Artificial Intelligence as an Enabler for Sustainable Circular Supply Chain Management of <scp>SMEs</scp> : A Solution‐Based Framework for Transformation Toward Net Zero Economy

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:The mixed-method approach combining BWM and F-DEMATEL offers a replicable methodology for studying AI enablers in SCSCM.

🏢実務担当者:SMEs can use the identified enablers to prioritize AI investments for circular supply chain transformation.

🏛政策担当者:Policymakers can reference this framework to design support programs that build AI readiness among SMEs for net-zero goals.

ABSTRACT Sustainable circular supply chain management (SCSCM) has become an essential channel to net zero emissions in recent years, and small and medium‐sized enterprises (SMEs) have a substantial but understudied role in the shift. However, SMEs face substantial challenges in implementing SCSCM practices due to limited resources and technological capabilities. This paper explores the enablers of artificial intelligence (AI) readiness, organizational, technological, and strategic readiness conditions that ready SMEs to utilize AI to transform SCSCM. To address this the study explicitly links these enablers to net zero mechanisms including emissions reduction through operational optimization, circular resource flows that minimize embodied carbon, and enhanced monitoring and verification of decarbonization progress. The mixed method approaches, that is, literature reviews, theoretical frameworks (“resource‐based view theory” [RBV], “dynamic capability theory” [DCT], and “innovation diffusion theory” [IDT]), and expert feedback, are used to identify and categorize the critical enablers. Here, “Best‐Worst‐Method” (BWM) was used to prioritize, and “Fuzzy Decision‐Making‐Trial and Evaluation‐Laboratory” (F‐DEMATEL) was utilized to make a “causal relationship” among the identified enablers. The outcomes of this study suggest that among the main category “skill and strategy‐related enablers” and among the sub‐category “technology and algorithms for processing data,” are the key enablers. And the analysis show that “data and infrastructure,” “skill and strategy,” and “organizational acceptance and management strategy” are in the cause group, whereas “organizational acceptance and management strategy” and “financial strategy” are in the effect group enablers. The findings of this research offer actionable guidance for SMEs to strategically implement AI‐driven sustainable circular supply chain practices, enhancing their operational efficiency and contributing tangibly to the transition toward a net‐zero economy.

• crossref https://doi.org/10.1002/bsd2.70351first seen 2026-05-14 22:34:54