On May 7, a conference focusing on textile industry digitalization concluded in Keqiao, Zhejiang. From intelligent dyeing workshop transformations to live demonstrations of AI fabric inspection and full-process management platforms for trading companies, three seemingly independent yet interconnected topics converged on one judgment: the textile industry's digitalization is moving decisively from the hesitation phase of 'whether to do it' into the execution phase of 'how to make it happen.'
Accelerating Technology Deployment: AI and Low-Carbon Dyeing as Key Drivers
The clearest signal from the conference was that AI applications in textiles are rapidly transitioning from concept validation to the threshold of large-scale deployment. Yan Yan, Vice President of the China National Textile and Apparel Council, described this as 'rapidly moving from concept validation to scale,' proposing a dual-track approach of building a 'textile intelligent large model' alongside smaller, vertical-specific models. This signals that industry leaders are already strategizing how to integrate disparate AI applications into a cohesive intelligent capability.
Another technological highlight came from the dyeing segment. The TDSD low-carbon digital dyeing process, unveiled by Hangzhou Huanyu Digital Smart Technology, delivered a striking set of metrics: nearly 99% water savings, 33% carbon reduction, and a 21% decrease in chemical usage. This 'three-in-one' model—combining inkjet equipment, new ink materials, and an AI color management system—fundamentally re-engineers traditional dyeing. For dyeing mills facing escalating environmental compliance pressures, such performance metrics transform green准入 from a cost burden into a potential competitive advantage for securing orders.
Industry Cluster Response: The Keqiao Model and SME Pathways
As the international textile capital, Keqiao showcased a distinct digital transformation roadmap during this conference. The '1+4+N' comprehensive intelligent system and the 'live streaming + platform + cross-border e-commerce + overseas warehouse' digital trade framework, outlined by Sun Weigang, Deputy Secretary of the Keqiao Textile City Party Working Committee, indicate that this industrial cluster's digitalization has leapfrogged from point-based applications to systemic integration. For other textile clusters, Keqiao's practice offers a replicable reference.
Notably, the conference's discussion on digital pathways for small and medium-sized enterprises (SMEs) was particularly pragmatic. Hu Song, Director of the China Textile Information Center, proposed a five-step method—'diagnose status, select scenarios, supplement data, run pilots, expand capabilities'—alongside practical advice to 'start with the most painful scenarios, the clearest data, and the most measurable benefits.' This directly addresses the real constraints of SMEs: limited capital and thin technical reserves. The philosophy of 'using small-cut applications to drive large-scale ecosystem transformation' implies that digitalization is no longer a privilege reserved for large corporations.
AI Fabric Inspection: From Pain Point to Standardization Gap
AI fabric inspection sparked the most intense discussions at the conference. Traditional manual inspection suffers from chronic issues: difficulty in recruiting and retaining workers, inspector fatigue leading to missed defects, and lagging quality problem detection. With rising labor costs, AI substitution seems an irreversible trend. Solutions presented by Shanghai Kaiqiang Intelligent Technology and Nantong Julian Digital Technology demonstrated breakthroughs in defect warning, warp/weft density monitoring, and production anomaly identification.
However, deployment is not without hurdles. Luo Yucheng, Deputy General Manager of Shaoxing Keqiao Weaving and Dyeing Industry Brain Operations Co., pointed out three major bottlenecks: algorithm adaptation difficulties for complex fabrics, system instability in harsh production environments, and high deployment costs for SMEs. This explains why, despite technological maturity, AI inspection penetration remains limited. The conference concluded with Chen Baojian, Deputy Director of the Textile Product Development Center, recommending the establishment of a unified industry defect standard database. This strikes at the core of the issue: without standards, AI lacks a 'textbook' for training, and models from different enterprises cannot interoperate.