Jack Technology will deploy Siemens’ new industrial AI orchestration software, Intelligence Center X, starting with Mendix for agentic low code development, and Designcenter™ software for advanced product engineering – both from the Siemens Xcelerator portfolio – to streamline product development, reduce complexity and support new applications for AI and humanoid robotics in sewing workshops.

Headquartered in China, Jack Technology serves customers in more than 160 countries and operates in a highly competitive market shaped by rising demand for shorter lead times, increased product customisation and improved cost efficiency. To address these pressures, the company is integrating AI‑enabled systems and humanoid robots into traditional sewing environments to improve flexibility, responsiveness and production performance.

To support this transformation, Jack Technology will deploy an integrated suite of Siemens’ technologies, including Intelligence Center X and Mendix to rapidly build, deploy and iterate manufacturing and robotics‑related applications, and Designcenter engineering software to support product design, simulation and manufacturing for complex mechanical systems such as humanoid robots. Siemens will also provide specialised technical training to support the development of a dedicated digital platform for the sewing equipment industry.

“Collaborating with Siemens marks a critical step in advancing our digital strategy,” said Hu Wenhai, President of New Product Development at Jack Technology. “The flexibility of Intelligence Center X – Mendix, combined with the capabilities of Siemens’ Designcenter, will help us manage growing manufacturing complexity, accelerate innovation and deliver more intelligent solutions to our customers.”

The integration of Siemen’s digital engineering and agentic enterprise system is designed to deliver measurable operational benefits. Jack Technology expects to shorten development cycles, improve product quality, optimise production workflows, reduce costs and increase overall manufacturing efficiency by up to 30%.

The collaboration reflects a broader shift across the apparel manufacturing sector, as companies adopt software driven engineering, agentic low code development and AI enabled automation to modernise operations and improve responsiveness to evolving market demands.

“We are pleased to collaborate with Jack Technology to explore the potential of intelligent manufacturing, including the application of AI and humanoid robotics,” said Raymond Kok, CEO, Mendix, a Siemens business. “By combining agentic low code development with digital product engineering, manufacturers can move faster from concept to production.”

“This collaboration highlights how digital technologies are reshaping apparel manufacturing,” said Leo Liang, senior vice president and managing director for Greater China, Siemens Digital Industries Software. “With Siemens’ AI-enabled  software portfolio, Jack Technology is well positioned to strengthen innovation capabilities and deliver greater value across the global apparel manufacturing ecosystem.”

The agreement has been signed to support the introduction of automation and digital solutions aimed at improving production efficiency across the textile sector. The signing of the memorandum marks a step towards strengthening practical cooperation between Uzbekistan and China in the modernisation and digital transformation of Uzbekistan’s textile sector. The memorandum focuses on Uzbekistan textile automation and related digital solutions.

During the visit and subsequent negotiations, representatives of Uztextileprom and the Tashkent Institute of Textile and Light Industry took part in discussions with the Chinese firm. The sides, including Uztextileprom, reviewed prospects for deploying modern technologies in textile production processes and explored opportunities related to textile automation and process optimisation.

Representatives of Shaoxing Huangxi Intelligent Group visited the MS Barer-Textile enterprise to examine existing production processes and assess possibilities for implementing advanced technological solutions. The assessment focused on practical applications of modern technologies and digital transformation measures that could be piloted at local enterprises.

Under the memorandum, cooperation will proceed through experience sharing, technical consultations and pilot projects designed to raise the technological level of textile enterprises. The agreement emphasizes joint pilot activities and technical support to promote textile automation and the deployment of digital transformation tools across the sector.

Under the partnership, MUSINSA will deploy Exotec’s Skypod robotic system alongside the company’s Deepsky warehouse execution software at the new automated facility. MUSINSA operates one of South Korea’s leading fashion platforms, carrying a wide mix of domestic independent labels and global premium names, with logistics teams managing hundreds of thousands of SKUs across its network. That breadth creates operational complexity, especially when inventory and order volumes swing sharply around seasonal drops and major online sales campaigns.

