AI-driven mass personalized customization in fashion: An intelligent manufacturing paradigm

Surging consumer demand for personalization is straining conventional mass customization paradigms, which have reached a functional bottleneck. Constrained by predefined modularity, conventional mass customization approaches can neither achieve the real-time, data-driven responsiveness of hyper-personalization nor deliver the creative openness inherent in co-design experiences. To address these limitations at a systemic level, we propose and validate a novel AI-driven Mass Personalized Customization (AMPC) paradigm that integrates generative intelligence and manufacturing adaptability within a unified, closed-loop framework. The AMPC paradigm features a four-layer architecture with a central Data & Technology Hub connecting the Semantic Intent, Design Generation, Physical Realization, and Closed-Loop Service subsystems. To validate this paradigm, we present a systematic case study in the apparel industry, detailing the complete workflow from user intent recognition, through AI-driven creative generation and virtual try-on, to smart manufacturing and feedback analysis. The analysis shows that the AMPC framework overcomes traditional limitations by enabling greater user design freedom and establishing a data-driven feedback loop for ongoing optimization, thereby offering a new model for the digital transformation of the apparel value chain.