Building a data‐driven circular supply chain hierarchical structure: Resource recovery implementation drives circular business strategy

• Published in: Business strategy and the environment Vol. 31; no. 5; pp. 2082 - 2106
• Main Authors: Tseng, Ming‐Lang, Ha, Hien Minh, Tran, Thi Phuong Thuy, Bui, Tat‐Dat, Chen, Chih‐Cheng, Lin, Chun‐Wei
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Periodicals Inc 01.07.2022
• Subjects: Artificial intelligence; Attributes; Big Data; Business; circular business strategy; circular supply chain; Cluster analysis; Criteria; data‐driven analysis; Decision analysis; Decision making; Delphi method; Digitization; Entropy; Entropy (statistics); Industry 4.0 and digitalization; Intelligence; Life cycle analysis; Life cycle assessment; Life cycles; Machine learning; Plastics recycling; Qualitative analysis; Recovery; Recycling; Resource recovery; reverse supply chain; Structural hierarchy; Supply; Supply chains; Waste to energy; Weighting methods
• ISSN: 0964-4733; 1099-0836
• DOI: 10.1002/bse.3009

The circular supply chain has recently received more attention as a relevant solution to effectively tackle environmental issues while simultaneously achieving resource recovery and circular business strategy benefits. This study builds a hierarchical circular supply chain structure from big data including qualitative and quantitative information. This study uses data‐driven analysis to clarify circular supply chain trends and opportunities in practice. A valid hierarchical circular supply chain structure is composed of a big dataset. However, the attributes of the hierarchical circular supply chain structure must be explored to identify the opportunities and challenges of the circular supply chain. A combination of data‐driven content and cluster analysis, including the fuzzy Delphi method, fuzzy decision‐making trials, evaluation laboratories, and the entropy weight method, is utilized to address this gap. The study analyzes a set of five attributes from the literature, and 23 criteria are validated. The results show that resource recovery implementation, Industry 4.0 and digitalization, and 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.