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

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  • Shankar, Ravi
  • Bhattacharyya, Shisam
  • Choudhary, Alok

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.

Suggested Citation

  • Shankar, Ravi & Bhattacharyya, Shisam & Choudhary, Alok, 2018. "A decision model for a strategic closed-loop supply chain to reclaim End-of-Life Vehicles," International Journal of Production Economics, Elsevier, vol. 195(C), pages 273-286.
  • Handle: RePEc:eee:proeco:v:195:y:2018:i:c:p:273-286
    DOI: 10.1016/j.ijpe.2017.10.005
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    Citations

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    Cited by:

    1. Sung Hoon Chung & Robert D. Weaver & Hyun Woo Jeon, 2020. "Sustainable Management of Remanufacturing in Dynamic Supply Chains," Networks and Spatial Economics, Springer, vol. 20(3), pages 703-731, September.
    2. Sungki Kim & Nina Shin & Sangwook Park, 2020. "Closed-Loop Supply Chain Coordination under a Reward–Penalty and a Manufacturer’s Subsidy Policy," Sustainability, MDPI, vol. 12(22), pages 1-28, November.
    3. Zhanwei Tian & Guoqing Zhang, 2021. "Multi-echelon fulfillment warehouse rent and production allocation for online direct selling," Annals of Operations Research, Springer, vol. 304(1), pages 427-451, September.
    4. Dong-Her Shih & Chia-Ming Lu & Chia-Hao Lee & Sin-Ye Cai & Kuo-Jui Wu & Ming-Lang Tseng, 2018. "Eco-Innovation in Circular Agri-Business," Sustainability, MDPI, vol. 10(4), pages 1-13, April.
    5. Sonu Rajak & K. E. K. Vimal & Sricharan Arumugam & Jagadesan Parthiban & Swesh Kannan Sivaraman & Jayakrishna Kandasamy & Angel Acevedo Duque, 2022. "Multi-objective mixed-integer linear optimization model for sustainable closed-loop supply chain network: a case study on remanufacturing steering column," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(5), pages 6481-6507, May.
    6. Simonetto, Marco & Sgarbossa, Fabio & Battini, Daria & Govindan, Kannan, 2022. "Closed loop supply chains 4.0: From risks to benefits through advanced technologies. A literature review and research agenda," International Journal of Production Economics, Elsevier, vol. 253(C).
    7. Meng, Kai & Cao, Ying & Peng, Xianghui & Prybutok, Victor & Gupta, Varun, 2020. "Demand-dependent recovery decision-making of a batch of products for sustainability," International Journal of Production Economics, Elsevier, vol. 224(C).
    8. Meiling He & Tianhe Lin & Xiaohui Wu & Jianqiang Luo & Yongtao Peng, 2020. "A Systematic Literature Review of Reverse Logistics of End-of-Life Vehicles: Bibliometric Analysis and Research Trend," Energies, MDPI, vol. 13(21), pages 1-22, October.
    9. Fatin Amrina A. Rashid & Hawa Hishamuddin & Nizaroyani Saibani & Mohd Radzi Abu Mansor & Zambri Harun, 2022. "A Review of Supply Chain Uncertainty Management in the End-of-Life Vehicle Industry," Sustainability, MDPI, vol. 14(19), pages 1-28, October.
    10. Luttiely Santos Oliveira & Ricardo Luiz Machado, 2021. "Application of optimization methods in the closed-loop supply chain: a literature review," Journal of Combinatorial Optimization, Springer, vol. 41(2), pages 357-400, February.
    11. D'Adamo, Idiano & Gastaldi, Massimo & Rosa, Paolo, 2020. "Recycling of end-of-life vehicles: Assessing trends and performances in Europe," Technological Forecasting and Social Change, Elsevier, vol. 152(C).
    12. Dong-Her Shih & Chia-Ming Lu & Chia-Hao Lee & Yuh-Jiuan M. Parng & Kuo-Jui Wu & Ming-Lang Tseng, 2018. "A Strategic Knowledge Management Approach to Circular Agribusiness," Sustainability, MDPI, vol. 10(7), pages 1-20, July.

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