This book provides a comprehensive overview of recent developments in network dynamics and control with applications to supply chains, manufacturing and logistics systems. It systemizes these developments in the form of new taxonomies and methodological principles to shape the research domain of supply network dynamics control. Uniquely, the book links the fundamentals of control and system theories and artificial intelligence with supply chain and operations management. It addresses the needs of researchers and practitioners alike, revealing the challenges and opportunities of supply chain and operations management by means of dynamic system analysis.
Author(s): Alexandre Dolgui, Dmitry Ivanov, Boris Sokolov
Series: Springer Series in Supply Chain Management, 20
Publisher: Springer
Year: 2022
Language: English
Pages: 212
City: Cham
Contents
1 Introduction to Supply Network Dynamics and Control
References
2 Digital Transformation Process Towards Resilient Production Systems and Networks
Acronyms
2.1 Introduction
2.1.1 Supply Chain Management Initiatives
2.1.2 Building Supply Chain Resilience Through Digital Transformation
2.2 Literature Review
2.2.1 Supply Chain Management
2.2.2 Supply Chain Disruption Propagation/Ripple Effect
2.2.3 Resilient Manufacturing
2.2.4 Smart Manufacturing
2.2.5 Supply Chain Resilience
2.2.6 Novel Technologies Utilized for Disruption Response
2.3 Production Networks Modeling and Control Towards Mass Personalization
2.3.1 State of the Research: Case Studies
2.3.2 Disruptions as a Catalyst for Business Models Change
2.3.3 Digital Transformation Challenges in the Manufacturing Industry
2.3.4 Digital Transformation Strategy
2.3.5 A Holistic Approach of Digital Business Transformation
2.4 Framework for Digital Transformation in Manufacturing
2.4.1 Digital Acceptance
2.4.2 SCM Towards Reduced Complexity and Uncertainty
2.5 Discussion and Outlook
2.5.1 Impact of COVID-19 Disruption on Smart Manufacturing
2.5.2 Supply Chain Lessons Learned from COVID-19
2.5.3 Flexible Supply Chain
2.5.4 The Importance of a Supply Chain with Revenue Assurance
2.5.5 The Importance of a Visible Supply Chain
2.5.6 The Importance of Logistics
2.5.7 The Importance of Supply Chain Risk Management
2.6 Conclusion
References
3 Collaborative Control, Task Administration, and Fault Tolerance for Supply Chain Network-Dynamics
3.1 Introduction
3.2 Collaborative Fault Tolerance and Resilience by Teaming Framework for Collaborative Supply Networks
3.2.1 The RBT Formalism
3.2.2 The RBT Framework
3.2.3 STF/RP, SFCP, DNF/RP, and DFCP
3.2.4 RBT Case Studies
3.3 Collaborative Demand and Capacity Sharing
3.4 Task Administration Protocols for Handling Supply Network Dynamics
3.5 Collaborative Response to Disruption Propagation
3.5.1 The CRDP Framework
3.5.2 The CRDP Case Studies
3.6 Food Supply Chain Security by Agricultural Robotic Systems for Early Detection, Diagnosis, and Treatment
3.6.1 ARS Framework
3.6.2 ARS Case Studies
3.6.2.1 Case 1: Communication and Connection in ARS (Guo et al., 2018)
3.6.2.2 Case 2: Collaboration of ARS's Agents (Dusadeerungsikul & Nof, 2019)
3.6.2.3 Case 3: Real-Time Information Updating in ARS (Dusadeerungsikul & Nof, 2021)
3.7 Cyber Collaborative Control, Optimization, and Harmonization of Smart Warehouse, a Key Element of Supply Network
3.7.1 Cyber Collaborative Warehouse (C2W) Design Concept
3.7.2 C2W Case Study
3.7.3 C2W in the Supply Network
3.8 Conclusion
References
4 Managing Supply Chain Disruption by Collaborative Resource Sharing
4.1 Introduction
4.2 Theoretical Background
4.2.1 Supply Chain Resilience and Robustness
4.2.2 Collaborative Resource Sharing
4.2.3 Relational View Theory
4.2.4 Trust and Commitment
4.3 Methodology
4.4 Findings
4.5 Discussion and Conclusion
References
5 Reconfigurable Strategies to Manage Uncertainties in Supply Chains Due to Large-Scale Disruptions
5.1 Introduction
5.2 SC Uncertainties, Sources, and Impacts
5.2.1 Uncertainties in Supply Chains (SCs)
