Choosing the right supplier has a significant impact on the efficiency and productivity of a supply chain (SC). Different supplier selection models and approaches have been developed for reverse SCs. The lean, agile, resilient, and green (LARG) strategy is an innovative paradigm for supply chain competitiveness and sustainability. This study proposes a fuzzy-based methodology that integrates the fuzzy group best-worst method (FG-BWM) and the fuzzy combined compromise solution (FCoCoSo) method for supplier selection in reverse SCs within a LARG strategic paradigm. The FG-BWM is used to measure the importance weights of the supplier selection criteria. Subsequently, FCoCoSo is coupled with the normalized weighted geometric Bonferroni mean functions to select the most suitable supplier. The BWM is used for its simplicity and efficacy. The CoCoSo model is used for its unique ability to produce a compromise solution. The Bonferroni functions are used to capture the interrelationships among the decision attributes and eliminate the influence of extreme data. The main proposed framework aims at providing an easy-to-implement but reliable method to help manufacturers active in recycling and concerned with sustainability issues to rank and select suppliers. We present a real-world case study whose results demonstrate the applicability of the proposed integrated framework to supplier selection in reverse SCs, focusing in particular on the wood and paper industry.
Tavana, Madjid; Shaabani, Akram; Di Caprio, Debora; and Bonyani, Abbas, "An integrated group fuzzy best-worst method and combined compromise solution with Bonferroni functions for supplier selection in reverse supply chains" (2021). Business Systems and Analytics Faculty Work. 79.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.