Sweden's EON Enters Secondhand Garment Supply Chain with AI Sorting Module, Reshaping Textile Machinery Landscape

The global secondhand apparel market is expanding at over 15% annually, yet garment sorting remains labor-intensive and error-prone. Swedish textile machinery maker Eton Systems showcased its aUPS module at Texprocess in Frankfurt, using AI vision and automated transport to address this bottleneck.

Background: From Production to Recycling Eton Systems, a member of the Swedish textile machinery association TMAS, built the aUPS module on its proven UPS material handling system. By adding AI-powered visual recognition, the module can rapidly identify fabric type, color, and style of used garments and sort them automatically.

The system relies on the ETONingenious software platform, originally developed for in-factory material flow management, now extended to the recycling stage. This means Eton is not inventing a sorting machine from scratch but upgrading existing industrial hardware with AI algorithms targeting a previously overlooked niche.

From an industry perspective, this move signals that traditional textile machinery suppliers are expanding their service boundaries from manufacturing to circular economy. For industrial clusters like Keqiao and Shengze—home to chemical fiber and fabric production—and apparel hubs like Nantong and Changshu, this shift will directly influence downstream equipment purchasing decisions.

Industry Impact: How Sorting Efficiency Transforms the Supply Chain The core challenge of used garment sorting lies in material diversity—a cotton T-shirt and a polyester jacket require completely different processing paths. Manual sorting processes roughly 200-300 pieces per hour, with accuracy declining over long shifts. Eton's aUPS module claims to handle several thousand pieces per hour with far higher consistency.

What does this mean for exporters? For companies dealing in secondhand garments—China is the world's largest exporter—higher sorting efficiency directly lowers per-unit processing costs and shortens the turnaround from collection to resale. Sorting centers in Guangzhou and Yiwu currently rely heavily on manual labor; automation could reshape regional cost structures.

More importantly, AI sorting can accurately identify fabric composition, which is critical for recycled fiber producers. The finer the sorting, the higher the purity of recycled fiber, and the more stable the raw material quality for downstream spinning mills. From recycled polyester plants in Shengze to circular fabric lines in Keqiao, this automation accelerates the closed loop of 'old garment → recycled fiber → new fabric.'

Practical Recommendations ### For Buyers (sorting centers, recycled fiber mills) - Prioritize the system's ability to identify blended fabrics—the biggest pain point—rather than just sorting speed. - Evaluate compatibility with existing material handling systems; the aUPS module requires Eton's UPS system, so start with a small pilot line to minimize upfront costs. - Pay attention to software update terms; AI models need regular retraining for new fabrics and styles. Choose vendors that support remote upgrades.

For Exporters (secondhand garment traders) - Automated sorting will change export grading standards. Early collaboration with equipment makers to define classification rules can create a competitive edge. - Monitor European requirements for sorting transparency; AI systems provide traceable data that help meet upcoming EU textile waste regulations. - Consider joint procurement with recycled fiber mills to share costs and secure raw material channels, building a vertical collaboration from collection to recycling.