Quantitative Modelling of Supply Chain Disruption Propagation Dynamics

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Quantitative Modelling of Supply Chain Disruption Propagation Dynamics
Title:
Quantitative Modelling of Supply Chain Disruption Propagation Dynamics
Journal Title:
7th International Conference on Knowledge Innovation and Invention 2024 (ICKII 2024)
DOI:
Publication URL:
Publication Date:
30 December 2024
Citation:
Liu Shudong, Xiang Shili and Wang Lu. "Quantitative Modelling of Supply Chain Disruption Propagation Dynamics," in Proceedings of ICKII 2024.
Abstract:
Supply chain disruptions, caused by both natural disasters and human-made incidents, have inflicted substantial losses on numerous companies. The management of supply chain risks, including disruption risk, has garnered significant attention from both academia and industries over the past few decades. It is imperative for companies to develop effective solutions for disruption risk management. This work is directly motivated by a partner company seeking an effective solution with disruption event monitoring, analysis and assessment, early warning and mitigation plans. This study delves into the mechanisms and dynamics of disruption propagation along the supply chain. Specifically, we have developed quantitative mathematical models for the entire supply chain using system dynamics to obtain the characteristics of disruption propagation such as time spent for the disruption to propagate from the event location to a focal company and the severity of its impact. Crucially, our models yield important management insights that can be instrumental in development of disruption monitoring system, enhancing the resilience and robustness of supply chains, development of mitigation strategies aimed at minimizing the adverse effects of disruptions.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research - A*STAR – MiRXES Agile Diagnostics Manufacturing Joint Lab
Grant Reference no. : I2301E0017
Description:
© 2024 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2673-4591
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