S. Sani
Strategies for Achieving Pre-emptive Resilience in Military Supply Chains
Sani, S.; Schaefer, D.; Milisavljevic-Syed, J.
Authors
Professor Dirk Schaefer D.Schaefer@hull.ac.uk
Dean, Faculty of Science and Engineering
J. Milisavljevic-Syed
Abstract
As technological advancement is rapidly evolving modern warfare, military supply chains are becoming more dynamic and complex with high vulnerability to unexpected disruptions. To increase their overall resilience against such unexpected disruptions, traditional approaches are no longer sufficient. To date, research on supply chain resilience has mainly focused on reactive responses and recovery strategies (post-disruption). Hence, the research gap addressed in this paper is that of identifying new and proactive strategies to enable pre-emptive resilience in military supply chains (pre-disruption). In this paper, the authors first provide a critical review of the pertinent literature and research conducted over the past 12 years. Following on from there, they identify new research directions for enabling pre-emptive resilience to aid military logistic planners in monitoring supply chains and strategic decision-making to maintain their resilience.
Citation
Sani, S., Schaefer, D., & Milisavljevic-Syed, J. (2022). Strategies for Achieving Pre-emptive Resilience in Military Supply Chains. Procedia CIRP, 107, 1526-1532. https://doi.org/10.1016/j.procir.2022.05.186
Presentation Conference Type | Conference Paper (published) |
---|---|
Conference Name | 55th CIRP Conference on Manufacturing Systems 2022 |
Acceptance Date | May 26, 2022 |
Online Publication Date | May 26, 2022 |
Publication Date | Jan 1, 2022 |
Deposit Date | Apr 3, 2024 |
Publicly Available Date | Apr 5, 2024 |
Journal | Procedia CIRP |
Print ISSN | 2212-8271 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 107 |
Pages | 1526-1532 |
DOI | https://doi.org/10.1016/j.procir.2022.05.186 |
Keywords | Military supply chain; Supply chain disruptions; Pre-emptive resilience; Simulation; Mathematical modelling; Decision support; Digital twin |
Public URL | https://hull-repository.worktribe.com/output/4618527 |
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Copyright Statement
© 2022 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)
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