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Strategic Planning Models for Reverse and Closed-Loop Supply Chains [Kõva köide]

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(Northeastern University, Boston, USA), , (Southern New Hampshire University, Manchester, USA)
  • Formaat: Hardback, 304 pages, kõrgus x laius: 234x156 mm, kaal: 566 g
  • Ilmumisaeg: 22-Sep-2008
  • Kirjastus: CRC Press Inc
  • ISBN-10: 1420054783
  • ISBN-13: 9781420054781
Teised raamatud teemal:
Strategic Planning Models for Reverse and Closed-Loop Supply ChainsSuurem pilt
  • Formaat: Hardback, 304 pages, kõrgus x laius: 234x156 mm, kaal: 566 g
  • Ilmumisaeg: 22-Sep-2008
  • Kirjastus: CRC Press Inc
  • ISBN-10: 1420054783
  • ISBN-13: 9781420054781
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781420054781.html
Märksõnad:
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.
Preface xiii
Acknowledgments xv
About the Authors xvii
Introduction
1(10)
Motivation
1(4)
Overview of the Book
5(2)
Outline of the Book
7(2)
Conclusions
9(2)
References
9(2)
Strategic Planning of Reverse and Closed-Loop Supply Chains
11(8)
Introduction
11(1)
Selection of Used Products
12(1)
Evaluation of Collection Centers
12(1)
Evaluation of Recovery Facilities
13(1)
Optimization of Transportation of Goods
13(1)
Evaluation of Marketing Strategies
14(1)
Evaluation of Production Facilities
14(1)
Evaluation of Futurity of Used Products
15(1)
Selection of New Products
15(1)
Selection of Secondhand Markets
16(1)
Synchronization of Supply Chain Processes
16(1)
Supply Chain Performance Measurement
16(1)
Conclusions
17(2)
References
17(2)
Literature Review
19(18)
Introduction
19(1)
Operational Planning of Reverse and Closed-Loop Supply Chains
19(5)
Strategic and Tactical Planning of Reverse and Closed-Loop Supply Chains
24(7)
Conclusions
31(6)
References
31(6)
Quantitative Modeling Techniques
37(36)
Introduction
37(1)
Analytic Hierarchy Process and Eigen Vector Method
37(2)
Analytic Network Process
39(1)
Fuzzy Logic
40(3)
Extent Analysis Method
43(1)
Fuzzy Multicriteria Analysis Method
44(4)
Quality Function Deployment
48(1)
Method of Total Preferences
49(1)
Linear Physical Programming
49(3)
Goal Programming
52(3)
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)
55(3)
Borda's Choice Rule
58(1)
Expert Systems
58(1)
Bayesian Updating
59(2)
Taguchi Loss Function
61(2)
Six Sigma
63(4)
Process Capability Ratio (Cp)
64(1)
Process Capability Index (Cpk)
64(1)
Three Sigma Process
65(1)
4.5 Sigma Process
66(1)
Six Sigma Process
66(1)
Neural Networks
67(1)
Geographical Information Systems
68(1)
Linear Integer Programming
69(1)
Conclusions
69(4)
References
69(4)
Selection of Used Products
73(14)
The Issue
73(1)
First Model (Linear Integer Programming)
73(5)
Nomenclature
74(1)
Model Formulation
74(1)
Modified Cost-Benefit Function
75(1)
Linear Integer Programming Model
76(1)
Numerical Example
77(1)
Second Model (Linear Physical Programming)
78(2)
Model Formulation
78(1)
Class 1S Criteria (Smaller Is Better)
78(1)
Class 2S Criteria (Larger Is Better)
79(1)
Numerical Example
80(1)
Conclusions
80(7)
References
85(2)
Evaluation of Collection Centers
87(38)
The Issue
87(1)
First Model (Eigen Vector Method and Taguchi Loss Function)
88(5)
Evaluation Criteria
88(1)
Model
89(1)
n Value
90(1)
Distance from Residential Area (DH)
91(1)
Distance from Roads (DR)
91(1)
Utilization of Incentives from Local Government (uI)
91(1)
Per Capita Income of People in Residential Area (PI)
91(1)
Space Cost (SC)
92(1)
Labor Cost (LC)
92(1)
Incentives from Local Government (IG)
92(1)
Evaluation Criteria for Second and Third Models
93(2)
Criteria of Consumers
93(1)
Criteria of Local Government Officials
94(1)
Criteria of Supply Chain Company Executives
94(1)
Second Model (Eigen Vector Method, TOPSIS, and Borda's Choice Rule)
95(8)
Phase I (Individual Decision Making)
95(6)
Phase II (Group Decision Making)
101(2)
Third Model (Neural Networks, Fuzzy Logic, TOPSIS, Borda's Rule)
103(7)
Phase I (Derivation of Impacts)
103(3)
Phase II (Individual Decision Making)
106(3)
Phase III (Group Decision Making)
109(1)
Fourth Model (ANP and Goal Programming)
110(8)
Application of ANP
110(6)
Application of Goal Programming
116(1)
Nomenclature for Problem Formulation
116(1)
Problem Formulation
116(2)
Fifth Model (Eigen Vector Method, Taguchi Loss Function, and Goal Programming)
118(6)
Application of Eigen Vector Method and Taguchi Loss Function
118(3)
Application of Goal Programming
121(1)
Nomenclature Used in the Methodology