5.2.2 Sources of SC Uncertainty and Vulnerability
5.2.2.1 Environmental Uncertainties
5.2.2.2 Economic Uncertainties
5.2.2.3 Operational and Technical Uncertainties
5.2.2.4 Human Thinking and Decision-Making Uncertainties
5.2.3 SC Uncertainty Due to Large-Scale Disruptions
5.2.4 Impacts of Uncertainties in SCs Due to Large-Scale Disruptions
5.2.4.1 Impact on Demand Management
5.2.4.2 Impact on Supply Management
5.2.4.3 Impact on Production Management
5.2.4.4 Impact on Transportation and Delivery Management
5.2.4.5 Impact on Information Management
5.2.4.6 Impact on Financial Management
5.2.4.7 Impact on SC Sustainability Performance
5.2.5 Planning and Strategies for SC Uncertainties and Observations
5.3 Reconfigurable Strategies to Manage SC Uncertainties Due to Large-Scale Disruptions
5.3.1 Reconfigurable Strategies to Manage Demand Uncertainties
5.3.2 Reconfigurable Strategies to Manage Supply Uncertainties
5.3.3 Reconfigurable Strategies to Manage Production Uncertainties
5.3.4 Reconfigurable Strategies to Manage Transportation and Delivery Uncertainties
5.3.5 Reconfigurable Strategies to Manage Information Management Uncertainties
5.3.6 Reconfigurable Strategies to Manage Financial Uncertainties
5.3.7 Reconfigurable Strategies for Supply Chain Sustainability
5.4 Modeling Methods for the Evaluation of the Strategies
5.5 Conclusions
References
6 Impact of Additive Manufacturing on Supply Chain Resilience During COVID-19 Pandemic
6.1 Introduction
6.2 State of the Art on Impacts of AM on Supply Chain
6.2.1 Impacts of AM on Managerial Level
6.2.2 Impact of AM on Operational Level
6.3 AM in the Fight Against COVID-19 Pandemic
6.4 Simulation
6.5 Results and Discussion
6.6 Conclusions
References
7 Short-Term Routing Models for COVID-19 Treatment Transfer Between Hospitals
7.1 Introduction
7.2 Literature Review
7.3 Models
7.3.1 Problem Definition
7.3.2 Mathematical Model
7.4 Experiments
7.4.1 Experiment 1
7.4.2 Experiment 2
7.4.3 Experiment 3
7.5 Conclusion
References
8 AI-Enhanced Maintenance for Building Resilience and Viability in Supply Chains
8.1 Introduction
8.2 Literature Analysis
8.2.1 Resilience and Viability in Supply Chain Management
8.2.2 Dynamic Bayesian Networks
8.3 Application of Dynamic Bayesian Network in Industrial Maintenance
8.3.1 Data Preparation and Analysis
8.3.2 Manual Modeling of the Dynamic Bayesian Network
8.3.3 Probability Values of KRIs
8.3.4 Determination of the Probability Values of the RIs and PIs
8.3.5 Determination of the Probability Values of the KPIs
8.3.6 Manual Modeling of the DBN
8.4 Discussion of Results
8.5 Outlook
References
9 Building Viable Digital Business Ecosystems with Collaborative Supply Chain Platform SupplyOn
9.1 Introduction
9.2 Data Completeness and Data Quality
9.2.1 Importance of Data Visibility to Cope with Supply Chain Disruptions and Crises
9.2.2 Partnership and Trust
9.3 Digital Supply Chain: Vision and Technology
9.3.1 Vision of a Digital Supply Chain
9.3.2 Digital Supply Chain Technology
9.4 How SupplyOn Helps to Foster Resilience and Viability
9.4.1 Managing the Supply Chain Complexity in the Context of Digital Transformation
9.4.2 SupplyOn Security Approach
Process and Security Management
Data Privacy
Data and Application Strategy
Security Infrastructure
9.5 Building a Digital Supply Chain Platform: How SupplyOn Supports the Supply Chain in the Automotive Industry
9.5.1 Overview of the SupplyOn Solution Suite
9.5.2 Deep Dive for Demand Processes
9.5.3 Deep Dive for Advance Shipping Notification
9.5.4 Deep Dive for Goods Receipt
9.5.5 Deep Dive for Finance Processes
9.6 Case Study Seres
9.6.1 The Art of Manufacturing Electronic Vehicles at Seres Automobile
9.6.2 The Challenge of C2M
9.6.3 Seres' “321 Supply Chain System”
9.6.4 From Traditional Supply Chain to Supply Chain Ecosystem
9.6.5 Seres Smart Manufacturing Is Based on Three Pillars
9.6.6 A Flexible SCM Platform for Global Needs
9.7 Conclusion
References
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