Integrating AI into circular supply chain decision-making: a systematic review and conceptual meta-framework
TL;DR
The study concludes that AI should be positioned as a governed decision-support layer rather than an autonomous technical add-on, and that effective circular supply chain decision-making depends on data quality, interoperability, organisational capability, ecosystem coordination, and sustainability guardrails.
Abstract
This paper develops a meta-framework for AI-enabled circular supply chain decision-making for sustainability, addressing the lack of an integrated, decision-oriented view across circular supply chain, governance, and AI literature. The study asks which managerial decisions are required across circular supply chain processes and how artificial intelligence can support them. A qualitative systematic literature review was conducted using Scopus, followed by AI-assisted conceptual synthesis. From an initial corpus of 6,718 records, a focused subset of 1,032 studies was derived using circularity, supply chain, and AI-related inclusion criteria. Deductive and inductive coding identified recurring constructs relating to process coverage, decision levels, AI roles, governance, capability prerequisites, and sustainability constraints. The results show that AI-supported circular supply chain decision-making is structured across five interdependent layers: strategic governance, tactical policy design, AI decision support, operational control, and foundational enablers. The framework also identifies a cross-layer governance spine centred on decision rights, auditability, and constraints on triple-bottom-line performance. The study concludes that AI should be positioned as a governed decision-support layer rather than an autonomous technical add-on, and that effective circular supply chain decision-making depends on data quality, interoperability, organisational capability, ecosystem coordination, and sustainability guardrails.