The digital transformation of the textile industry is moving from pilot projects to systemic restructuring. The 2026 Textile Industry Digital Development Conference, held on May 7 in Keqiao, Shaoxing, sent a clear signal: the era of isolated point upgrades is ending, replaced by a holistic leap from data infrastructure to intelligent decision-making, spanning the entire chain from workshop to trade.
Dual Drivers: Policy and Platform
Vice President of the China National Textile and Apparel Council (CNTAC), Yan Yan, outlined three key directions for the '15th Five-Year Plan' period: deep implementation of AI, enhanced full-chain collaboration, and deep integration of green manufacturing. The parallel development of a 'Textile Smart Large Model' and vertical-scenario small models is particularly critical, signifying the industry is proactively building a common knowledge base rather than leaving companies to fend for themselves.
As the 'International Textile City', Keqiao's digital layout is taking shape. Sun Weigang, Deputy Secretary of the Party Working Committee of China Textile City, revealed the construction of a '1+4+N' comprehensive intelligent agent system. This system aims to transform market transactions, trend analysis, and supply chain services into a platform-based model, while building a digital trade chain integrating 'livestreaming + platform + cross-border e-commerce + overseas warehouses'. For buyers, this means that in the future, sourcing from Keqiao will not only involve fabric but also traceable, collaborative digital services.
From 'Small Cuts' to 'Big Ecosystem'
Hu Song, Director of the China Textile Information Center, proposed a 'Digital Five-Step Method'—diagnose current status, select scenarios, supplement data, conduct pilots, and expand capabilities—directly addressing the pain points of SMEs that 'dare not transform, do not know how to transform'. He emphasized that companies should start with the most painful scenarios and the clearest data, using 'small-cut applications' to drive 'big ecosystem transformation'. This judgment offers practical value for factories: rather than pursuing a fully intelligent factory in one go, it is better to achieve precise breakthroughs in areas like fabric inspection and production scheduling.
Long Fangsheng, General Manager of Meixinda Printing and Dyeing, offered another perspective. He views digitalization as a 'three-life evolution' for dyeing and printing companies—shaping the form, casting the soul, and awakening wisdom—asserting that 'a smart factory is not a cost center but an order creation center'. This means that for companies that have completed basic automation, digital upgrades are shifting from a 'cost-reduction' tool to a 'revenue-growth' engine.
The Intersection of Green and Smart: Disruptive Dyeing Technology
Hangzhou Huanyu Digital Smart Technology's TDSD® low-carbon digital dyeing process was a technical highlight of the conference. Combining inkjet equipment, new ink materials, and an AI color management system, this technology achieves extreme environmental performance: nearly 99% water saving, 33% carbon reduction, and 21% chemical reduction. For dyeing plants under pressure from EU carbon tariffs and domestic environmental policies, this 'print-and-dye' model not only reduces emissions but also significantly enhances flexible supply capabilities—small-batch, quick-turn orders will no longer be a challenge.
AI Fabric Inspection: The Final Step from Lab to Production Line
AI intelligent fabric inspection was the most discussed topic at the conference. Companies like Shanghai Kaiquan Intelligent Technology and Nantong Julian Digital Technology showcased multi-scenario solutions from online defect warning to warp/weft density monitoring. However, Luo Yucheng, Deputy General Manager of Keqiao Weaving and Dyeing Industrial Brain, also admitted that the industry still faces bottlenecks such as algorithm adaptation difficulties for complex fabrics and high implementation costs for SMEs.
Chen Baojian, Deputy Director of the Textile Product Development Center, was blunt in his summary: the three major pain points of traditional manual inspection—difficulty in recruitment, easy fatigue, and delayed detection—are unsolvable. AI inspection is an inevitable trend but is still in the pilot adaptation phase. He suggested that the industry build a unified defect standard database—a critical infrastructure for moving AI from 'usable' to 'good to use'.