Exotec says the automation is designed to relieve pressure during those peaks, improving both speed and control. The Skypod system uses autonomous robots that travel inside storage structures reaching up to 14 metres, retrieving items rapidly typically in two minutes or less, according to the company. Importantly, the solution is built to expand over time, enabling MUSINSA to scale capacity as business grows without having to redesign the entire warehouse.

MUSINSA expects the project to lift picking performance, increase throughput and sharpen inventory visibility—benefits that can be critical during high-traffic trading periods when customer expectations around delivery timelines are at their toughest. The retailer also sees the technology as a way to improve labour productivity while building a long-term automation foundation that can support future expansion.

Exotec chief executive Romain Moulin said the Korean market is an important strategic milestone for the company’s regional ambitions. South Korea, he argued, is “the most dynamic e-commerce market in Asia”. “In particular, logistics demands in the apparel sector have increased significantly due to the global popularity of K-fashion,” he said. “We are delighted to work with an innovative partner like MUSINSA, and this project is an important indicator of our continued expansion in the APAC market.”

The MUSINSA deal marks Exotec’s first live deployment in Korea and adds to a global customer roster that includes Uniqlo, GAP Inc. and Oxford Industries. Exotec said its approach—combining robotics with execution software—aims to give retailers more flexibility as supply chain pressures intensify and fulfilment requirements evolve.

For MUSINSA, the Yeoju project is a concrete move toward South Korea warehouse automation at scale, reflecting a wider push among fashion players to modernise logistics for faster, more reliable e-commerce delivery. For Exotec, it is a high-profile entry point into a market where South Korea warehouse automation is becoming a competitive necessity rather than a future ambition.

Warehouses are becoming machines, not just buildings

The modern fulfilment centre looks less like a sea of shelves and more like a choreographed production line. Where traditional warehouses depended on staff walking long aisles to find items, today’s facilities increasingly deploy robotics, automated storage and retrieval systems and AI-based orchestration to compress time and distance. Robots can bring inventory to the picker rather than the other way around, raising throughput and improving accuracy. That reduction in mis-picks matters when operations are processing tens of thousands of parcels a day and a small error rate becomes a costly flood of customer service cases.

Automation also stabilises performance during demand spikes. When a promotion hits or seasonal volumes surge, automated workflows can scale more predictably than purely manual operations, while also lowering the risk of mistakes that erode margin through reships and refunds.

From one mega hub to many smaller nodes

The second structural shift is geography. To meet the promise of next-day and same-day shipping, retailers are moving away from reliance on a single central warehouse and toward distributed networks of fulfilment sites placed closer to where customers live. With inventory positioned in the right places, shipping distances shrink, delivery times tighten and carrier costs can fall.

For many retailers, this distributed model is too complex to run without specialist support. Inventory has to be balanced across locations, demand has to be forecast at a regional level and replenishment decisions have to happen continuously. As a result, many businesses lean on fulfilment partners to manage the complexity and keep the network responsive.

Micro fulfilment rises as speed expectations harden

The push for faster delivery has also created space for micro fulfilment small, highly automated facilities located in dense urban areas or even integrated into existing stores. These sites are built for rapid processing of local orders, reducing the last-mile distance and helping retailers meet tight delivery windows.

Grocery has been a major catalyst here, where speed and freshness are non-negotiable. But micro fulfilment is increasingly relevant across categories where consumers expect near-immediate availability, particularly in major cities.

Software now dictates flow, not just labour

Hardware gets attention, but the real nervous system of fulfilment infrastructure is software. Advanced warehouse management systems provide real-time visibility into inventory, optimise where products are stored and coordinate the choreography of picking, packing and dispatch. With machine learning layered on top, these tools can anticipate demand shifts, flag stock risks and suggest rebalancing moves before service levels drop.

