Industrial Supply Chain Performance Benchmarking using Fuzzy Grey Relation Method

Present work aims to develop an efficient decision support system (DSS) to facilitate supply chain performance appraisement, benchmarking and related decision-making. Supply chain performance extent can be attributed as a function of multiple criteria/attributes. Most of the criterions/attributes being intangible in nature; supply chain performance appraisement relies on the subjective judgment of the decision-makers. Moreover, quantitative appraisement of supply chain performance appears very difficult due to involvement of illdefined (vague) performance measures as well as metrics. In order to overcome this, the study explores the concept of fuzzy logic in order to tackle incomplete and inconsistent subjective judgment of the decisionmakers. In this work, a performance appraisement index system has been postulated to gather evaluation information data (weights and ratings) in relation to supply chain performance measures and metrics. Combining the concepts of fuzzy set theory, entropy, ideal, and grey relation analysis, a fuzzy grey relation method for supply chain performance benchmarking problem has been presented. First, triangular fuzzy numbers and linguistic values characterized by triangular fuzzy numbers have been used to evaluate the importance weights of all criteria and the superiority of all alternatives versus various criteria above the alternative level. Then, the concept of entropy has been utilized to solve the adjusted integration weight of all objective criteria above the alternative level. Moreover, using the concepts of ideal, the grey ration grades of various alternatives versus ideal solution have been ranked to determine the best alternative. Finally, an empirical example of selecting most appropriate industry Saurav Datta et al 776 (corresponding supply chain) has been used to demonstrate the applicability of the aforesaid approach. The study results showed that this method appears to be an effective means for tackling MultiCriteria Decision Making (MCDM) problems in uncertain environments. Empirical data have been analyzed and results obtained thereof, have been reported to exhibit application potential of the said fuzzy grey relation based decision-support systems in appropriate situation.