Track: Supply Chain Sustainability / Green Supply Chain

Abstract

To ensure the flow of products with different classes in supply chains such as closed loop supply chains, etc., managing quality uncertainty estimation is a cost-effective, profitable, and environment-friendly contribution by establishing standard recovery treatments of cores. The paper compares the quality values obtained from Bayesian analysis with deterministic levels to analyze the core retrieval strategies. The uncertainty effects due to several quality factors are demonstrated in both the inspection and disassembly stages. Such as misclassification that occurred because of overlapping of quality ranges in consideration with fuzzy evaluation during value and material recovery.

Moreover, the adopted dual methodology invalidates the legitimacy of providing probability distribution to unknown variables in previously developed models. Both probabilistic and prescriptive models are moderated by the qualitative assessment of cores facilitated by realistic scenarios. With the help of the proposed mixed approaches, the current research contributes to the knowledge of core retrieval management by incorporating the quality variability in the model at the product level. The result demonstrates that quality estimates ensure precise eligibility and misclassification detection of returns of cores at the initial stages well-before exhaustive remanufacturing operations.