The narrative of digital transformation in the textile industry is undergoing a fundamental shift. As industry public data consistently shows AI moving from 'proof of concept' to 'scale deployment,' the 2026 Textile Industry Digital Development Conference in Keqiao served as a calibration of the upgrade path. The core issue is no longer 'whether to transform' but 'how to do it and where to start.'
Three Tracks: Digital Breakthroughs in Dyeing, Inspection, and Full Process
The conference focused on three directions: digital transformation of printing and dyeing enterprises, AI intelligent fabric inspection, and full-process digitalization. This is no coincidence, as these are the areas with the most concentrated pain points, clearest data, and most measurable benefits in the current industrial chain. For instance, traditional printing and dyeing processes have long suffered from high water and energy consumption, hindering green transformation, while AI fabric inspection directly addresses the low efficiency and high error rates of manual quality control.
A notable data point came from Hangzhou Huanyu Digital Smart Technology: its TDSD low-carbon digital dyeing process saves nearly 99% of water, reduces carbon emissions by 33%, and cuts chemical use by 21%. If this technology is widely adopted, the environmental cost of the dyeing stage could drop dramatically, which is critical for exporters facing carbon tariffs in markets like the EU.
A Low-Cost, Quick-Results Path for SMEs
For the vast majority of SMEs in the textile industry, the biggest obstacle to digital transformation is the unclear return on investment. Hu Song, director of the China Textile Information Center, explicitly proposed a five-step method: 'diagnose the current situation, select scenarios, supplement data, run pilots, and expand capabilities.' He emphasized that SMEs should adopt low-cost, quick-results digital paths, starting from the most painful scenarios, the clearest data, and the most measurable benefits.
This approach was validated by Shaoxing Getakesi Light Textile Technology, which built a digital management platform for trading companies using a low-code platform, achieving incremental construction from point to surface. For small and medium-sized trading companies with annual revenues below 50 million yuan, this provides a path to full-process digitalization without heavy IT investment.
AI Fabric Inspection: From Pilot to Unified Industry Standards
Traditional manual fabric inspection faces challenges such as recruitment difficulties, fatigue-related missed detections, and delayed identification of quality issues. Shanghai Kaiqian Intelligent Technology and Nantong Julian Digital Technology showcased their respective solutions: a 'teachable self-learning AI system' and a multi-scenario AI inspection system covering online defect warnings. However, industry consensus is that AI inspection is still in the 'pilot and adaptation' stage, with major bottlenecks including algorithm adaptation for complex fabrics, system stability in complex environments, and high implementation costs for SMEs.
A key signal from the conference was the proposal to establish a unified industry standard for defect databases. If such a database is built, AI inspection could move from a fragmented, company-specific approach to a replicable infrastructure, significantly reducing the marginal cost of technology promotion.
Digital Infrastructure for Chain Collaboration and Green Compliance
The three directions proposed by Yan Yan, vice chairman of the International Textile Manufacturers Federation—deep AI deployment, efficient whole-chain collaboration, and deep integration of green manufacturing—point to a single goal: building a carbon footprint tracking model covering the entire industrial chain. With the EU's Carbon Border Adjustment Mechanism (CBAM) gradually taking effect, green compliance is no longer an option but a threshold for export enterprises. The water and carbon reduction data from the TDSD process provides the technical support for such compliance.
Keqiao has already built a '1+4+N' comprehensive intelligent system, promoting the digitalization and platformization of market transactions, information release, trend analysis, and supply chain services, and has opened up a full digital trade chain through 'live streaming + platforms + cross-border e-commerce + overseas warehouses.' This indicates that the digitalization of industrial clusters is not a single-point breakthrough but a system integration.