122(1)
Problem Formulation
122(2)
Conclusions
124(1)
References
124(1)
Evaluation of Recovery Facilities
125(28)
The Issue
125(1)
First Model (Analytic Hierarchy Process)
126(4)
Three-Level Hierarchy
126(2)
Numerical Example
128(2)
Second Model (Linear Physical Programming)
130(2)
Nomenclature for LPP Model
130(1)
Criteria for Identification of Efficient Recovery Facilities
131(1)
Class 1S Criteria (Smaller is Better)
131(1)
Class 2S Criteria (Larger Is Better)
131(1)
Numerical Example
132(1)
Evaluation Criteria for Third and Fourth Models
132(3)
Criteria of Consumers
134(1)
Criteria of Local Government Officials
134(1)
Criteria of Supply Chain Company Executives
135(1)
Third Model (Eigen Vector Method, TOPSIS, and Borda's Choice Rule)
135(5)
Phase I (Individual Decision Making)
135(5)
Phase II (Group Decision Making)
140(1)
Fourth Model (Neural Networks, Fuzzy Logic, TOPSIS, Borda's Choice Rule)
140(8)
Phase I (Derivation of Impacts)
141(4)
Phase II (Individual Decision Making)
145(2)
Phase III (Group Decision Making)
147(1)
Fifth Model (Two-Dimensional Chart)
148(3)
Conclusions
151(2)
References
151(2)
Optimization of Transportation of Products
153(28)
The Issue
153(1)
First Model (Linear Integer Programming)
154(4)
Nomenclature
154(1)
Model Formulation
155(2)
Numerical Example
157(1)
Second Model (Linear Physical Programming)
158(3)
Model Formulation
158(2)
Numerical Example
160(1)
Third Model (Goal Programming)
161(7)
Nomenclature
161(1)
Model Formulation
162(4)
Numerical Example
166(2)
Fourth Model (Linear Physical Programming)
168(5)
Model Formulation
168(3)
Numerical Example
171(2)
Fifth Model (Fuzzy Goal Programming)
173(6)
Model Formulation
173(5)
Numerical Example
178(1)
Conclusions
179(2)
References
179(2)
Evaluation of Marketing Strategies
181(20)
The Issue
181(1)
First Model (Fuzzy Logic and TOPSIS)
182(6)
Drivers of Public Participation
182(1)
Methodology
183(5)
Second Model (Fuzzy Logic, Quality Function Deployment, and Method of Total Preferences)
188(4)
Performance Aspects and Enablers
188(2)
Numerical Example
190(2)
Third Model (Fuzzy Logic, Extent Analysis Method, and Analytic Network Process)
192(6)
Main Criteria and Subcriteria
193(1)
Numerical Example
193(5)
Conclusions
198(3)
References
199(2)
Evaluation of Production Facilities
201(26)
The Issue
201(1)
First Model (Fuzzy Logic and TOPSIS)
202(10)
Evaluation Criteria
203(1)
Environmentally Conscious Design (ECD)
203(1)
Environmentally Conscious Manufacturing (ECM)
203(1)
Attitude of Management (AMT)
204(1)
Potentiality (POT)
204(1)
Cost (COS)
204(1)
Customer Service (CSE)
204(1)
Numerical Example
205(7)
Second Model (Fuzzy Logic, Extent Analysis Method, and Analytic Network Process)
212(3)
Third Model (Fuzzy Multicriteria Analysis Method)
215(11)
Conclusions
226(1)
References
226(1)
Evaluation of Futurity of Used Products
227(8)
The Issue
227(2)
Usage of Fuzzy Logic
229(1)
Rules Used in Bayesian Updating
230(1)
Bayesian Updating
231(1)
FLEX-Based Expert System
232(1)
Conclusions
232(3)
References
233(2)
Selection of New Products
235(10)
The Issue
235(1)
Assumptions
236(1)
Nomenclature
236(2)
Formulation of Fuzzy Cost-Benefit Function
238(3)
Total New Product Sale Revenue per Period (SR)
238(1)
Total Reuse Revenue per Period (uR)
238(1)
Total Recycle Revenue per Period (CR)
239(1)
Total New Product Production Cost per Period (MC)
239(1)
Total Collection Cost per Period (CC)
239(1)
Total Reprocessing Cost per Period (RC)
239(1)
Total Disposal Cost per Period (DC)
240(1)
Loss-of-Sale Cost per Period (LC)
240(1)
Investment Cost (IC)
240(1)
Model
241(1)
Numerical Example
241(2)
Conclusions
243(2)
References
244(1)
Selection of Secondhand Markets
245(6)
The Issue
245(1)
Performance Aspects and Enablers for Application of QFD
245(1)
Selection of Potential Secondhand Markets
246(4)
Conclusions
250(1)
Design of a Synchronized Reverse Supply Chain
251(6)
The Issue
251(1)
Model (Two Design Experiments)
251(3)
First Experiment (Determination of Nominal Pool)
251(2)
Second Experiment (Determination of Variance Pool)
253(1)
Conclusions
254(3)
References
255(2)
Performance Measurement
257(14)
The Issue
257(1)
Application of LPP to QFD Optimization
258(3)
First Step
258(2)
Second Step
260(1)
Reverse/Closed-Loop Supply Chain Performance Measurement
261(8)
Performance Aspects and Enablers
261(2)
Numerical Example
263(6)
Conclusions
269(2)
References
269(2)
Conclusions
271(4)
Author index 275(4)
Subject Index 279
Pochampally, Kishore K.; Nukala, Satish; Gupta, Surendra M.