循環供應鏈作為一種有效解決環境問題。同時，實現資源回收和循環商業策略效益的相關解決方案，最近受到越來越多的關注。本研究從大數據構建了一個分層的循環供應鏈結構，包括定性和定量信息。本研究在實踐中使用數據驅動分析來闡明循環供應鏈趨勢和挑戰。有效的分層循環供應鏈結構由一個大數據集組成。然而，分層循環供應鏈結構的因素需要探索，以表達機會和挑戰。循環供應鏈。數據驅動的內容和群集分析相結合，包括模糊德爾菲法、模糊決策試驗、評估實驗室和熵權法，已被用來解決這一問題。本研究從文獻中收集了一組因素。有五個層面和 23 個因子得到驗證。結果表明，資源回收實施、工業4.0和數字化、逆向供應鏈實踐屬於因果因子，而循環業務策略和生命週期可持續性評估屬於影響因子。最終標準包括材料效率、廢物轉化為能源、機器學習、電子廢物、塑料回收和人工智能。 This study builds a hierarchical circular supply chain structure from data-driven information such as qualitative and quantitative information. This study uses data-driven analysis to clarify the circular supply chain trends and opportunities in the practices. The circular supply chain topic has received more attention recently as a relevant solution to effectively tackle environmental issues while achieving economic and social benefits simultaneously. The valid hierarchical circular supply chain structure is composed of a data set. However, the hierarchical circular supply chain structure needs to be explored on their attributes to express the opportunities and challenges towards the circular supply chain. To handle such a gap, a data-driven combination of content and cluster analysis, fuzzy Delphi Method, fuzzy decision-making trial and evaluation laboratory, entropy weight method has been utilized. A set of attributes is criticized from the literature. There are five aspects and 23 criteria validated. The results present that resource recovery implementation, Industry 4.0 and digitalization, reverse supply chain practice pertain to the causal group, while circular business strategy and life cycle sustainability assessment are included in the effect group. The conclusive criteria comprise material efficiency, waste-to-energy, machine learning, e-waste, plastic recycling, and artificial intelligence.
