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Circular supply chain management with large scale group decision making in the big data era: The macro-micro model

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  • Choi, Tsan-Ming
  • Chen, Yue

Abstract

Today, achieving the circular economy is a common goal for many enterprises and governments all around the world. In the big data era, decision making is well-supported and enhanced by a massive amount of data. In particular, large scale group decision making (LSGDM), which refers to the case in which a lot of decision makers join the decision making process, has emerged. Social network analyses are known to be relevant to LSGDM. In this paper, we examine the literature on LSGDM and highlight the current methodological advances in the area. We review the works focusing on applications of LSGDM. We study how big data can be used in circular supply chains. Based on the reviewed studies, we further construct the three-stage LSGDM CSCM micro framework as well as the five-step LSGDM CSCM macro framework (with a feedback loop) and form the Macro-Micro Model. We discuss how the Macro-Micro Model can help to support circular supply chain management (CSCM). We propose future research directions and areas. This paper contributes by being the first study uncovering systematically how LSGDM can be applied to support CSCM in the big data era using the Macro-Micro Model.

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  • Choi, Tsan-Ming & Chen, Yue, 2021. "Circular supply chain management with large scale group decision making in the big data era: The macro-micro model," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    • Handle: RePEc:eee:tefoso:v:169:y:2021:i:c:s0040162521002237
    DOI: 10.1016/j.techfore.2021.120791
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    5. Chauhan, Chetna & Parida, Vinit & Dhir, Amandeep, 2022. "Linking circular economy and digitalisation technologies: A systematic literature review of past achievements and future promises," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
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    9. Pedota, Mattia, 2023. "Big data and dynamic capabilities in the digital revolution: The hidden role of source variety," Research Policy, Elsevier, vol. 52(7).
    10. Shayan Bahadoran Baghbadorani & Seyed Abdolhassan Johari & Zahra Fakhri & Esmaeil Khaksar Shahmirzadi & Shavkatov Navruzbek Shavkatovich & Sangkeum Lee, 2022. "A New Version of African Vulture Optimizer for Apparel Supply Chain Management Based on Reorder Decision-Making," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    11. Yu, Vincent F. & Aloina, Grace & Eccarius, Timo, 2023. "Adoption intentions of home-refill delivery service for fast-moving consumer goods," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).
    12. Zhou, Yongyi & Zhang, Yulin & Wahab, M.I.M. & Goh, Mark, 2023. "Channel leadership and performance for a closed-loop supply chain considering competition," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    13. Leonardo Fernandes & António Miguel Rosado da Cruz & Estrela Ferreira Cruz & Sérgio Ivan Lopes, 2023. "A Review on Adopting Blockchain and IoT Technologies for Fostering the Circular Economy in the Electrical and Electronic Equipment Value Chain," Sustainability, MDPI, vol. 15(5), pages 1-23, March.
    14. Manju Saroha & Dixit Garg & Sunil Luthra, 2022. "Analyzing the circular supply chain management performance measurement framework: the modified balanced scorecard technique," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(2), pages 951-960, June.
    15. Abderahman Rejeb & Karim Rejeb & John G. Keogh & Suhaiza Zailani, 2022. "Barriers to Blockchain Adoption in the Circular Economy: A Fuzzy Delphi and Best-Worst Approach," Sustainability, MDPI, vol. 14(6), pages 1-23, March.

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