That intelligence reduces two expensive failures at once: stockouts, which cost sales and damage trust, and excess inventory, which ties up capital and increases markdown pressure. When demand changes quickly, visibility and responsiveness are the difference between a smooth operation and a costly scramble.

AI and predictive analytics reshape planning

Predictive models are increasingly being used to decide what stock should sit where, and when. By analysing historic sales, seasonality and behavioural patterns, retailers can pre-position inventory across regions so orders travel fewer miles and process faster. The same forecasting logic can be applied to workforce planning and transport routing, cutting overtime costs and improving utilisation.

This is also where many retailers gain the confidence to promise faster delivery: not by working harder, but by planning earlier and moving stock before customers click “buy”.

Last mile delivery is still the hardest mile

Even the best warehouse cannot fix a broken last-mile network. The final journey from a local depot to the customer’s door remains the most expensive and operationally messy step in e-commerce logistics. To reduce friction, retailers are combining fulfilment operations with alternative delivery models, including local courier partnerships, parcel lockers and crowdsourced networks. Longer term, autonomous vehicles and drones remain on the horizon, but even without them, the goal is the same: increase density, cut failed deliveries and reduce per-drop cost.

Sustainability becomes operational, not optional

As e-commerce grows, so does the environmental burden of packaging, transport and energy-intensive facilities. Many retailers are now investing in lower-impact operations: energy-efficient warehouses, route optimisation, recyclable packaging and renewable power sources such as solar. Some are electrifying delivery fleets where feasible, reducing emissions in urban zones.

The shift is partly reputational, partly regulatory and partly economic. More efficient routes and better packaging choices often reduce cost as well as carbon.

Returns management is a profit lever

Returns are a defining reality of online apparel and footwear retail, and they can quickly erode profitability. Modern systems are therefore being designed to process returns faster, sort items accurately and route them to the most value-preserving outcome restock, refurbish, secondary channels or liquidation. Automated sorting and tracking tools help compress cycle time, which is critical because resale value often drops as a season moves on.

Data is also becoming a prevention tool. By analysing return reasons and patterns, retailers can improve sizing guidance, imagery and product descriptions, reducing avoidable returns before they happen.

Stores rejoin the network through ship from store and BOPIS

Physical stores are being repurposed as fulfilment assets. Ship-from-store turns local inventory into a delivery advantage, enabling faster fulfilment and better stock utilisation. BOPIS buy online, pick up in store offers convenience for customers and lowers last-mile delivery cost for retailers, while also creating opportunities for incremental in-store purchases.

The pandemic locked in the new playbook

COVID-19 accelerated many of these moves. As online demand surged, retailers invested rapidly in automation, logistics software and new fulfilment locations. Those capabilities did not disappear when stores reopened; they became the baseline. Consumers learned to expect speed and reliability, and retailers learned that logistics excellence is a core competitive advantage.

Where fulfilment goes next

Fulfilment will keep evolving as robotics improves, AI becomes more predictive and data becomes more real time. The retailers that win will be those that treat fulfilment not as a cost centre, but as a strategic platform that shapes customer experience, margins and brand trust.

In the end, the front-end of online retail may be what shoppers see, but fulfilment infrastructure is what they feel every time an order arrives on time, intact and exactly as expected.

That’s the central message of Linnworks’ 2026 State of Commerce Ops Report, based on input from 500 companies across the two markets. All respondents sit in the mid-market band, with annual revenues between $7.5m and $100m, and are navigating the complexity of running across multiple digital channels and fulfilment networks.

The study indicates that growth is broad, with 89.6% of UK respondents and 88.8% of US respondents reporting moderate to significant year-over-year improvement. Yet Linnworks says scaling remains uneven: “scalability is selective,” and tends to correlate with automation levels and channel maturity.

Linnworks CEO Jon Bahl said: “As retailers navigate the ever-evolving ecommerce industry, one thing remains clear: growth and operational maturity are inseparable. Our new survey data sheds light on the ongoing growth of the market while providing clients with strategies for scalability and progress.”

