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Structural Dynamics and Resilience in Supply Chain Risk Management 1st ed. 2018 [Kõva köide]

  • Formaat: Hardback, 320 pages, kõrgus x laius: 235x155 mm, kaal: 6387 g, 33 Illustrations, color; 77 Illustrations, black and white; XXIV, 320 p. 110 illus., 33 illus. in color., 1 Hardback
  • Sari: International Series in Operations Research & Management Science 265
  • Ilmumisaeg: 23-Nov-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319693042
  • ISBN-13: 9783319693040
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Structural Dynamics and Resilience in Supply Chain Risk Management 1st ed. 2018Suurem pilt
  • Formaat: Hardback, 320 pages, kõrgus x laius: 235x155 mm, kaal: 6387 g, 33 Illustrations, color; 77 Illustrations, black and white; XXIV, 320 p. 110 illus., 33 illus. in color., 1 Hardback
  • Sari: International Series in Operations Research & Management Science 265
  • Ilmumisaeg: 23-Nov-2017
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319693042
  • ISBN-13: 9783319693040
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319693040.html
This book offers an introduction to structural dynamics, ripple effect and resilience in supply chain disruption risk management for larger audiences. In the management section, without relying heavily on mathematical derivations, the book offers state-of-the-art concepts and methods to tackle supply chain disruption risks and designing resilient supply chains in a simple, predictable format to make it easy to understand for students and professionals with both management and engineering background.

In the technical section, the book constitutes structural dynamics control methods for supply chain management. Real-life problems are modelled and solved with the help of mathematical programming, discrete-event simulation, optimal control theory, and fuzzy logic.

The book derives practical recommendations for management decision-making with disruption risk in the following areas:









How to estimate the impact of possible disruptions on performance in the pro-active stage?

How to generate efficient and effective stabilization and recovery policies?

When does one failure trigger an adjacent set of failures?

Which supply chain structures are particular sensitive to ripple effect?

