The textile industry has talked about moving from informatization to intelligentization for years, but the real bottleneck is not equipment or data—it's 'decision-making.' When ERP and MES systems are fully deployed but data remains stuck at the query and reporting level, unable to translate into production instructions, intelligentization becomes a castle in the air. On July 27, Zhongke Wenge launched the 'Industrial Decision Brain' in Keqiao, Shaoxing, targeting this core gap—bridging the decision loop from data to execution, offering a new key for textile enterprises to realize AI implementation.
Three Major Obstacles: Data, Models, and Experience
At the launch event, Zhongke Wenge CTO Cao Jia pinpointed the core contradiction in current industrial intelligentization: factories do not lack equipment, systems, or data—they lack the decision intelligence to connect data with execution. Two decades of informatization have allowed textile enterprises to accumulate massive amounts of data, but this data is often fragmented in format and ambiguous in meaning, making it difficult for AI to understand directly. More critically, textile processes involve complex mechanisms and rules that general-purpose large models cannot handle for specialized scenarios like scheduling, color matching, and quality inspection.
The third obstacle comes from the loss of experience. The tacit knowledge of veteran loom operators and color masters has long existed in the form of 'feel' and 'eye,' and once personnel change, this knowledge dissipates. The underlying architecture of the Industrial Decision Brain—DOMA (Data-Ontology-Models-Agents) and the 'Base-Pivot-Core-Brain-Edge' decision AI tree—is designed specifically for these three obstacles: first, through the ontology layer to make data understandable to AI; then using specialized models to adapt to process logic; and finally, through agents to transform decisions into equipment commands and solidify experience into a reusable knowledge base.
Quantitative Breakthroughs in Key Textile Links: From 30 Minutes to Tens of Seconds
The Industrial Decision Brain is not just a concept—it has already penetrated multiple key links in textile production and operations. In trade order processing, the traditional model required manual cross-referencing of multiple documents for a single foreign trade order, taking 30 to 60 minutes. After system implementation, key information such as fabric specifications, colors, quantities, and delivery dates can be automatically recognized in tens of seconds, with accuracy exceeding 95%. This means trade order handlers can shift their focus from mechanical checking to customer communication and exception handling.
Weaving scheduling is another pain point. Previously, it relied on veteran workers to coordinate equipment, capacity, processes, and delivery schedules, often taking hours for a single plan. When rush orders or equipment anomalies occurred, the entire plan had to be redone. The Industrial Decision Brain can automatically assign tasks under multiple constraints, compressing scheduling time to minutes while supporting real-time recalculation, enabling dynamic responsiveness.
Breakthroughs in dyeing are even more tangible. Color matching traditionally relied on repeated sampling by senior workers, often requiring 7 to 8 rounds to finalize a formula. By integrating historical formulas, spectral data, and dye attributes, the system reduces sampling rounds to 3 to 4, saving 30% to 50% in costs. Dyeing vat scheduling time dropped from about 2 hours to about 2 minutes, and cross-departmental collaboration for completion feedback and order delivery was compressed from 1.5 to 3 hours to about 5 minutes. These numbers represent a substantive leap from human-driven to data-driven operations.
Conductive Effects on Industrial Clusters: Lessons from Keqiao
The choice of Keqiao, Shaoxing, as the launch site is no accident. Keqiao is the world's largest textile distribution center, gathering thousands of weaving, dyeing, and trading companies with concentrated scenarios and pain points. The deployment of the Industrial Decision Brain in Keqiao will first impact the operational efficiency of local enterprises: faster trade order processing means stronger foreign trade order-taking capabilities; optimized weaving scheduling directly improves capacity utilization; and reduced dyeing costs enhance profit margins.
For upstream raw material suppliers, the improved scheduling efficiency of downstream factories will bring more stable procurement rhythms and more accurate delivery forecasts, reducing inventory accumulation. For downstream brand buyers, order delivery predictability will improve, potentially boosting the response speed of fast-fashion supply chains. However, it should be noted that the deployment cost and technical threshold of this system are not low. Small and medium-sized enterprises may face dual challenges of capital and talent, and in the short term, leading companies will benefit first.