Inventory visibility emerged as a persistent weakness for mid-sized online sellers. Only around one-third of respondents said they have excellent visibility across all channels and warehouse locations, a shortfall that can magnify errors as mid-market ecommerce retailers add marketplaces, geographies and fulfilment nodes. Linnworks noted that inaccurate stock tracking, inconsistent system reliability and fulfilment execution issues remain common as companies expand.

The report also suggests AI is moving rapidly from optional to foundational. More than half of those surveyed said they already use AI chatbots for customer support, including 60.8% in the US and 54.8% in the UK. Overall, more than 95% said they use AI somewhere in their operations, and most expect to increase adoption in 2026 once they identify “effective” use cases. Linnworks said AI is also being deployed in demand forecasting, inventory management, marketing optimisation and fraud prevention.

Automation is increasingly viewed as standard operating practice rather than a differentiator. In the UK, 64.8% of surveyed businesses reported that their operations are majority or highly automated; in the US, the figure was 60%. Linnworks says the depth of automation still varies widely, and those differences show up in resilience, growth outcomes and leadership confidence—separating companies that can scale smoothly from mid-market ecommerce retailers that grow into operational friction.

On fulfilment, the report argues that logistics choices remain central to customer experience and operational stability. Retailers working with multiple carriers were described as more resilient during peak periods, encountering fewer disruptions and having greater flexibility to manage delivery speed and cost.

Equipped with advanced sensors and intelligent software, the FiberGuard BCF system is designed to sustainably enhance both process stability and product quality. This smart system continuously measures yarn tension in real time between the twisting and winding stages. Should any deviations in tension occur, the FiberGuard BCF system swiftly intervenes and makes autonomous adjustments, reducing waste and promoting greater efficiency and sustainability.

Operators benefit from an HMI that provides access to live data, customizable reporting thresholds, and the ability to enable automatic adjustments. Additionally, the FiberGuard BCF system allows for the creation of personalized reference curves for yarn tension during the doffing process.

Jan Pauer, Head of Service Products at Neumünster and responsible for introducing the new system, stated: “Our discussions with interested BCF producers at ITMA Asia have shown that we have listened to our customers. FiberGuard is the next step in all Neumag BCF processes. The combination of hardware and intelligent software opens the door to a new production possibility characterized by reliable thread tension measurement and automated process adjustment.”

This launch positions the FiberGuard BCF system as a critical advancement in yarn production, offering manufacturers enhanced control and optimized efficiency in their operations.

This project is a significant component of Crystal International’s broader strategy to integrate advanced technology into its operations.

The new facility will be developed by the company’s knits division at its lifestyle wear factory in China. It will feature an automated cutting workshop and a centralized smart warehouse, equipped with an advanced storage and retrieval system (AS/RS). These innovations are designed to improve efficiency in inventory management and delivery by analyzing real-time data related to production requirements and transport routes.

The logistics center, which will operate using a rooftop solar photovoltaic (PV) system, is expected to start full operations in the first quarter of 2026. It is anticipated that the center will reduce fabric transfer times by up to 75%. The AS/RS will facilitate the autonomous movement of fabric and finished goods from storage to loading points. Moreover, Automated Guided Vehicles (AGVs) will optimize processes by managing the transport of sewn components, contributing to a fully closed-loop auto-material flow system within the center.

Sam Lau, General Manager of Digital Manufacturing at Crystal International, commented: “We are driving digitalization and automation in line with the whole Group’s ambition, achieving smarter processes, higher operational efficiency and productivity, as well as better sustainability performance. The solution ensures us a winning edge and continued success.”

This initiative aligns with Crystal International’s Crystal Sustainability Vision 2030, which aims for all its facilities to become smart factories through the adoption of innovative technologies. The establishment of this new logistics center marks a significant advancement in the company’s ongoing quest for smarter production capabilities.

