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.

今天，实现循环经济已成为全球许多企业和政府的共同目标。在大数据时代，海量数据为决策提供了良好的支持和增强。特别是，出现了大规模团体决策（LSGDM），这是指许多决策者都参与决策过程的情况。已知社交网络分析与LSGDM相关。在本文中，我们研究了有关LSGDM的文献，并重点介绍了该领域当前的方法学进展。我们回顾了专注于LSGDM应用的工作。我们研究了如何在循环供应链中使用大数据。根据审查的研究，我们进一步构建了三阶段LSGDM CSCM微观框架以及五步LSGDM CSCM宏观框架（带有反馈回路），并形成了宏观微观模型。我们讨论了宏观微观模型如何帮助支持循环供应链管理（CSCM）。我们提出了未来的研究方向和领域。本文是第一个系统地揭示如何使用“宏观-微观模型”将LSGDM应用于大数据时代以支持CSCM的研究。