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DfRem-Driven Closed-Loop Supply Chain Decision-Making: A Systematic Framework for Modeling Research

Author

Listed:
  • Shuiye Niu

    (Department of Industrial Engineering, Tsinghua University, Beijing 100084, China)

  • Honglong Zhuo

    (School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China)

  • Kelei Xue

    (Business School, Nankai University, Tianjin 300071, China)

Abstract

With the prevalence of a circular economy, extended producer responsibility, and the maturity of intelligent manufacturing technology, Design for Remanufacture (DfRem) has become a new driving force for the profitability of the modern supply chain. DfRem activities occur at the stage of new product design but have a significant impact on subsequent remanufacturing operations. Based on the closed-loop supply chain operation systems with DfRem, we systematically explored the impact of DfRem on supply chain operation decisions through a case study and modeling analysis and built up a modeling research framework of DfRem-driven closed-loop supply chain (CLSC) operation decision-making. Our research identified DfRem-driven model design elements and discussed the modeling of DfRem-driven investment decision problems, involving different approaches to obtain DfRem investment (i.e., DfRem-driven horizontal or vertical supply chain cooperation) and the ownership of DfRem-level decision-making rights. Moreover, the DfRem-driven two-stage and multi-period modeling processes are elaborated in detail. The aim of this paper is to provide other scholars with a more comprehensive understanding of DfRem research issues, and to establish an integrated DfRem-driven research framework for subsequent scholars to better conduct modeling research on DfRem.

Suggested Citation

  • Shuiye Niu & Honglong Zhuo & Kelei Xue, 2019. "DfRem-Driven Closed-Loop Supply Chain Decision-Making: A Systematic Framework for Modeling Research," Sustainability, MDPI, vol. 11(12), pages 1-19, June.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:12:p:3299-:d:240065
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    References listed on IDEAS

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    Cited by:

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    2. Ionica Oncioiu & Ovidiu Constantin Bunget & Mirela Cătălina Türkeș & Sorinel Căpușneanu & Dan Ioan Topor & Attila Szora Tamaș & Ileana-Sorina Rakoș & Mihaela Ștefan Hint, 2019. "The Impact of Big Data Analytics on Company Performance in Supply Chain Management," Sustainability, MDPI, vol. 11(18), pages 1-22, September.
    3. Yohannes A. Alamerew & Marianna Lena Kambanou & Tomohiko Sakao & Daniel Brissaud, 2020. "A Multi-Criteria Evaluation Method of Product-Level Circularity Strategies," Sustainability, MDPI, vol. 12(12), pages 1-20, June.
    4. Wang, Jian & He, Shulin, 2023. "Government interventions in closed-loop supply chains with modularity design," International Journal of Production Economics, Elsevier, vol. 264(C).
    5. Mladen Pancić & Dražen Ćućić & Hrvoje Serdarušić, 2023. "Business Intelligence (BI) in Firm Performance: Role of Big Data Analytics and Blockchain Technology," Economies, MDPI, vol. 11(3), pages 1-19, March.
    6. Mohammadtaghi Falsafi & Rosanna Fornasiero, 2022. "Explorative Multiple-Case Research on the Scrap-Based Steel Slag Value Chain: Opportunities for Circular Economy," Sustainability, MDPI, vol. 14(4), pages 1-18, February.
    7. Rohit Agrawal & Vishal A. Wankhede & Anil Kumar & Sunil Luthra & Abhijit Majumdar & Yigit Kazancoglu, 2022. "An Exploratory State-of-the-Art Review of Artificial Intelligence Applications in Circular Economy using Structural Topic Modeling," Operations Management Research, Springer, vol. 15(3), pages 609-626, December.

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