2.2.1 Modified Cost-Benefit Function	p. 75
5.2.2.2 Linear Integer Programming Model	p. 76
5.2.3 Numerical Example	p. 77
5.3 Second Model (Linear Physical Programming)	p. 78
5.3.1 Model Formulation	p. 78
5.3.1.1 Class 1S Criteria (Smaller Is Better)	p. 78
5.3.1.2 Class 2S Criteria (Larger Is Better)	p. 79
5.3.2 Numerical Example	p. 80
5.4 Conclusions	p. 80
References	p. 85
6 Evaluation of Collection Centers	p. 87
6.1 The Issue	p. 87
6.2 First Model (Eigen Vector Method and Taguchi Loss Function)	p. 88
6.2.1 Evaluation Criteria	p. 88
6.2.2 Model	p. 89
6.2.2.1 n Value	p. 90
6.2.2.2 Distance from Residential Area (DH)	p. 91
6.2.2.3 Distance from Roads (DR)	p. 91
6.2.2.4 Utilization of Incentives from Local Government (UI)	p. 91
6.2.2.5 Per Capita Income of People in Residential Area (PI)	p. 91
6.2.2.6 Space Cost (SC)	p. 92
6.2.2.7 Labor Cost (LC)	p. 92
6.2.2.8 Incentives from Local Government (IG)	p. 92
6.3 Evaluation Criteria for Second and Third Models	p. 93
6.3.1 Criteria of Consumers	p. 93
6.3.2 Criteria of Local Government Officials	p. 94
6.3.3 Criteria of Supply Chain Company Executives	p. 94
6.4 Second Model (Eigen Vector Method, TOPSIS, and Borda's Choice Rule)	p. 95
6.4.1 Phase I (Individual Decision Making)	p. 95
6.4.2 Phase II (Group Decision Making)	p. 101
6.5 Third Model (Neural Networks, Fuzzy Logic, TOPSIS, Borda's Rule)	p. 103
6.5.1 Phase I (Derivation of Impacts)	p. 103
6.5.2 Phase II (Individual Decision Making)	p. 106
6.5.3 Phase III (Group Decision Making)	p. 109
6.6 Fourth Model (ANP and Goal Programming)	p. 110
6.6.1 Application of ANP	p. 110
6.6.2 Application of Goal Programming	p. 116
6.6.2.1 Nomenclature for Problem Formulation	p. 116
6.6.2.2 Problem Formulation	p. 116
6.7 Fifth Model (Eigen Vector Method, Taguchi Loss Function, and Goal Programming)	p. 118
6.7.1 Application of Eigen Vector Method and Taguchi Loss Function	p. 118
6.7.2 Application of Goal Programming	p. 121
6.7.2.1 Nomenclature Used in the Methodology	p. 122
6.7.2.2 Problem Formulation	p. 122
6.8 Conclusions	p. 124
References	p. 124
7 Evaluation of Recovery Facilities	p. 125
7.1 The Issue	p. 125
7.2 First Model (Analytic Hierarchy Process)	p. 126
7.2.1 Three-Level Hierarchy	p. 126
7.2.2 Numerical Example	p. 128
7.3 Second Model (Linear Physical Programming)	p. 130
7.3.1 Nomenclature for LPP Model	p. 130
7.3.2 Criteria for Identification of Efficient Recovery Facilities	p. 131
7.3.2.1 Class 1S Criteria (Smaller is Better)	p. 131
7.3.2.2 Class 2S Criteria (Larger Is Better)	p. 131
7.3.3 Numerical Example	p. 132
7.4 Evaluation Criteria for Third and Fourth Models	p. 132
7.4.1 Criteria of Consumers	p. 134
7.4.2 Criteria of Local Government Officials	p. 134
7.4.3 Criteria of Supply Chain Company Executives	p. 135
7.5 Third Model (Eigen Vector Method, TOPSIS, and Borda's Choice Rule)	p. 135
7.5.1 Phase I (Individual Decision Making)	p. 135
7.5.2 Phase II (Group Decision Making)	p. 140
7.6 Fourth Model (Neural Networks, Fuzzy Logic, TOPSIS, Borda's Choice Rule)	p. 140
7.6.1 Phase I (Derivation of Impacts)	p. 141
7.6.2 Phase II (Individual Decision Making)	p. 145
7.6.3 Phase III (Group Decision Making)	p. 147
7.7 Fifth Model (Two-Dimensional Chart)	p. 148
7.8 Conclusions	p. 151
References	p. 151
8 Optimization of Transportation of Products	p. 153
8.1 The Issue	p. 153
8.2 First Model (Linear Integer Programming)	p. 154
8.2.1 Nomenclature	p. 154
8.2.2 Model Formulation	p. 155
8.2.3 Numerical Example	p. 157
8.3 Second Model (Linear Physical Programming)	p. 158
8.3.1 Model Formulation	p. 158
8.3.2 Numerical Example	p. 160
