Supply Chain Risk Analysis through the Computational Method

Supply chain management (SCM) requires risk analysis for the sustainable development of organizations such as retail, healthcare, information technology, and media.SCM has set ambitious goals and requirements for organizations to increase their share of productivity.However, considering the various criteria and factors involved in the process, selecting and deciding on the optimal SCM source can be challenging for organizations.In addressing this challenge, selection priority and risk analysis factors in SCM and alternatives are important.This challenge was resolved using the hesitant fuzzy-analytic hierarchy process (HF-AHP) and hesitant fuzzy-technique for order preference by similarity to ideal solution (HF-TOPSIS).The proposed approach considers numerous criteria, assigning weights using the HF-AHP method.Natural disasters are assigned the highest weight and geopolitical uncertainty the lowest weight.Within these groups, among subfactors, hurricane has the highest weight and economic conditions the lowest weight.HF-TOPSIS ranks the SCM alternatives, whereby systematic SCM has the highest priority and mitigation strategy the lowest priority.The proposed strategy can maintain the dynamics of choosing the ideal SCM, providing significant knowledge to policymakers and SCM partners.