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

【Author】 Choi, Tsan-Ming; Chen, Yue

【Source】TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE

【影响因子】10.884

【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.

【Keywords】Large scale group decision making (LSGDM); Circular supply chains; Research agenda; Literature review; Frameworks; Macro-micro model

【发表时间】2021 AUG

【收录时间】2022-01-02

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