Data-driven sustainable supply chain management with MEREC-EDAS approach using bipolar fuzzy credibility numbers

This paper presents the novel concept of bipolar fuzzy credibility numbers (BFCNs) which is a robust extension of bipolar fuzzy numbers (BFNs). The BFCNs integrate credibility measures to effectively manage bipolarity and vagueness. A new multi-criteria decision making (MCDM) technique is proposed which determine objective weights by evaluating the relevance of the criteria with “method based on the removal effects of criteria” (MEREC). The modified “evaluation based on distance from average solution” (EDAS)” method is applied to rank the feasible alternatives. Einstein operations are used to construct new AOs namely bipolar fuzzy credibility Einstein weighted averaging (BFCEWA) and bipolar fuzzy credibility Einstein weighted geometric (BFCEWG) operators. Furthermore, bipolar fuzzy credibility Einstein ordered weighted averaging (BFCEOWA) and bipolar fuzzy credibility Einstein ordered weighted geometric (BFCEOWG) operators are also developed to prioritize the objects using score function. The MEREC-EDAS approach is proposed for sustainable solution in real-life problems involving bipolarity and vagueness. A real-world case study is conducted to demonstrate the practical application of the MEREC-EDAS approach for evaluating the most effective supply chain management (SCM) strategy in e-commerce.