Sustainable supply chain management trends in world regions: A data-driven analysis

Feng Ming Tsai, Tat-Dat Bui, Ming-Lang Tseng, Mohd Helmi Ali, Ming Lim, Anthony SF Chiu

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

This study proposes a data-driven analysis that describes the overall situation and reveals the factors hindering improvement in the sustainable supply chain management field. The literature has presented a summary of the evolution of sustainable supply chain management across attributes. Prior studies have evaluated different parts of the supply chain as independent entities. An integrated systematic assessment is absent in the extant literature and makes it necessary to identify potential opportunities for research direction. A hybrid of data-driven analysis, the fuzzy Delphi method, the entropy weight method and fuzzy decision-making trial and evaluation laboratory is adopted to address uncertainty and complexity. This study contributes to locating the boundary of fundamental knowledge to advance future research and support practical execution. Valuable direction is provided by reviewing the existing literature to identify the critical indicators that need further examination. The results show that big data, closed-loop supply chains, industry 4.0, policy, remanufacturing, and supply chain network design are the most important indicators of future trends and disputes. The challenges and gaps among different geographical regions is offered that provides both a local viewpoint and a state-of-the-art advanced sustainable supply chain management assessment.
Original languageEnglish
Article number105421
JournalResources, Conservation and Recycling
Volume167
Early online date23 Jan 2021
DOIs
Publication statusE-pub ahead of print - 23 Jan 2021

Keywords

  • Data-driven analysis
  • Entropy weight method
  • Fuzzy Delphi method
  • Fuzzy decision-making trial and evaluation laboratory
  • Sustainable supply chain management

ASJC Scopus subject areas

  • Waste Management and Disposal
  • Economics and Econometrics

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