Green supplier selection using multi-criterion decision making under fuzzy environment: A case study in automotive industry

In the past few decades, it has been widely observed that environmental awareness is continuously increasing among people, stakeholders, and governments. However, rigorous environmental rules and policies pushed organizations to accept affirmative changes like green supply chain management practices in their processes of the supply chain. Selection of green supplier is a tedious task and comprises a lot of challenges starting from evaluation to their final selection, which is experienced by supplier management professionals. The development and implementation of practical decision-making tools that seek to address these challenges are rapidly evolving. In the present work, the evaluation of a set of suppliers is primarily based on both conventional and environmental criteria. This work proposes a multi-criteria decision making (MCDM) based framework that is used to evaluate green supplier selection by using an integrated fuzzy Analytical Hierarchy Process (AHP) with the other three techniques namely MABAC (Multi-Attributive Border Approximation Area Comparison), WASPAS (Weighted Aggregated Sum-Product Assessment) and TOPSIS (Technique for order preference by similarity to ideal Solution). Initially, six green supplier selection environmental criteria (Environmental management system, green image, staff environment training, eco-design, pollution control, and resource consumption) and three conventional criteria (price, quality and service level) have been identified through literature review and expert's opinions to employ MCDM approach. A real-world case study of the automotive industry in India is deliberated to exhibit the proposed framework applicability. From AHP findings, 'Environment management system', 'Pollution control', 'Quality', and 'Green image' have been ranked as the topmost four green supplier selection criteria. Besides, the consistency test was performed to check the uniformity of the expert's input whereas the 'robustness' of the approach was tested by performing sensitivity analysis. The results illustrate that the applied fuzzy hybrid methods reach common green supplier rankings. Moreover, out of the four green supplier's alternatives, supplier number 'one' got the highest rank. This shows that the applied models are robust in nature. Further, this study relinquishes a single platform for the selection of green supplier under fuzzy environment. The applied methodology and its analysis will provide insight to decision-makers of supplier selection. It may aid decision-makers and the procurement department not only to differentiate the significant green supplier selection criteria but also to assess the most efficient green supplier in the supply chain in the global market.