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As global Supply Chains (SCs) face increasing complexities and risks, organizations must balance operational efficiency with preparedness for unforeseen disruptions. Recent events, including the devastating floods or wildfires and the impacts of the volatility of international relations, have underscored the vulnerability of SCs. The study explores the critical need for combining supply chain management, risk management, and sustainability for the systematic analysis of disruption risks from the unpredictability of natural disasters, man-made events, and rapid technological advancements. We develop a decision support system integrating the fuzzy C-means clustering and integrated multi-criteria decision-making approach for risk categorization and prioritization, respectively. The developed framework considers the significance of multiple risk factors (e.g., urgency and vulnerability) in the risk disruption analysis process through the hybrid Bayesian best-worst method-combined compromise solution approach. This enables managers to identify critical disruption risks (e.g., communication network disruptions, production facility-related risk, and increased demand for certain goods) while observing their adverse effects on the resiliency of sustainable SCs. Compared to the traditional risk priority number, the proposed system includes the importance of risk factors and provides a more stable and separable ranking, empowering managers to deal with potential resource limitations. This study also suggests risk mitigation strategies to alleviate the negative consequences of disruptions within organizational constraints and improve sustainable SC responsiveness to future disasters. • Incorporating supply chain risk analysis into a disruption management framework. • Developing a decision support system for clustering and prioritizing disruption risks. • Considering vulnerability and non-recoverability in the risk prioritization process. • Exploring strategies to mitigate disruption risks to enhance supply chain resilience.
Ferdous et al. (Fri,) studied this question.
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