When printing and dyeing enterprises start talking about digital factories as order creation centers rather than cost centers, and AI fabric inspection technology moves from concept validation to scaled implementation, a clear signal emerges: textile digitalization is leaving the 'showroom' phase and entering the deep waters of industrial application.
At the 2026 Textile Industry Digital Development Conference held in Keqiao on May 7, practitioners from across the supply chain used real cases and data to outline the contours and challenges of this transformation path.
Event Background
The conference, themed 'Smart Connection to Textile Capital, Digital Future – Building New Quality Productivity in the Textile Industry,' was led by the China National Textile and Apparel Council (CNTAC) with deep participation from the Keqiao government. Yan Yan, Vice Chairman of the International Textile Manufacturers Federation and Vice President of CNTAC, outlined three major directions for the '15th Five-Year Plan' period: promoting deep AI deployment and building a textile intelligent large model, deepening full-chain efficient collaboration, and deeply integrating green manufacturing. Keqiao showcased its '1+4+N' comprehensive intelligent agent system, aiming to leap from single-point applications to system integration.
Notably, the conference did not stop at macro-level proposals but released multiple technological breakthroughs with quantitative indicators. Hangzhou Huanyu Digital Smart Technology Co., Ltd. unveiled its TDSD (Textile Digital Smart Dyeing) low-carbon digital dyeing process, achieving nearly 99% water savings, 33% carbon reduction, and 21% chemical reduction through a fully self-developed inkjet equipment and AI color management system. This figure means that the traditionally high-water-consuming printing and dyeing industry is being redefined by technology.
Industry Impact
For textile enterprises, digitalization is no longer a 'whether or not' choice but a 'where to start' operational question. Hu Song, Director of the China Textile Information Center, clearly proposed a five-step method: 'diagnose the current situation, select scenarios, supplement data, run pilots, and expand capabilities,' emphasizing that companies should start with the most painful scenarios, the clearest data, and the most measurable benefits. This judgment directly addresses the chronic problem of 'big and comprehensive digitalization, but difficult to implement.'
- **Printing and Dyeing**: Long Fangsheng, General Manager of Zhejiang Meixinda Textile Printing and Dyeing Technology Co., Ltd., shared the company's three evolutions of 'shaping the form, casting the soul, and enlightening the wisdom,' proposing that printing and dyeing enterprises should break out of the simple fabric production positioning and use digital upgrades to create incremental value. This reflects that digitalization in the printing and dyeing sector is shifting from 'reducing labor and costs' to 'value-added revenue generation.'
- **Fabric Inspection**: Shanghai Kaiquan Intelligent Technology Co., Ltd.'s 'teachable' self-learning AI vision system, and Nantong Julian Digital Technology Co., Ltd.'s multi-scenario solutions covering online defect warnings and warp/weft density monitoring, both point to a trend: AI fabric inspection is moving from the lab to the workshop. However, Luo Yucheng, Deputy General Manager of Shaoxing Keqiao Weaving and Printing-Dyeing Industry Brain Operation Co., Ltd., pointed out that challenges such as algorithm adaptation for complex fabrics, system instability in harsh environments, and high implementation costs for SMEs remain obstacles.
- **Trading**: Wang Rong, CEO of Shaoxing Getaikesi Light Textile Technology Co., Ltd., proposed a low-code platform progressive construction path, providing a lightweight digital transformation paradigm for small and medium trading enterprises. This means digitalization is no longer a privilege of large enterprises.
For buyers and foreign trade companies, these changes are reshaping the quality control logic of the supply chain. Traditional manual fabric inspection suffers from recruitment difficulties, fatigue-induced missed defects, and delayed problem detection. Once AI inspection scales, it will directly impact the pass rate and delivery time of fabric deliveries. Meanwhile, water-saving and carbon-reducing processes in the printing and dyeing sector could become a crucial bargaining chip for export companies facing international green barriers.
Practical Recommendations
For Buyers - Focus on suppliers' digital transformation progress in printing and dyeing, especially those adopting water-saving and carbon-reducing processes, as they are more resilient in meeting European and American green compliance requirements. - Incorporate AI fabric inspection coverage into supplier evaluation metrics, prioritizing factories with deployed intelligent quality control systems to reduce post-delivery return risks. - For small and medium traders, prioritize suppliers that have built low-code digital management platforms, as these companies typically offer faster order response and more transparent processes.
For Foreign Trade Companies - Proactively deploy carbon footprint tracking systems. The full-chain carbon footprint model revealed at the Keqiao conference is taking shape, and early adoption will provide a trust advantage with clients. - Monitor the efficiency gains from AI fabric inspection technology. Consider partnering with technology service providers to introduce AI-assisted inspection in overseas warehouses or during quality checks to reduce overseas return costs. - Leverage the digital trade ecosystem of 'live streaming + platforms + cross-border e-commerce + overseas warehouses.' Keqiao is building a fully digital trade chain, and foreign trade companies should accelerate online channel development to capture traffic dividends.
The wave of textile digitalization is irreversible. But the real differentiator will not be who first shouts the slogan of 'smart factory,' but who can find the smallest entry point in the most painful link and use data to drive continuous business evolution.