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The Reverse Logistic Process of an Automobile Supply Chain Network Supported by a Collaborative Decision-Making Model

Author

Listed:
  • Jorge E. Hernández

    (CIGIP (Centro de Investigación Gestión eIngeniería de Producción), Universidad Politécnica de Valencia)

  • Raúl Poler

    (CIGIP (Centro de Investigación Gestión eIngeniería de Producción), Universidad Politécnica de Valencia)

  • Josefa Mula

    (CIGIP (Centro de Investigación Gestión eIngeniería de Producción), Universidad Politécnica de Valencia)

  • Francisco C. Lario

    (CIGIP (Centro de Investigación Gestión eIngeniería de Producción), Universidad Politécnica de Valencia)

Abstract

Decision system technologies have long since been a strong support to model and solve planning complexities in the supply chain in a collaborative context. Moreover, one of the main topics to emerge is reverse logistics, which is becoming more relevant in supply chains in terms of the logistics process of removing new or used products from their initial point. Therefore, to present the main aspects that should be considered to share the decision information, which is already used among the members of the supply chain, a study of reverse logistics has been carried out to discover how decision-making activities support the process in supply chains. Furthermore, a simulation experiment has been performed with both the DGRAI 3.0 tool and Rockwell Arena 11® to observe the quality evolution of decision making and the economical impact that the proposed collaborative model will have on the current system. Moreover, this research work shows that a clear impact will appear on the decisional quality at the bottom levels of the supply chain than on the decisional quality of the whole system. The main work hypothesis is that the logistic process costs must lower given the implementation of the proposed collaborative model.

Suggested Citation

  • Jorge E. Hernández & Raúl Poler & Josefa Mula & Francisco C. Lario, 2011. "The Reverse Logistic Process of an Automobile Supply Chain Network Supported by a Collaborative Decision-Making Model," Group Decision and Negotiation, Springer, vol. 20(1), pages 79-114, January.
    • Handle: RePEc:spr:grdene:v:20:y:2011:i:1:d:10.1007_s10726-010-9205-7
    DOI: 10.1007/s10726-010-9205-7
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    References listed on IDEAS

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    Cited by:

    1. Guo, Chunxiang & Liu, Xiaoli & Jin, Maozhu & Lv, Zhihan, 2016. "The research on optimization of auto supply chain network robust model under macroeconomic fluctuations," Chaos, Solitons & Fractals, Elsevier, vol. 89(C), pages 105-114.
    2. Govindan, Kannan & Soleimani, Hamed & Kannan, Devika, 2015. "Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future," European Journal of Operational Research, Elsevier, vol. 240(3), pages 603-626.
    3. Jorge E. Hernández & Andrew C. Lyons & Konstantinos Stamatopoulos, 2016. "A DSS-Based Framework for Enhancing Collaborative Web-Based Operations Management in Manufacturing SME Supply Chains," Group Decision and Negotiation, Springer, vol. 25(6), pages 1237-1259, November.
    4. Jorge E. Hernández & Josefa Mula & Raúl Poler & Andrew C. Lyons, 2014. "Collaborative Planning in Multi-tier Supply Chains Supported by a Negotiation-Based Mechanism and Multi-agent System," Group Decision and Negotiation, Springer, vol. 23(2), pages 235-269, March.

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