The textile industry is at a critical inflection point for AI deployment. In late July, a conference in Keqiao, Shaoxing, signaled a clear shift: AI is moving beyond concepts to directly tackle core factory decision-making. Zhongke Wenge's 'Industrial Decision Brain' product aims to break down barriers in data, models, and experience, demonstrating minute-level decision-making and 30%-50% cost reduction in trade order processing, weaving scheduling, and dyeing color matching. What does this mean for buyers, factories, and foreign trade firms? AI's penetration is set to reshape efficiency and cost structures across the textile supply chain.
Data Silos and Experience Gaps: Real Obstacles to AI Adoption
Over the past decade, textile factories have widely deployed ERP, MES, and PLC systems, generating vast data. However, industry data shows most enterprises still use this data only for recording and querying, not for production optimization or decision support. Zhongke Wenge CTO Cao Jia identified three core obstacles: data is difficult for AI to interpret directly, general-purpose large models cannot adapt to complex process mechanisms, and critical expertise remains trapped in a few experienced technicians, risking loss with personnel turnover.
From an industry perspective, these obstacles highlight the gap between informatization and intelligence. In dyeing color matching, traditional processes rely heavily on senior workers' intuition, requiring 7-8 sample rounds, leading to high costs and long cycles. This 'experience black box' limits capacity flexibility and causes knowledge loss during personnel changes. Effective AI deployment must first solve data standardization and experience structuring.
From Single-Point Tools to Systemic Decision-Making: AI Penetrating the Textile Value Chain
The 'Industrial Decision Brain' provides a complete capability system, not isolated tools. Its DOMA architecture—from data, ontology, and models to agents—connects the production chain layer by layer. In textile scenarios, this system covers multiple key nodes:
- Trade order processing: Manual checking of a foreign order took 30-60 minutes; AI now automatically identifies fabric specifications, colors, quantities, and delivery dates, completing the task in seconds with over 95% accuracy.
- Weaving scheduling: Traditional scheduling relying on manual experience for equipment, capacity, inventory, and delivery took hours; AI now integrates multiple constraints to assign tasks automatically, compressing scheduling to minutes and responding to rush orders and equipment anomalies in real time.
- Dyeing color matching: AI generates recommendations using historical formulas, spectral data, and dye properties, reducing sample rounds from 7-8 to 3-4, cutting costs by 30%-50%, and converting senior expertise into reusable models.
- Dyeing vat scheduling: Vat scheduling time dropped from about 2 hours to about 2 minutes, with AI generating explainable plans to quickly adapt to order changes.
- Completion feedback and order delivery: Cross-departmental verification and process triggering compressed from 1.5-3 hours to about 5 minutes, with the system automatically linking sales, warehousing, quality control, and finance.
These data points show AI evolving from a single-point efficiency tool into a closed-loop system spanning 'perception-decision-execution.' Notably, AI not only optimizes digital processes but also translates decisions into on-site actions through embodied robots for intelligent inspection, yarn hanging, and cloth doffing. The inspection robot identifies over 20 types of textile defects with over 95% accuracy and 8-hour battery life; the yarn hanging robot achieves ±0.1mm repeat positioning accuracy.
Impact on the Supply Chain: Cost Restructuring and Higher Competitive Thresholds
Large-scale AI application in textile factories will directly alter cost structures. In dyeing color matching, 30%-50% cost savings can significantly expand profit margins for small and medium-sized dyeing firms, but also demand higher data quality and system compatibility. Zhongke Wenge emphasizes its solution does not require tearing down existing systems; instead, it connects order, equipment, material, process, and quality data to form a unified business language.
For buyers, AI-enabled factories mean shorter delivery times and higher quality consistency. Compression of foreign order processing time reduces delays from manual errors. For foreign trade firms, AI-driven scheduling and delivery closed loops enhance customer trust, especially in small-batch, multi-variety quick-response orders where minute-level decision-making becomes a key differentiator.
Challenges remain: uneven data quality, weak digital foundations among SMEs, and AI talent shortages. As noted by Yin Qiang from the China National Textile and Apparel Council, AI technical capability does not automatically translate into industrial empowerment; it must be deeply integrated with specific scenarios and business processes.