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

Sarker; M.R.; Rahman; M.S.; Ali; S.M.; Ibne Hossain; N.U.I.; Santibañez-González; E.D.R.S.

Keywords: Approximate fuzzy arithmetic; COVID, 19; Supply chain resilience; Uncertainty

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. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

Más información

Título según WOS: ID WOS:001040196400001 Not found in local WOS DB
Título según SCOPUS: Modeling supply chain resilience drivers in the context of COVID-19 in manufacturing industries: leveraging the advantages of approximate fuzzy DEMATEL
Título de la Revista: Journal of Intelligent Manufacturing
Volumen: 36
Número: 5
Editorial: Springer
Fecha de publicación: 2025
Página de inicio: 2939
Página final: 2958
Idioma: English
DOI:

10.1007/s10845-023-02181-6

Notas: ISI, SCOPUS