Performance evaluation of supply chain resilience in epidemic crises: an extension of network non-parametric methodology

In response to the imperative to fortify supply chain (SC) resilience amidst deep uncertainty, particularly in the context of a pandemic, this study endeavors to introduce a pioneering methodology termed fuzzy network data envelopment analysis. The developed model stands out for its adaptability, being capable of accommodating an array of membership functions tailored to varying network configurations. By synthesizing complex network dynamics into a unified framework, the proposed methodology offers a robust solution for evaluating SC resilience. Notably, its integration of a single objective function addresses disparate network complexities through efficient linear programming techniques, thereby providing precise resilience assessments essential for strategic decision-making. We present an empirical study that aims to conduct a thorough assessment and enhancement of the resilience within face mask supply chains, comprising suppliers, producers, and distributors, to demonstrate the capabilities of our novel model. The findings of this study provide insights into enhancing the performance of SCs in the face of unexpected events such as COVID-19, how uncertainty can affect the performance of SCs, and how inefficiencies can be identified and improved.