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A multi-layer Bayesian network method for supply chain disruption modelling in the wake of the COVID-19 pandemic

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  • Seyedmohsen Hosseini
  • Dmitry Ivanov

Abstract

While the majority of companies anticipated the negative and severe impacts of the COVID-19 pandemic on the supply chains (SC), most of them lacked guidance on how to model disruptions and their performance impacts under pandemic conditions. Lack of such guidance resulted in delayed reactions, incomplete understanding of pandemic impacts, and late deployment of recovery actions. In this study, we offer a method of modelling and quantifying the SC disruption impacts in the wake of a pandemic. We develop a multi-layer Bayesian network (BN) model that can be used to identify SC disruption triggers and risk events amid the COVID-19 pandemic and quantify the consequences of pandemic disruptions. The unique features of BN, such as forward and backward propagation analysis, are utilised to simulate and measure the impact of different triggers on SC financial performance and business continuity. In this way, we combine resilience and viability SC perspectives and explicitly account for the pandemic settings. The outcomes of this research open a novel theoretical lens on application of BNs to SC disruption modelling in the pandemic setting. Our results can be used as a decision-support tool to predict and better understand the pandemic impacts on SC performance.

Suggested Citation

  • Seyedmohsen Hosseini & Dmitry Ivanov, 2022. "A multi-layer Bayesian network method for supply chain disruption modelling in the wake of the COVID-19 pandemic," International Journal of Production Research, Taylor & Francis Journals, vol. 60(17), pages 5258-5276, September.
    • Handle: RePEc:taf:tprsxx:v:60:y:2022:i:17:p:5258-5276
    DOI: 10.1080/00207543.2021.1953180
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    Cited by:

    1. Yu, Yaocheng & Shuai, Bin & Huang, Wencheng, 2024. "Resilience evaluation of train control on-board system considering common cause failure: Based on a beta-factor and continuous-time bayesian network model," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    2. David Berlepsch & Fred Lemke & Matthew Gorton, 2024. "The Importance of Corporate Reputation for Sustainable Supply Chains: A Systematic Literature Review, Bibliometric Mapping, and Research Agenda," Journal of Business Ethics, Springer, vol. 189(1), pages 9-34, January.
    3. Rasoul Amirzadeh & Asef Nazari & Dhananjay Thiruvady & Mong Shan Ee, 2023. "Modelling Determinants of Cryptocurrency Prices: A Bayesian Network Approach," Papers 2303.16148, arXiv.org.
    4. Liu, Hui & Su, Bingbing & Guo, Min & Wang, Jingbei, 2024. "Exploring R&D network resilience under risk propagation: An organizational learning perspective," International Journal of Production Economics, Elsevier, vol. 273(C).
    5. Sawik, Tadeusz, 2023. "Reshore or not Reshore: A Stochastic Programming Approach to Supply Chain Optimization," Omega, Elsevier, vol. 118(C).
    6. Choudhury, Nishat Alam & Ramkumar, M. & Schoenherr, Tobias & Singh, Shalabh, 2023. "The role of operations and supply chain management during epidemics and pandemics: Potential and future research opportunities," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).

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