Fermatean fuzzy CRITIC-CODAS-SORT for characterizing the challenges of circular public sector supply chains

Abstract

The challenges of implementing circular supply chains (CSCs) are well documented, but their characterization in public sector supply chains is unavailable. Thus, this paper closes such a gap by viewing it as a multiple criteria sorting (MCS) problem. Following a systematic literature review of identifying CSC challenges, they are analytically mapped to public sector characteristics through a novel integration of Criteria Importance Through Intercriteria Correlation (CRITIC) and Combinative Distance-based Assessment (CODAS)-SORT (CODAS-SORT) methods under a Fermatean fuzzy environment. Unlike other comparable methods, the proposed Fermatean fuzzy CRITIC-CODAS-SORT is a computationally efficient MCS tool that only requires minimal cognitive workload from expert decision-makers. The Fermatean fuzzy CRITIC generates the weights of domain characteristics, while the Fermatean fuzzy CODAS-SORT assigns the CSC challenges to predefined classes. Findings reveal that inadequate top management support, inadequate government policies, lack of interest in the concept of circularity, lack of incentives to adopt CSC practices, restrictive collection policies, inadequate knowledge on CSC, and size and complexity of supply chains are the most relevant CSC challenges in the public sector. These findings would contribute to the design of effective initiatives and resource allocation decisions in advancing the circularity of supply chains in the public sector. Various policy insights are proposed, along with some future research agenda.

Keywords

• Multiple criteria sorting
• multi-attribute decision-making
• uncertainty modelling
• soft computing
• circular economy