One out of every five sweaters in the world is produced in Dalang Town, Dongguan, Guangdong. This wool knitting powerhouse, with an annual garment output exceeding 900 million pieces and a full-industry-chain transaction value surpassing 72 billion RMB, hosted the 95th International Wool Textile Conference in late June. As natural fibers meet digital technology, a traditional industrial cluster is being redefined.
From Manufacturing Base to Technology Testbed
Dalang's scale is not new, but what deserves attention is the R&D intensity of China's textile industry behind it—rising from 1% in 2020 to 1.3% in 2024. This means that despite a 0.3% year-on-year decline in revenue for large-scale enterprises, the industry has not cut innovation budgets. On the contrary, total profits surged 19.3% year-on-year, with an operating profit margin of 2.9%, signaling substantial improvement in profitability.
This 'revenue decline, profit rise' scissors gap is precisely a signal of technology dividends. For example, AI-assisted design has improved pattern design efficiency by over 200% and reduced sample development costs by more than 60% in some companies. These are not lab data but real cases implemented in Dalang and surrounding industrial belts.
Penetration Curve of Smart Manufacturing
The digitalization rate of key processes in China's textile industry has reached 63.2%, higher than the national manufacturing average. A more direct indicator is the number of 5G factories: in 2024, 30 textile enterprises were listed in the national 5G factory directory. This means every node of the wool textile chain—from smart workshops to AI quality inspection, from industrial internet to digital workers—is undergoing transformation.
Data from Jiangsu Yueda Textile's 'Lighthouse Factory' is representative: spindle employment reduced by about 70%, overall labor productivity increased more than fourfold, unit energy consumption decreased by 15%, and processing costs dropped by 26%. This set of numbers reveals a trend: smart manufacturing is no longer just 'icing on the cake' but a core tool for cost control and quality improvement.
For the wool textile industry, this means the traditional 'multi-variety, small-batch' production model is being taken over by flexible manufacturing systems. AI can quickly respond to design changes, smart scheduling systems optimize machine utilization, and green technologies like non-water dyeing and digital printing address long-standing environmental compliance pain points.
Divergence Signals in Trade Structure
From January to May 2025, China's textile and apparel exports totaled $116.73 billion, up a marginal 0.1% year-on-year. But internal structure shows clear divergence: textile exports reached $59.48 billion, up 1.7%; apparel exports were $57.24 billion, down 1.6%. This 'strong intermediate goods, weak finished goods' pattern reflects global supply chain reconfiguration—brands prefer to source fabrics and yarns, completing final sewing closer to consumer markets.
Meanwhile, domestic consumption remains resilient. Retail sales of garments, footwear, hats, and textiles by units above the designated size totaled 512.13 billion RMB, up 8.1% year-on-year, accelerating 5 percentage points from the same period last year. Strong domestic demand provides a buffer for companies facing export pressure.
## Uneven Investment Hotspots
Fixed asset investment in the textile industry grew 10.8% year-on-year, but apparel and chemical fiber sectors declined 10.3% and 14.5%, respectively. This data indicates capital is concentrating on upstream and core manufacturing links. The contraction in chemical fiber investment may relate to overcapacity and raw material price volatility; the decline in apparel investment reflects cautiousness among brands about new capacity.
For the wool textile industry, this is both a challenge and an opportunity. Upstream yarn and fabric links benefit from efficiency gains through smart transformation; downstream garment manufacturing needs to digest cost pressures through design innovation and brand premium.