How to measure the disruption risks in the supply chain?
1 Supply Chain Management and Structural Dynamics Control
1(18)
1.1 Structural Dynamics and Supply Chains
1(6)
1.2 Basics of Supply Chain Management
7(3)
1.3 Technical Description of Supply Chain Structural Dynamics
10(3)
1.4 General Formal Statement of the Supply Chain Structure Dynamics Control Problem
13(2)
1.5 Generalized Dynamic Model of Supply Chain Structural Dynamics Control Processes (M Model)
15(4)
References
16(3)
2 Supply Chain Risk Management: Bullwhip Effect and Ripple Effect
19(26)
2.1 Uncertainty and Risks
19(6)
2.1.1 Sources of Uncertainty
20(2)
2.1.2 Uncertainty and Complexity
22(3)
2.2 Risk Management in the Supply Chain
25(6)
2.2.1 General Framework of Risk Control
25(3)
2.2.2 Operational and Disruption Risks
28(3)
2.3 Bullwhip Effect
31(3)
2.4 Ripple Effect
34(11)
2.4.1 Definition
34(2)
2.4.2 Reasons for Ripple Effect
36(1)
2.4.3 Mitigation Strategies for Ripple Effect
37(3)
2.4.4 Information Technologies for Tackling the Ripple Effect
40(1)
References
40(5)
3 Supply Chain Resilience: Modelling, Management, and Control
45(46)
3.1 Terminological Framework: Redundancy, Robustness, Stability, Flexibility, Resilience
47(6)
3.2 Frameworks for Supply Chain and Operations Disruption Management
53(2)
3.3 State Dynamics Control
55(1)
3.4 Control-Theoretic Supply Chain Resilience Framework
56(7)
3.4.1 Case 1: Stability
58(1)
3.4.2 Case 2: Robustness
59(1)
3.4.3 Case 3: Resilience
59(1)
3.4.4 Case 4: Viability
59(4)
3.5 Supply Chain Resilience Analysis with the Help of Attainable Sets
63(4)
3.6 Fuzzy-Theoretic Analysis of Supply Chain Structural Robustness with the Help of Genome Method
67(13)
3.6.1 Genome Method for Structural Robustness Analysis in the Supply Chain
67(3)
3.6.2 Supply Chain Structural Robustness Computation: Exact Method
70(2)
3.6.3 Computation of the Upper and Lower Boundaries for Supply Chain Structural Robustness
72(3)
3.6.4 Computation Example
75(1)
3.6.5 Advanced Analysis with Costs Considerations
75(5)
3.7 Models and Algorithms of Supply Chain Reconfiguration
80(11)
3.7.1 Decision Making Framework for Resilience Supply Chains
80(2)
3.7.2 Algorithms of Supply Chain (Re)Planning Under Uncertainty
82(5)
References
87(4)
4 Principles and Methods of Model-Based Decision-Making in the Supply Chain
91(24)
4.1 Basics of Model-Based Decision-Making in Supply Chain Management
91(9)
4.1.1 Problems, Systems, and Decision-Making
91(2)
4.1.2 Models and Modelling
93(2)
4.1.3 Model-Based Decision-Making
95(3)
4.1.4 Quantitative Models and Operations Research
98(2)
4.2 Multi-disciplinary Nature of Quantitative Modelling Framework
100(5)
4.3 Modelling Paradigms
105(10)
4.3.1 Mathematical Optimization
105(1)
4.3.2 Simulation
106(1)
4.3.3 Optimization-Based Simulation
107(1)
4.3.4 Control Theory
108(4)
4.3.5 Heuristics
112(1)
References
113(2)
5 OR/MS Methods for Structural Dynamics in Supply Chain Risk Management
115(46)
5.1 Literature Selection Principles
115(3)
5.2 Mixed-Integer Programming
118(2)
5.3 Stochastic Programming/Fuzzy and Robust Optimization
120(1)
5.4 Pricing and Game Theory
121(1)
5.5 Simulation
122(5)
5.5.1 System Dynamics
123(1)
5.5.2 Agent-Based Simulation
123(1)
5.5.3 Discrete-Event Simulation
123(2)
5.5.4 Graph-Theoretical Studies
125(1)
5.5.5 Optimization-Based Simulation
126(1)
5.6 System Science and Control Theory
127(7)
5.6.1 Dynamic Feedback Production-Inventory Control
128(3)
5.6.2 Optimal Multi-stage Production Planning and Scheduling
131(3)
5.7 Analysis and Observations
134(27)
5.7.1 Reasons for Supply Chain Risks
134(1)
5.7.2 Risk Mitigation and Recovery Measures
135(5)
5.7.3 Application of Quantitative Analysis Methods
140(5)
5.7.4 Critical Analysis and Future Research Needs
145(3)
References
148(13)
6 Hybrid Multi-objective Mathematical Optimization: Optimal Control Model for Proactive Supply Chain Recovery Planning
161(42)
6.1 Problem Statement and Modelling Approach
161(2)
6.1.1 Management Problem Statement
161(1)
6.1.2 Modelling Approach
162(1)
6.2 Mathematical Model
163(5)
6.2.1 Problem Description
163(1)
6.2.2 Linear Programming Model
164(1)
6.2.3 Optimal Control Model
164(4)
6.3 Computational Procedure
168(6)
6.3.1 Model Coordination
168(1)
6.3.2 Linear Programming Model Solution and Complexity
169(1)
6.3.3 Optimal Control Problem Solution
170(4)
6.4 Experiments for Distribution Network Structural Dynamics
174(13)
6.4.1 Supply Chain Design Structural Dynamics
174(3)
6.4.2 Planning Results
177(2)
6.4.3 Sensitivity Analysis
179(2)
6.4.4 Distribution Network Re-design
181(6)
6.5 Experiments for Manufacturing Supply Chain Structural Dynamics with Ripple Effect Considerations
187(13)
6.5.1 Supply Chain Design Structural Dynamics
187(5)
6.5.2 Planning Results for Initial Supply Chain Design
192(1)
6.5.3 Planning Results for the Re-designed Supply Chain
193(1)
6.5.4 Quantifying the Ripple Effect
194(3)
6.5.5 Impact of Recovery Speed
197(3)
6.6 Managerial Insights
200(3)
References
201(2)
7 Control-Theoretic Models and Algorithms for Supply Chain Scheduling with Capacity Disruption and Recovery Considerations