8.4 Third Model (Goal Programming)	p. 161
8.4.1 Nomenclature	p. 161
8.4.2 Model Formulation	p. 162
8.4.3 Numerical Example	p. 166
8.5 Fourth Model (Linear Physical Programming)	p. 168
8.5.1 Model Formulation	p. 168
8.5.2 Numerical Example	p. 171
8.6 Fifth Model (Fuzzy Goal Programming)	p. 173
8.6.1 Model Formulation	p. 173
8.6.2 Numerical Example	p. 178
8.7 Conclusions	p. 179
References	p. 179
9 Evaluation of Marketing Strategies	p. 181
9.1 The Issue	p. 181
9.2 First Model (Fuzzy Logic and TOPSIS)	p. 182
9.2.1 Drivers of Public Participation	p. 182
9.2.2 Methodology	p. 183
9.3 Second Model (Fuzzy Logic, Quality Function Deployment, and Method of Total Preferences)	p. 188
9.3.1 Performance Aspects and Enablers	p. 188
9.3.2 Numerical Example	p. 190
9.4 Third Model (Fuzzy Logic, Extent Analysis Method, and Analytic Network Process)	p. 192
9.4.1 Main Criteria and Subcriteria	p. 193
9.4.2 Numerical Example	p. 193
9.5 Conclusions	p. 198
References	p. 199
10 Evaluation of Production Facilities	p. 201
10.1 The Issue	p. 201
10.2 First Model (Fuzzy Logic and TOPSIS)	p. 202
10.2.1 Evaluation Criteria	p. 203
10.2.1.1 Environmentally Conscious Design (ECD)	p. 203
10.2.1.2 Environmentally Conscious Manufacturing (ECM)	p. 203
10.2.1.3 Attitude of Management (AMT)	p. 204
10.2.1.4 Potentiality (POT)	p. 204
10.2.1.5 Cost (COS)	p. 204
10.2.1.6 Customer Service (CSE)	p. 204
10.2.2 Numerical Example	p. 205
10.3 Second Model (Fuzzy Logic, Extent Analysis Method, and Analytic Network Process)	p. 212
10.4 Third Model (Fuzzy Multicriteria Analysis Method)	p. 215
10.5 Conclusions	p. 226
References	p. 226
11 Evaluation of Futurity of Used Products	p. 227
11.1 The Issue	p. 227
11.2 Usage of Fuzzy Logic	p. 229
11.3 Rules Used in Bayesian Updating	p. 230
11.4 Bayesian Updating	p. 231
11.5 FLEX-Based Expert System	p. 232
11.6 Conclusions	p. 232
References	p. 233
12 Selection of New Products	p. 235
12.1 The Issue	p. 235
12.2 Assumptions	p. 236
12.3 Nomenclature	p. 236
12.4 Formulation of Fuzzy Cost-Benefit Function	p. 238
12.4.1 Total New Product Sale Revenue per Period (SR)	p. 238
12.4.2 Total Reuse Revenue per Period (UR)	p. 238
12.4.3 Total Recycle Revenue per Period (CR)	p. 239
12.4.4 Total New Product Production Cost per Period (MC)	p. 239
12.4.5 Total Collection Cost per Period (CC)	p. 239
12.4.6 Total Reprocessing Cost per Period (RC)	p. 239
12.4.7 Total Disposal Cost per Period (DC)	p. 240
12.4.8 Loss-of-Sale Cost per Period (LC)	p. 240
12.4.9 Investment Cost (IC)	p. 240
12.5 Model	p. 241
12.6 Numerical Example	p. 241
12.7 Conclusions	p. 243
References	p. 244
13 Selection of Secondhand Markets	p. 245
13.1 The Issue	p. 245
13.2 Performance Aspects and Enablers for Application of QFD	p. 245
13.3 Selection of Potential Secondhand Markets	p. 246
13.4 Conclusions	p. 250
14 Design of a Synchronized Reverse Supply Chain	p. 251
14.1 The Issue	p. 251
14.2 Model (Two Design Experiments)	p. 251
14.2.1 First Experiment (Determination of Nominal Pool)	p. 251
14.2.2 Second Experiment (Determination of Variance Pool)	p. 253
14.3 Conclusions	p. 254
References	p. 255
15 Performance Measurement	p. 257
15.1 The Issue	p. 257
15.2 Application of LPP to QFD Optimization	p. 258
15.2.1 First Step	p. 258
15.2.2 Second Step	p. 260
15.3 Reverse/Closed-Loop Supply Chain Performance Measurement	p. 261
15.3.1 Performance Aspects and Enablers	p. 261
15.3.2 Numerical Example	p. 263
15.4 Conclusions	p. 269
References	p. 269
16 Conclusions	p. 271
Author index	p. 275
Subject Index	p. 279

Available:*

On Order

Summary

Summary

Author Notes

Table of Contents