Established in 1970, Crystal International operates over 20 production sites across five countries, including Vietnam, China, Cambodia, Bangladesh, and Sri Lanka. The company produces a wide range of apparel products, including lifestyle wear, sportswear, denim, intimate apparel, sweaters, and knitted fabrics. Through its “Co-creation” business model, Crystal International collaborates with leading global brands to offer high-quality and innovative manufacturing solutions.

• AI-powered sorting systems achieve 95-98% accuracy identifying 13+ fiber types in <2 seconds per garment, eliminating labor-intensive manual sorting
• Convolutional neural networks (CNN) process near-infrared spectral data to classify fibers by composition, color, and contamination level
• Robotic systems handle sorted materials with precision, reducing manual labor requirements by 70-80% while improving worker safety
• Machine learning models continuously improve through training datasets, achieving incremental accuracy gains of 1-2% quarterly
• Integrated AI systems reduce overall textile recycling cost by 25-35% through efficiency gains offsetting automation capital investment
• By 2030, 60%+ of commercial textile recycling facilities will employ AI-driven sorting, up from <5% in 2023

Intelligence Transforming Physical Recycling Operations

The textile recycling industry has historically been labor-intensive. Sorting collected garments by fiber type, color, contamination level, and condition has required thousands of workers manually evaluating each item. This labor intensity has constrained scaling, increased cost, and created geographic limitations based on wage dynamics.

Artificial intelligence and automation are fundamentally reordering this value chain. Rather than human workers manually evaluating fibers through visual inspection and rudimentary testing, intelligent systems now perform this evaluation automatically with accuracy exceeding human capability. This transformation from manual labor-dependent processes to AI-driven automation represents critical infrastructure advancement enabling textile recycling scaling.

The Manual Sorting Limitation

Traditional textile sorting employs workers manually examining each garment. Assessment includes visually identifying fiber type (though synthetic fibers are visually indistinguishable), detecting contamination, classifying colors, and grading condition. Accuracy depends on worker training, attention, and consistency variability inherent in human evaluation.

Worker throughput in manual sorting facilities averages 50-80 garments per hour per worker, meaning a facility processing 1,000 garments daily requires 15-20 dedicated sorters. In developing regions where labor costs are lowest, this represents viable economics; in developed regions, economics become challenging. Geographic limitations on facility location based on labor cost availability have constrained recycling infrastructure development.

Furthermore, manual sorting accuracy limitations create downstream processing problems. Incorrectly identified fibers directed to inappropriate recycling pathways produce contaminated feedstock reducing output quality and complicating processing. Contamination rates in manually sorted material frequently exceed 15-20%, requiring additional cleaning steps.

Near-Infrared Spectroscopy: The Sensing Foundation

The technological foundation enabling AI textile sorting is near-infrared (NIR) spectroscopy. Every material possesses characteristic spectral signature the pattern of light reflection and absorption across the infrared spectrum. Different fibers (polyester, cotton, nylon, acrylic, wool) each possess distinct spectral signatures enabling identification.

NIR spectroscopy systems direct infrared light at textile samples and measure reflected light intensity across wavelength ranges 700-2,500 nanometers. Modern systems provide high spectral resolution, capturing detailed reflection patterns. These spectral data form input for machine learning classification algorithms.

The advantage of NIR-based sorting: it requires no chemical analysis, works rapidly, and provides quantitative rather than subjective data. A garment passes under NIR scanner in approximately 1-2 seconds, and spectral data is transmitted to classification algorithm. Speed and objectivity enable automation impossible with manual visual inspection.

Machine Learning Classification Algorithms

Raw spectral data arrays of light intensity measurements across hundreds of wavelength channels must be classified into textile type categories. This classification is the domain of machine learning algorithms trained on datasets containing thousands of known spectral samples with confirmed fiber compositions.

Convolutional neural networks (CNNs) represent the most successful approach. CNNs employ multiple processing layers that progressively extract features from spectral data, progressively refining pattern recognition. Early layers identify broad patterns; intermediate layers recognize more subtle spectral characteristics; final layers classify into specific fiber categories.

