When a dyeing workshop saves nearly 99% water and an AI fabric inspection system learns to improve itself, textile digitalization is no longer a PowerPoint slide but a machine running on the factory floor. The 2026 Textile Industry Digitalization Conference, held on May 7 in Keqiao, proved this with real data from companies across the supply chain.
Three Parallel Tracks: Full-Chain Transformation from Dyeing to Trade
The conference focused on three core directions: digital transformation of printing and dyeing enterprises, AI fabric inspection technology, and full-process digital upgrading. This is not a simple tech showcase but a precise response to industry pain points—high water consumption in traditional dyeing, low efficiency of manual inspection, and high digitalization barriers for SMEs. These issues are especially acute in clusters like Keqiao and Shengze.
Yan Yan, Vice Chairman of the International Textile Manufacturers Federation and Vice President of the China National Textile and Apparel Council, outlined a clear strategic direction: promote deep AI deployment, build a 'textile smart large model' alongside vertical small models; deepen full-chain collaboration by breaking data silos; and deeply integrate green manufacturing by building a carbon footprint tracking model. These three routes point to one goal: moving digitalization from single-point applications to system integration.
Data Speaks: 99% Water Saving and Self-Learning AI
The most exciting cases came from the technology implementation side. Hangzhou Huanyu Digital Smart Technology Co., Ltd. demonstrated its TDSD (Textile Digital Smart Dyeing) low-carbon digital dyeing process, which uses self-developed inkjet equipment, new ink materials, and an AI color management system to achieve nearly 99% water savings, 33% carbon reduction, and 21% chemical reduction. This means dyeing enterprises can turn environmental compliance into a competitive advantage.
On the quality control front, Shanghai Kaiquan Intelligent Technology Co., Ltd. showcased a 'teachable' self-learning AI inspection system. Traditional manual inspection suffers from difficulty in recruiting and retaining workers, fatigue-induced missed defects, and delayed problem detection. The AI system, through software-hardware co-design and deep integration with production management systems, achieves dual leaps in inspection efficiency and defect recognition accuracy. Nantong Julian Digital Technology Co., Ltd. developed multi-scenario AI inspection solutions covering online defect warning, warp/weft density monitoring, and production anomaly identification.
Notably, Luo Yucheng, Deputy General Manager of Shaoxing Keqiao Weaving & Dyeing Industry Brain Operation Co., Ltd., identified three bottlenecks in the current AI inspection industry: algorithm adaptation difficulties for complex fabrics, system instability in complex environments, and high implementation costs for SMEs. This shows that while the technology works, large-scale adoption still requires unified industry standards. Chen Baojian, Deputy Director of the Textile Product Development Center, suggested building a unified defect standard database, which is a critical step for scaling AI inspection.
Divergent Paths: Large Enterprises Build Ecosystems, SMEs Tackle Pain Points
Digitalization is not one-size-fits-all. Hu Song, Director of the China Textile Information Center, in his keynote report 'Digital Navigation: High-Quality Development Transformation Roadmap for the Textile Industry during the 15th Five-Year Plan', clearly outlined differentiated paths: SMEs should adopt low-cost, quick-result digital paths, while large enterprises should shift from system building to data ecosystem leadership. His 'digital five-step method'—diagnose current status, select scenarios, supplement data, run pilots, and expand capabilities—is essentially a gradual transformation strategy.
The sharing by Long Fangsheng, General Manager of Zhejiang Meixinda Textile Printing and Dyeing Technology Co., Ltd., confirmed this logic. Meixinda underwent three life evolutions—'shaping the body, casting the soul, and awakening wisdom'—to transform its smart factory from a cost center into an order creation center. Meanwhile, Shaoxing Getakesi Light Textile Technology Co., Ltd., targeting small and medium-sized trading companies, proposed a lightweight path using low-code platforms to achieve a full-process digital closed loop. The common thread: both started from the most painful scenarios, with the clearest data and the most measurable benefits.