A decision model for a strategic closed-loop supply chain to reclaim End-of-Life Vehicles

Ravi Shankar, Shisam Bhattacharyya and Alok Choudhary

International Journal of Production Economics, 2018, vol. 195, issue C, 273-286

Abstract: Closed-loop supply chain strategies for End-of-Life (EOL) products and their logistics operations have received greater attention in recent years from supply chain research community. These strategies include warranty–based acquisition, quantity–based acquisition, quality–based acquisition, centrally coordinated logistics operations and third-party logistics (3PL) operations. The proposed research integrates two important aspects of an automobile's closed-loop supply chain strategy. The first aspect is optimal transportation planning for raw material parts, newly manufactured and EOL products in a closed-loop supply chain, using demand, collection rate and capacity of associated facilities in the network as functional parameters. We formulated a mixed integer mathematical model for the closed-loop supply chain network with a multi-echelon inventory, multi-period planning and multi-product scenario, which are used to compute the maximum contribution margin generated through different strategies. The second aspect pertains to using the output of the proposed model in first stage to handle the sequential form of a cooperative game. The proposed two–phase decision model analyzes the realization times and delivery limits of different products as an indicator of swapping different strategies. We analyze three instances to understand and validate the applicability of the model. In these scenarios, sensitivity analysis has been performed to demonstrate the robustness of the proposed model. We present managerial insights, leading to flexibility in decision making. It is observed that the demand, collection rate and capacity of network facilities create highly sensitive trilogy for the contribution margin of proposed network. The outcome of this research firstly confers optimal amounts of mass flows in the closed loop supply chain network from a state of the end product (new products, recycled products and non–recycled used products) to a state of the raw material (ferrous metal, non-ferrous metal and shredder fluff). Secondly, authors culminated a confound dichotomy among all reintegration strategies (conveyance, acquisition and cannibalization) by distinct enumeration and quantification (regarding realization times and delivery limits) of each one to forge a robust planning horizon for original equipment manufacturer.

Keywords: Closed-loop supply chain; Mixed integer programming; Sequential game; Logistics; Supply chain strategy; Optimization (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:195:y:2018:i:c:p:273-286

DOI: 10.1016/j.ijpe.2017.10.005

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