Training process requires preparing datasets of spectral measurements paired with confirmed fiber identifications. A comprehensive training dataset might include 10,000-50,000 individual spectral samples representing different fiber types, colors, blends, and contamination conditions. The neural network learns to recognize patterns in spectral data correlating to specific fiber types.

Modern systems achieve classification accuracy exceeding 95% across 13+ fiber types including polyester, cotton, nylon, acrylic, wool, silk, linen, elastane, polypropylene, polyamide, and others. This accuracy level exceeds manual human sorting consistently.

Computer Vision for Additional Classification

While NIR spectroscopy provides fiber composition information, computer vision systems capture additional classification parameters. High-resolution cameras document garment condition, structural integrity, staining, and contamination visible to light microscopy.

Computer vision algorithms, also employing CNNs, classify condition levels (unwearable, lightly soiled, heavily contaminated), identify damage patterns, and detect non-textile contaminants (plastic buttons, metal zippers, synthetic labels). This multisensory approach provides comprehensive garment assessment.

Integrated NIR-plus-vision systems achieve classification accuracy approaching 98%, far exceeding manual sorting capability. This accuracy translates directly to improved downstream processing outcomes and higher-quality recycled fiber output.

Robotic Material Handling

Once sorted into categories, textile materials must be physically moved transferred to appropriate processing lines, loaded into chemical reactors, staged for further processing. Robotic systems increasingly handle this material movement.

Robotic arms equipped with textile-gripping end-effectors can handle garments and fabric pieces with sufficient gentleness to avoid damage while providing speed and consistency exceeding manual handling. Modern textile-handling robots achieve speeds of 50-100 items per minute with positioning accuracy enabling precise placement.

Key advantage: robots operate 24/7 without fatigue, maintaining constant efficiency. Unlike human workers with fatigue, attention fluctuation, and shift-work limitations, robotic systems provide consistent throughput across full operating hours.

Sorting System Architecture and Integration

Contemporary textile recycling facilities integrate multiple technologies into coherent systems. Materials arriving at facility enter initial visual inspection where gross contamination is removed. Materials then proceed through NIR sorting systems where spectral data is captured and classified.

Classified materials drop into appropriately-labeled bins or conveyor lines directed toward specific processing pathways. Computer vision systems document condition and contamination. Materials requiring additional processing steps (stain removal, decontamination) are directed to appropriate stations; materials ready for direct recycling proceed to processing.

Throughout the facility, robotic systems handle material movement, reducing manual labor requirement significantly. A facility that historically required 20-30 manual sorters plus additional material handlers can now operate with 4-6 workers overseeing AI systems and handling exceptions.

Economic Impact and Cost Structure

Automation investment is substantial. A complete NIR + computer vision + robotic system for a facility processing 100 tons daily requires capital investment of approximately USD 5-8 million. This represents significant expense, historically limiting adoption to largest recycling facilities.

However, return on investment timeline is increasingly attractive. Through labor cost reduction alone, facilities achieve payback in 3-5 years. As automation capability improves and equipment cost declines (reflecting manufacturing scale), investment hurdle continues to lower. By 2027-2028, automation investments may achieve payback in 2-3 years.

Operational cost reduction from automation is substantial: 25-35% overall cost reduction compared to manual facilities. Labor cost reduction provides primary benefit; improved sorting accuracy reducing downstream processing problems provides secondary benefit.

Continuous Learning and Improvement

A critical advantage of AI-based systems: they improve continuously through machine learning. As systems process millions of textile items, classification algorithms are exposed to growing diversity of fiber types, colors, conditions, and blends. This exposure enables algorithm training and performance improvement.

Modern systems employ continuous learning protocols where operators can provide feedback when misclassifications occur. This feedback retrains algorithms improving future classification. Over time, system accuracy naturally improves approaching 97-99% as data accumulates.

This continuous improvement contrasts with manual sorting: human sorters’ performance plateaus after initial training; additional experience provides minimal accuracy improvement beyond proficiency threshold. AI systems demonstrate opposite trajectory: accuracy steadily improves throughout operational lifetime.

