The resale market for used garments is growing at over 15% annually, yet sorting has remained a labor-intensive bottleneck. Swedish textile machinery maker Eton Systems may have just shifted the equation. At Texprocess in Frankfurt, the company unveiled its aUPS AI-powered sorting module, integrating automated material handling with machine vision to tackle the classic trade-off between speed and accuracy in used garment sorting.
Background
Eton Systems, a member of the Swedish textile machinery association TMAS, developed aUPS as an intelligent upgrade to its proven UPS transport system. The module runs on the company's ETONingenious platform, using machine learning algorithms to classify garments in real time by style, material, color, and wear level. Based on predefined recovery routes—such as resale, fiber regeneration, or landfill diversion—the system automatically routes each item to the appropriate chute.
The technical breakthrough here is not in hardware innovation but in fusing the 'transport' function of a conveyor system with the 'decision-making' capability of AI. What previously required dozens of skilled workers to visually inspect and manually sort can now be handled by a set of vision sensors and edge computing devices. For regions with high labor costs like Europe and Japan, the economic case for this substitution is already solid.
Industry Impact
Automation in used garment sorting first threatens sorting centers in Southeast Asia and Eastern Europe, which have long relied on cheap labor to process exports from the West. If AI sorting modules achieve scale in Europe, exporters may pre-sort garments locally and ship 'classified' lots abroad, squeezing the margins of middlemen.
For China's textile industry, the implications are twofold. As the world's largest producer of synthetic fibers, China consumes vast amounts of recycled polyester chips and waste textiles. Higher sorting precision—especially in separating pure cotton from poly-cotton blends—directly improves the quality of recycled polyester staple fiber and recycled cotton yarn. However, if Europe builds its own sorting capacity, China's role as a global garment sorting hub could face pressure.
For brands and retailers, better sorting efficiency makes 'closed-loop' recycling more commercially viable. Previously, take-back programs struggled with high back-end sorting costs. With AI sorting, collected garments can be routed faster and more accurately to resale or fiber regeneration channels, lowering the operational hurdle for brands to build their own recycling systems.