AI-based Optimisation of the Apparel Customisation Industry Chain: A Systematic Review

Abstract

The growing demand for personalised apparel, combined with advances in artificial intelligence (AI), is reshaping the apparel customisation industry chain. However, existing studies mostly focus on the isolated optimisation of individual stages (e.g., design, production, or marketing) and lack an integrated perspective on multi-agent collaboration across the entire value chain. To address this gap, this paper presents a systematic review conducted in accordance with PRISMA guidelines. From the Web of Science, Scopus, IEEE Xplore, and CNKI databases, 35 eligible studies were selected and critically synthesised. The review identifies five core interconnected challenges: high demand variability, complex product/bill scheduling with frequent disruptions, fragmented information across stages, inefficient material utilisation, and slow design-to-production lead times. Key research gaps are also identified, including the integration of sustainability metrics, adaptation for small and medium-sized enterprises, cross-domain fashion trend integration, and human-AI collaboration mechanisms. Based on these synthesised findings, a conceptual five-layer AI-agent collaborative architecture is discussed as a potential future research direction to achieve end-to-end coordination across design, sourcing, production, inventory, and marketing. This conceptual framework synthesises current technological trends but requires empirical validation in real-world settings.