Sustainable Apparel Supply Chains: A Review of AI-Driven Optimization, Waste Reduction, and Circular Economy Practices
Abstract
The apparel industry faces persistent sustainability challenges arising from overproduction, inefficient resource utilization, textile waste, and increasingly complex global supply chains. Artificial intelligence (AI) has emerged as a promising technology for improving operational decision-making; however, evidence of its contribution to sustainability remains fragmented across research on forecasting, production, waste management, and the circular economy. This narrative review synthesizes current knowledge on AI-enabled optimization, waste reduction, and circular economy practices within apparel supply chains. Drawing on recent literature, the review examines AI applications in demand forecasting, production planning, supplier selection, logistics, risk management, and explainable decision support, and evaluates their role in reducing textile waste and supporting circular strategies such as product life extension, traceability, reverse logistics, and material recovery. The review finds that AI contributes to sustainability primarily by improving the quality of operational decisions rather than through automation alone. Although AI can enhance resource efficiency, reduce waste, and strengthen circular supply chains, these benefits depend on effective governance, high-quality data, organizational capability, and integration with circular economy practices. Based on this synthesis, an integrated conceptual framework is proposed that positions AI as a decision-support capability linking operational decisions with sustainability outcomes through continuous feedback and governance mechanisms. The review also identifies key research gaps, including the need for industrial validation, apparel-specific datasets, and responsible AI implementation. The findings provide researchers and practitioners with an integrated perspective on how AI can support the transition towards more sustainable and circular apparel supply chains.