2022/02/22 by Chunguang Bai, Qingyun Zhu, Joseph Sarkis · 2 citations
Business, Management and Accounting · Engineering · Mathematics · #Artificial intelligence #Business #Circular economy #Computer science #Economics #Environmental Sustainability in Business #Fuzzy logic #Fuzzy set #Machine learning #Management science #Marketing #Mathematics #Operations research #Regret #Selection (genetic algorithm) #Set (abstract data type) #Sociology #Supplier evaluation #Supply chain #Supply chain management #Sustainable Building Design and Assessment #Sustainable Supply Chain Management #Typology #Value (mathematics)
paper · doi:10.1080/00207543.2022.2037779
openalex publication_date 2022/02/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
The circular economy (CE) seeks to maintain products and materials at their highest utility and value. The organisational and governmental policy have seised onto the CE philosophy to advance socio-economic and environmental development. CE remains an essentially contested concept – making its utilisation as a foundation for managerial and policy decisions challenging. Circularity assessment has not been systematically adopted, especially within supply chain management. Using critical scholarly and practical evidential foundation, we proposed a comprehensive set of metrics that can be utilised in supplier selection, monitoring, and development for circularity. These metrics include the macro, meso, and micro levels. A group decision-making method integrating best-worst method (BWM), regret theory (RT), and dual hesitant fuzzy sets (DHFS) for circular economy and circularity (CEC) supplier evaluation and selection is introduced – providing instrumental value for the identified metrics typology. The proposed BWM-DHFE-RT integrative analytical method can accommodate decisionmaker psychological behaviour under uncertainty while simultaneously capturing divergent or conflicting opinions of different decision-makers. An illustrative business scenario is utilised to demonstrate the application of the proposed method. Though the proposed CE performance metrics and methodology are used for CEC supplier management reasons they have broader applicability. Future research and application directions are discussed.