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

Highlights

• Study circular supply chain management (CSCM) with LSGDM.

• Explore applications of big data for CSCM.

• Examine the literature.

• Construct the three-stage LSGDM CSCM micro framework.

• Establish the five-step LSGDM CSCM macro framework.

• Build the macro-micro model.

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.

Introduction

Large scale group decision making (LSGDM) is an important topic nowadays. By definition, LSGDM must involve a substantially large amount of decision makers whose objectives are in general different. In some cases, the decision makers may have assessments which are fuzzy in nature and may be expressed in the form of fuzzy preferences or interval-based intuitionistic fuzzy numbers. So, it is critical to establish the consensus model and to achieve the best possible outcome.

In a supply chain system, such as the global supply chain with lots of supply chain agents operating all around the world, there are many individual decision makers who possess different objectives. Decision making in such a large scale complex supply chain system is similar to what we called system of systems in systems engineering. The corresponding decision making process is hence a kind of LSGDM.

Today, with the growing awareness and global concerns on environmental sustainability (Choi and Luo 2019; Guo et al., 2020; Li et al., 2020), circular supply chains are formed. In circular supply chains, not only closed-loop supply chain processes and the triple-bottom-line (TBL) based sustainability¹ (Chen et al. 2019; Centobelli et al., 2020; Govindan et al., 2020; Shi et al., 2020) are encouraged, but proposals on zero wastes and regenerative supply chains are also well-advocated (Jabbour et al., 2019; Durán-Romero et al., 2020; Ricciardi et al., 2020). A circular supply chain (CSC) commonly includes agents such as upstream material suppliers, product manufacturers, distributors, wholesalers, retailers, third-party collectors for scraps and wastes, remanufacturers, recyclers, etc. Fig. 1 depicts a typical circular supply chain system.

As CSCs are a part of circular economy, a simple Google Scholar search using the title words “circular economy” has shown the related figures as summarized in Table 1.

From Table 1, we can see an overall increasing trend in terms of the number of publications. Note that for the Year 2020, the projected total number of publications should be (1490/10.4)*12 = 1719, which is 4.5 times more than the figure in 2011. This uprising trend highlights the fact that circular economy, including circular supply chain management, is getting more and more important.

In circular supply chain management (CSCM), LSGDM is also present. How to achieve the optimal circular supply chain considering the preferences of individual decision makers is a very critical issue. In particular, how big data (Papadopoulos et al., 2020b) collected from different individual supply chain agents (and hence decision makers) can be used to build a circular supply chain requires deep explorations. This paper aims to establish a novel framework, combining the methodologies in the LSGDM literature, for such a purpose.

As a remark, in the literature, LSGDM has been widely explored. In particular, over the past decade, regarding the use of clustering methods as well as fuzzy sets and systems theories, lots of proposals and advances in LSGDM have been reported. Recently, Tang and Liao (2019) conduct an excellent and comprehensive review for LSGDM and relate it with big data analytics. The authors propose a framework with which the review is systematically reported. Many technical details are also highlighted. This paper also follows Tang and Liao (2019)’s approach but the focal point is on circular supply chain management with big data-driven LSGDM. To be specific, our proposed framework include both the micro-level and macro-level. For the micro-level, we establish three stages for LSGDM. For the macro-level, we include five steps. Combining them forms the complete Macro-Micro Model for LSGDM.

This paper's structure is arranged as follows. In Section 1, we present the background and motivation of this study. In Section 2, we present the research method and contribution statement. In Section 3, we review various LSGDM methodologies oriented research papers and highlight whether they are social-network specific or not. In Section 4, we examine various papers which focus on applications of LSGDM. In Section 5, we discuss prior studies related to the use of big data for circular supply chain operations. In Section 6, we establish novel frameworks and then build the Macro-Micro Model. In Section 7, we propose a future research agenda with several important directions for further studies. Finally, in Section 8, we conclude this paper.