The Application of Mamdani Fuzzy Inference System in Evaluating Green Supply Chain Management Performance

Abstract

Qualitative criteria for assessing green supply chain management (GSCM) performance are influenced by uncertainty, essentially due to the vagueness intrinsic to the evaluation of qualitative factors. This paper aims to decrease the uncertainty which is caused by human judgments in the process of GSCM performance evaluation employing linguistic terms and degrees of membership. In this study, a fuzzy set theory approach has been proposed for handling the linguistic imprecision and the ambiguity of human being’s judgment. It also pioneers applying the fuzzy inference system for evaluating GSCM performance of companies in terms of green criteria. In the proposed model, human reasoning has been modeled with fuzzy inference rules and has been set in the system, which is an advantage when compared to the models that combine fuzzy set theory with multi-criteria decision-making models. To highlight the real-life applicability of the proposed model, an empirical case study has been conducted. Findings reveal the usefulness of the proposed model in evaluating the performance of companies according to GSCM criteria with human linguistic terms. Findings also indicate that green design and green manufacturing dimensions have the highest impact on company performance. The robustness of the proposed FIS model has been proved with different defuzzification methods.