Three major U.S. retailers used their Q1 earnings calls to reveal their AI strategies, signaling a clear shift in textile order flows. Best Buy, Gap, and Dick's Sporting Goods all confirmed that AI investments are moving from back-office efficiency tools to front-line sales engines, directly altering inventory decisions and procurement rhythms. For textile supply chains dependent on large retail orders, the era of big, long-cycle, standardized orders is ending.
How AI-Driven Inventory Changes Reach Fabric Mills
Gap mentioned during its call that AI now participates in SKU-level replenishment decisions at the store level, analyzing regional sales data in real time to adjust order quantities automatically. The direct consequence: brand purchase orders will shift from quarterly bulk orders to high-frequency, small-lot rolling replenishments. Fabric suppliers must adapt to shorter lead times and more frequent repeat orders. Dick's Sporting Goods' case is more telling: its AI system not only predicts sales volumes but also links directly to fabric composition and color preferences, requiring upstream mills to have faster switching capabilities in color card preparation and greige fabric stocking. Although Best Buy focuses on electronics, its AI-driven personalized recommendation logic applies equally to apparel retail, meaning the same fabric may be repeatedly purchased by different channels in different colorways and packaging.
Flexible Supply Chain Is No Longer an Option but an Entry Barrier
A common thread across the three retailers' statements is that AI's core value lies in reducing inventory redundancy and stockout losses. This directly squeezes the traditional "betting" procurement model—brands are no longer willing to stockpile large quantities of greige fabric for uncertain fashion trends. Textile mills must lower minimum order quantities (MOQs) while maintaining consistent quality. For industrial clusters like Shengze and Keqiao, which specialize in chemical fiber and blended fabrics, this means reevaluating dyeing and finishing workshop scheduling, shifting from "full-batch production" to "quick-response combo orders." Home textile companies in Nantong may face similar challenges as AI systems optimize replenishment frequencies for bedding and towels.
Data Connectivity Will Replace Price as the New Competitive Edge
An overlooked detail: the efficiency of retailers' AI systems heavily depends on the accuracy of upstream data. If fabric suppliers cannot provide real-time inventory, in-production status, and logistics tracking information, AI prediction models become distorted. Therefore, future order allocation will no longer focus solely on unit price but on who has more open and standardized data interfaces. This is a watershed moment for export-oriented factories—those able to achieve direct data integration with brands' ERP systems will receive "preferred supplier" tags, while those still relying on email and Excel risk exclusion from AI-driven procurement loops.
Three Medium-Term Impacts on the Textile Industry
First, order fragmentation will accelerate; the proportion of single-batch orders exceeding 5,000 meters will decline steadily, with 1,000-3,000 meter quick-response orders becoming mainstream. Second, inventory risk will shift upstream; fabric suppliers will need to carry more "greige but not colored" intermediate inventory, raising cash flow management demands. Third, technology investment divergence will intensify; leading fabric companies will adopt AI scheduling and smart warehousing, while smaller factories unable to digitize will face irreversible order losses.