A large multi-group decision-making technique for prioritizing the big data-driven circular economy practices in the automobile component manufacturing industry

Abstract

Abstract The present study uses a large group decision-making technique to identify and rank the best big data-driven circular economy (BDDCE) practices in the auto-component industry. The data pertaining to the BDDCE practices were collected from the decision-makers in three groups, namely, purchasing, manufacturing, and logistics & marketing function from the auto-component manufacturing industry. First, the consensus on the BDDCE practices within the group was ascertained followed by determining the decision weights using the percentage distributions and subjective weights. This was followed by the by computing the dominance degrees on pairwise comparisons of the BDDCE practices and ranking them using the PROMETHEE II method. The findings indicated that the BDDCE practices that were more inclined towards the enhancement of internal supply chain integration were most preferred and highly ranked by the decisionmakers in the auto-component industry as compared to the practices that were focused on improving the supplier and customer interfaces such as green purchasing, sale of excess inventory, and developing recycling systems for end-of-life products and materials . The high ranked BDDCE practices included minimization of the raw material consumption, plan for reuse, recycle, recovery of material, parts, and reduction of the process waste at the design stage.