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

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