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

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 in...

Full description

Saved in:
Bibliographic Details
Published in:Business strategy and the environment 2022-07, Vol.31 (5), p.2082-2106
Main Authors: Tseng, Ming‐Lang, Ha, Hien Minh, Tran, Thi Phuong Thuy, Bui, Tat‐Dat, Chen, Chih‐Cheng, Lin, Chun‐Wei
Format: Article
Language:English
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
Qualitative analysis
Recovery
Recycling
Resource recovery
reverse supply chain
Structural hierarchy
Supply
Supply chains
Sustainability
Waste to energy
Weighting methods
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0964-4733
1099-0836
DOI:10.1002/bse.3009