The global textile industry is undergoing a deep shift from labor-intensive to data-driven operations. On May 7, the 2026 Textile Digitalization Development Conference in Keqiao sent a clear signal: AI applications in textiles have moved beyond the lab and entered a phase of 'small-scale, fast-iteration' mass deployment. Data from the conference highlighted a stark contrast: traditional manual fabric inspection faces chronic issues like labor shortages, high miss rates, and delayed feedback, while AI inspection technology has achieved dual leaps in detection efficiency and defect recognition accuracy, yet still struggles with complex fabric adaptation and cost affordability for SMEs.
Background
Keqiao, the world's largest textile distribution center, serves as a benchmark for digital transformation. The conference was guided by the China National Textile and Apparel Council (CNTAC) and attended by industry leaders like Yan Yan, Sun Weigang, and Hu Song. Yan Yan outlined three major directions for textile digitalization during the '15th Five-Year Plan': building a textile-specific AI large model, breaking down data silos across the supply chain, and deeply integrating green manufacturing. Sun Weigang introduced Keqiao's '1+4+N' intelligent system and its digital trade ecosystem combining livestreaming, platforms, cross-border e-commerce, and overseas warehouses.
From an industrial cluster perspective, Keqiao's 'small-step, fast-run' strategy is being replicated by other regions. Hu Song's report explicitly stated that SMEs should adopt a five-step approach: diagnose current status, select scenarios, supplement data, run pilots, and expand capabilities. They should start with the most painful, data-rich, and measurable pain points, avoiding large-scale, all-encompassing system projects. This judgment directly addresses the core anxiety of SMEs: 'afraid to start, unable to transform.'
Industry Impact
Multiple case studies presented at the conference revealed the 'cost-effectiveness' logic of digital transformation. Long Fangsheng, GM of Zhejiang Meixinda Printing and Dyeing, shared its three-stage evolution, emphasizing that smart factories should not be seen as cost centers but as order creation engines. This view is critical for dyeing mills facing environmental constraints and order fragmentation—digital capability is shifting from a competitive advantage to a survival threshold.
More striking data came from Hangzhou Huanyu Digital Smart's TDSD® low-carbon dyeing process: nearly 99% water savings, 33% carbon reduction, and 21% fewer chemicals. This technological pathway means dyeing mills can achieve flexible production without increasing environmental compliance costs. Its 'spray-to-dye' model, paired with in-house inkjet equipment and AI color management, is rewriting the energy and efficiency formula of traditional dyeing.
AI fabric inspection was a conference highlight. Shanghai Kaiquan Smart's 'teachable' self-learning AI system and Nantong Julian Digital's multi-scenario solutions covering online defect alerts and warp/weft density monitoring point to a trend: AI inspection is evolving from a single-point labor replacement to a full-chain quality control ecosystem. However, Luo Yucheng from Keqiao's Weaving and Dyeing Industry Brain also acknowledged persistent challenges: algorithm adaptation for complex fabrics, system instability in harsh environments, and high deployment costs for SMEs.