Abstract
Reverse Logistics (RL) has become an important factor for manufacturing industries to gain competitive edge, address environmental concerns and enhance supply chain sustainability. Due to lack of expertise, financial support and resources, manufacturers prefer outsourcing all activities related to the return flow of products to Third Party Reverse Logistics Providers (3PRLPs). However, in the current scenario, with regard to being truly sustainable, manufacturers are considering possibilities of collaboration with the 3PRLPs in order to reap benefits of better customer satisfaction, reduced environmental impact and generation of additional revenue. Thus, it becomes essential to analyze plausible Critical Success Factors (CSFs) which can aid in realizing sustainable collaboration with 3PRLPs. With this perspective, the present study seeks to recognize and examine CSFs related to the implementation of RL under sustainable collaboration with 3PRLPs. Further it attempts to develop an analytical structural model to study the contextual relationship among the identified CSFs and determine the most influential CSFs with the aid of fuzzy Interpretive Structural Modeling (ISM). The results obtained from fuzzy-ISM are further used as an input to fuzzy Matrix Impact of Cross-Multiplication Applied to Classification (MICMAC) analysis to classify the listed factors into four groups on the basis of their driving and dependence power. The uncertainty in the decision making environment is handled effectively using fuzzy set theory. The analytical model developed in the study yields a practical tool for the supply chain managers to centre focus primarily on the CSFs which exert the maximum dominance, to ensure effective implementation of RL under sustainable collaboration.
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Sharma, S., Darbari, J.D. (2021). Fuzzy MCDM Model for Analysis of Critical Success Factors for Sustainable Collaboration with Third Party Reverse Logistics Providers. In: Tiwari, A., Ahuja, K., Yadav, A., Bansal, J.C., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1393. Springer, Singapore. https://doi.org/10.1007/978-981-16-2712-5_51
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