Modeling supply chain resilience drivers in the context of COVID-19 in manufacturing industries: leveraging the advantages of approximate fuzzy DEMATEL

Md. Rayhan Sarker (), Md. Sazid Rahman (), Syed Mithun Ali (), Niamat Ullah Ibne Hossain () and Ernesto D. R. Santibanez Gonzalez ()
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Md. Rayhan Sarker: Bangladesh University of Engineering and Technology
Md. Sazid Rahman: University of Arkansas
Syed Mithun Ali: Bangladesh University of Engineering and Technology
Niamat Ullah Ibne Hossain: Arkansas State University
Ernesto D. R. Santibanez Gonzalez: University of Talca

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 5, No 1, 2939-2958

Abstract: Abstract The COVID-19 pandemic has emerged as a global threat that is making industrial managers rethink their supply chain (SC) structures in an uncertain business environment. Because of the COVID-19 pandemic, global SCs have been severely disrupted, triggering the need for a supply chain resilience (SCR) model. Based on the literature and expert input, 15 SCR drivers for the manufacturing industry were identified in three categories, namely, absorptive, adaptive, and restorative capacity. The approximate fuzzy Decision Making Trial and Evaluation Laboratory (AFDEMATEL) method was used to categorize these 15 SCR drivers into cause-and-effect groups and produce a priority list of the SCR drivers. System robustness, geographically dispersed multiple suppliers, and risk management culture are the top three critical SCR drivers, respectively, in the cause group, and all three are associated with the absorptive capacity of the manufacturing industry. Agile supply chain, contingency planning, and restoration of resources are the least important drivers, respectively, in the effect group. In an uncertain environment, the critical SCR drivers are system robustness and risk management culture. The study results will help supply chain managers formulate strategic policies to achieve supply chain resilience in an uncertain business environment.

Keywords: Approximate fuzzy arithmetic; COVID-19; Supply chain resilience; Uncertainty (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-023-02181-6

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