203(40)
7.1 Problem Statement
203(3)
7.1.1 Coordinated Supply Chain Scheduling Problem
203(1)
7.1.2 Supply Chains Scheduling with Capacity Disruptions and Recovery
204(2)
7.2 Modelling Approach
206(2)
7.2.1 Literature on Coordinated Supply Chain Scheduling
206(1)
7.2.2 Hybrid Optimal Control-Mathematical Programming Approach to Coordinated Supply Chain Scheduling
207(1)
7.3 Formal Model
208(15)
7.3.1 Notations
209(1)
7.3.2 Dynamic Model for the Operation control Processes (Model Mo)
210(4)
7.3.3 Dynamic Model of Channel Control (Model Mk)
214(2)
7.3.4 Dynamic Model of Resource Control (Model Mr)
216(2)
7.3.5 Dynamic Model of Flow Control (Model Mf)
218(3)
7.3.6 Formulation of the Scheduling Problem
221(1)
7.3.7 Example
221(2)
7.4 Modelling Capacity disruptions and Recovery
223(6)
7.4.1 Assumptions and Notations
223(2)
7.4.2 Mathematical Model M1
225(2)
7.4.3 Mathematical Model M2
227(2)
7.4.4 Model Coordination
229(1)
7.5 Computational Procedure
229(8)
7.5.1 Transformation of the Optimal Control Program to the Boundary Problem
229(2)
7.5.2 Hamiltonians
231(1)
7.5.3 Conjunctive System and Transversality Conditions
232(1)
7.5.4 Computational Algorithm
233(4)
7.6 Optimality and Complexity Analysis
237(6)
7.6.1 Optimality and Existence Analysis
237(1)
7.6.2 Analysis of the Algorithm Complexity
238(1)
References
239(4)
8 Simulation Applications to Structural Dynamics in Service and Manufacturing Supply Chain Risk Management
243(32)
8.1 Simulation Model of Service Supply Chain Design with Facility Disruption Considerations
243(10)
8.1.1 Brief Overview
243(1)
8.1.2 Verbal Problem Description
244(1)
8.1.3 Problem Statement and Modelling Approach
245(1)
8.1.4 Data for Simulation
246(1)
8.1.5 Simulation Results
247(3)
8.1.6 Managerial Insights
250(3)
8.2 Simulation Model of Supply Chain Planning with Production Capacity Disruption Considerations
253(8)
8.2.1 Brief Overview
253(1)
8.2.2 Verbal Problem Statement
253(1)
8.2.3 Problem Statement and Modelling Approach
254(2)
8.2.4 Data for Experiments
256(1)
8.2.5 Experimental Results
257(2)
8.2.6 Testing and Verification
259(1)
8.2.7 Managerial Insights
260(1)
8.3 Single Versus Dual Sourcing Analysis with Disruption Considerations
261(5)
8.3.1 Brief Overview
261(1)
8.3.2 Problem Statement
261(1)
8.3.3 Modelling Approach
262(1)
8.3.4 Experiments
263(3)
8.4 Managerial Insights
266(2)
8.5 Simulation Application to Supply Chain Structural Dynamics Analysis
268(7)
8.5.1 Simulation Framework
269(1)
8.5.2 Application of Simulation Modelling to Supply Chain Structural Dynamics
270(2)
References
272(3)
9 Entropy-Based Supply Chain Structural Complexity Analysis
275(18)
9.1 Supply Chain Structural Dynamics and Complexity
275(3)
9.1.1 Supply Chains as Complex Systems
275(2)
9.1.2 Problem Statement
277(1)
9.2 Supply Chain Adaptation Potential
278(6)
9.2.1 Quantitative Estimation of Adaptation Potential: Basic Computation
279(2)
9.2.2 Quantitative Estimation of Adaptation Potential: Extension
281(3)
9.3 Adaptation Potential-Based-Identification of Methods for Supply Chain Design
284(2)
9.4 Practical Aspects of the Adaptation Potential Calculation
286(2)
9.5 Estimation of Supply Chain Adaptation Potential Under Terms of Outsourcing
288(5)
References
292(1)
10 New Drivers for Supply Chain Structural Dynamics and Resilience: Sustainability, Industry 4.0, Self-Adaptation
293(22)
10.1 Case Studies
293(7)
10.1.1 Case Nissan: Resilient Supply Chain
293(2)
10.1.2 Toyota: Supply Chain Disruption Management
295(1)
10.1.3 Capacity Flexibility at Volkswagen
296(3)
10.1.4 Volkswagen and Prevent Group Legal Dispute: Impact on the Supply Chain
299(1)
10.1.5 Case Study ASOS: Building Resilient Supply Chains Using Back-Up Facilities
300(1)
10.2 Disruption Risks Management and Supply Chain Sustainability
300(3)
10.3 Structural Dynamics in the Framework of Industry 4.0
303(12)
10.3.1 Industry 4.0 as a New Driver for Supply Chain Structural Dynamics
303(1)
10.3.2 Vision of Adaptive Supply Chain Management Framework
304(7)
References
311(4)
Index 315
Prof. Dr. habil. Dr. Dmitry Ivanov is professor of Supply Chain Management at Berlin School of Economics and Law (BSEL). He has been teaching classes for more than 15 years in operations management, production and supply management, supply chain management, logistics, management information systems, and strategic management at undergraduate, master's, PhD, and executive MBA levels at different universities worldwide in English, German, and Russian. He studied industrial engineering and production management in St. Petersburg and Chemnitz and graduated with distinction. He gained Dr.rer.pol., Doctor of Science (ScD), and Dr.habil. degrees. He was awarded the German Chancellor Scholarship. His research explores supply chain structural dynamics and control, with an emphasis on global supply chain design with disruption consideration, distribution planning, and dynamic re-scheduling. He is the co-author of more than 270 publications. He is Chairman of IFAC TC 5.2 Manufacturing Modelling for Management and Control.