| Nota di contenuto |
Intro -- 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 -- Internet Links:.
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Proceedings of ECSF 2021 : engineering, construction, and infrastructure solutions for innovative medicine facilities / / edited by Dmitry Ivanov, Aleksandr Panin, and Inna Sukhanova
