Circular supply chain management with large scale group decision making in the big data era: 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.
Section snippets
Research method and contribution statement
In this paper, we propose a framework to help establish a circular supply chain in the scope of the LSGDM using big data. We put a strong emphasis on reviewing the literature on LSGDM. Our approach is to identify the most relevant and high-quality studies related to LSGDM. As such, we intentionally do not try to make our literature review exhaustive. To be specific, we conduct a title word search in Google Scholar. For LSGDM, we search “large scale group decision making” and select all the
Social-network specific (SS)
In textual analysis applications, social network analysis is very common. As a matter of fact, if we focus on the case with a massive amount of data in LSGDM, social network analysis can be conducted to generate insights.
In the literature, only a few papers very specifically highlight “social network analysis (SNA)” in their research. For instance, extending the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach, Wu et al. (2018a) explore LSGDM via a novel
LSGDM: Applications
A few prior studies have highlighted the significance of LSGDM in real-world applications. Note that some of the papers reviewed in Section 2 also have applications. However, as their contribution is mainly on methodologies, we do not classify them as applications-oriented. In the following, we focus on those studies which are devoted to applications of LSGDM.
Liu et al. (2015) study the optimal contractor selection problem using the LSGDM methodology. They also build an effective indicator
Big data and CSCM
In Industry 4.0, many advanced information technologies are present (Arza et al. 2020) and digitalization becomes a norm (Papadopoulos et al., 2020a, 2020b). For instance, blockchain is well-advocated (Choi 2019; Cai et al., 2020; Choi et al., 2020; Dutta et al., 2020; Kouhizadeh et al., 2020), machine learning is a critical tool (Mardani et al., 2020) and Internet of Things (IoTs) applications are also widely seen. However, among all the named technologies, big data analytics and tools (
The micro-level framework
Based on the reviewed literature with respect to LSGDM methodologies and applications as well as big data's applications for CSCM, two frameworks, at two different levels are proposed as follows. Observe that the following two frameworks are based on the generic four-stage process as proposed and discussed in Section 3.
For the micro level, we have a three-stage framework. First of all, suppose that in a single agent of the CSC (e.g., a retailer), called agent k, there are Nk managers who are
Future research agenda
After examining the literature as well as proposing the framework, we discuss various areas for future studies in the following.
Applications in real-world CSCM: The first proposed future research is to implement the proposed frameworks in the real world. We can see that in prior studies, the optimal LSGDM problem on selecting the sustainable supplier has been explored but that only corresponds to the three-stage LSGDM CSCM micro framework. In our proposed Macro-Micro Model, we also have the
Summary and concluding remarks
Today, with the global awareness of environmental sustainability, circular supply chain management is a timely topic. In Industry 4.0, big data analytics and tools are crucial and decision making is well-supported by a massive amount of unstructured data from all sources (including social media platforms). At the same time, LSGDM, which refers to the case when a lot of decision makers join the decision making process, has emerged and being established as a promising field of study. How LSGDM
Author statements
Tsan-Ming Choi: Original draft writing, analysis and insights development, supervision, revision.
Yue Chen: Original draft writing, data collection, revision, diagrams.
Acknowledgements
Tsan-Ming Choi's research is partially supported by The Hong Kong Polytechnic University (Project ID: P0009613).
Tsan-Ming Choi (Jason) is currently a Full Professor at The Hong Kong Polytechnic University, Kowloon, Hong Kong. He has authored 16 books and over 250 papers in Web of Science listed “citation journals”. His-current research interests focus on logistics and supply chain management, especially with the use of technologies. Prof. Choi is currently the Co-Editor-in-Chief of Transportation Research Part E: Logistics and Transportation Review, a Department Editor of IEEE Transactions on Engineering
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Tsan-Ming Choi (Jason) is currently a Full Professor at The Hong Kong Polytechnic University, Kowloon, Hong Kong. He has authored 16 books and over 250 papers in Web of Science listed “citation journals”. His-current research interests focus on logistics and supply chain management, especially with the use of technologies. Prof. Choi is currently the Co-Editor-in-Chief of Transportation Research Part E: Logistics and Transportation Review, a Department Editor of IEEE Transactions on Engineering Management, a Senior Editor of Production and Operations Management, and an Associate Editor of Decision Sciences, IEEE Transactions on Systems, Man, and Cybernetics: Systems, and Information Sciences. He is serving as a member of the engineering panel of Research Grants Council (Hong Kong). Email: [email protected]
Yue Chen (Allen) is currently an Assistant Professor at Macau University of Science and Technology, Taipa, Macau. He has the publications in Web of Science listed “citation journals”. His-research area mainly focuses on operations management, supply chain management, big data application, and sustainability. Email: [email protected]
We sincerely thank the editors and reviewers for their kind, encouraging and constructive comments on our paper.