A calibrated Fuzzy Best-Worst-method to reinforce supply chain resilience during the COVID 19 pandemic

Abstract

Making decisions require increasingly more sophisticated methods. The Best-Worst method was developed recently to find a good balance between the amount of information required and its consistency. The Fuzzy Best-Worst method was then proposed to integrate the uncertainty and imprecision of evaluations. In this paper, we propose the Calibrated Fuzzy Best-Worst Method, which enables us to define personalized fuzzy numbers. This new method was applied to the real case study of a global fashion supply chain suffering a dramatic drop of demand due to the COVID-19 shocks. Since its upstream was leading to a ripple effect on the rest of the supply chain stages, our study focused on evaluating the viability of its distribution channels based on weighted resilience capabilities and performance indicators. The proposed method allowed being more precise than the traditional Best-Worst method because it personalizes the uncertainty and subjectivity. The results revealed that cash flow, revenues, and inventory turnover were the most important performance indicators in the supply chain resilience. Financial strength, adaptability, and market position were shown to be the most critical resilience capabilities in preserving supply chain survivability with low-demand items and longer disruptions. Their effects explain why e-commerce was the most viable distribution channel.