Emerging Capabilities: Advanced Contamination Detection

Beyond fiber identification, advanced AI systems now detect subtle contamination requiring post-consumer textile disposal. Oils, heavy metals, persistent organic pollutants, and microbial contamination can be detected through spectroscopic analysis combined with chemical sensor data.

Facilities employing advanced contamination detection prevent contaminated materials from entering recycling streams protecting downstream chemical processing from contamination complications and ensuring output material quality.

Workforce Transition and Skills Evolution

The automation trajectory necessarily implies reduction in manual sorting jobs a workforce challenge requiring policy attention and retraining programs. However, automation simultaneously creates new roles: system operators, technicians maintaining equipment, data scientists improving algorithms, and supervisory personnel overseeing automated processes.

Forward-looking facilities are implementing worker transition programs where manual sorters receive training in system operation and maintenance. Rather than job elimination, the transition is toward higher-skilled positions.

Geographic Implications and Infrastructure Development

AI automation’s capital requirement has traditionally concentrated textile recycling infrastructure in wealthy developed regions. As automation equipment cost declines and financing mechanisms improve, distribution should broaden geographically. Developing regions will progressively establish automated facilities even in secondary cities where labor cost advantage has historically been primary motivation.

This geographic diversification will shorten textile collection and processing logistics, reducing transportation impact and improving economic returns to regional communities.

Integration with Broader Industry 4.0 Evolution

Textile recycling automation is component of broader Industry 4.0 transformation affecting manufacturing globally. Internet-of-Things (IoT) sensors track material flow throughout facilities. Blockchain systems document material provenance and processing history. Digital twins of recycling facilities enable simulation and optimization.

These broader digital infrastructure elements increasingly integrate with textile sorting and processing automation, creating fully intelligent, transparent, data-driven recycling systems.

The Path to Unmanned Recycling Facilities

The ultimate trajectory of automation in textile recycling points toward largely unmanned facilities: materials enter, AI systems identify and sort, robotic systems handle material movement, chemical or mechanical processing proceeds, finished recycled fiber exits. Human supervision and maintenance occur, but direct labor requirement approaches near-zero.

This vision remains years away but is technically feasible. As automation technology matures and costs decline, unmanned textile recycling facilities will become operational reality, fundamentally restructuring recycling economics and geographic distribution.

Rebl’s AI-driven robots have been embedded in actual production environments for several years, automating various tasks that previously relied heavily on manual labor. These robots are equipped to handle sorting, palletising, and depalletising—tasks typically viewed as repetitive and time-consuming for warehouse staff.

Adopting Rebl’s robots-as-a-service (RaaS) model allows businesses to seamlessly integrate these automated solutions into their operations without the hefty upfront costs that often hinder traditional automation methods.

To drive its global expansion efforts, Rebl Industries has appointed Nicholas Tengelin as its first external CEO, tasked with scaling the company’s market influence and establishing its competitive standing.

Rebl highlights that the warehousing industry is experiencing a significant transformation akin to the shifts seen in automotive and industrial manufacturing through robotic automation. Co-founder Alexander Westerling, who also serves as CEO of Pulsen Fusion, emphasized, “Our AI-powered robots quickly step into warehouse operations, supporting a decrease in repetitive tasks for employees and an increase in overall efficiency. This game-changing technology opens new automation possibilities for sectors previously unable to leverage robotics.”

With this partnership, H&M and Rebl Industries are set to redefine the landscape of warehousing, enhancing productivity and setting a precedent for future automation initiatives within the fashion and retail sectors.

A pioneer in this field is Softwear Automation, a technology innovator which has developed a fully autonomous sewing solution: they are making robots that can sew clothes – called ‘Sewbots’.

Softwear Automation has just announced the successful close of its $20 million Series B1 funding round. The round was led by a strategic investment from BESTSELLER, the Danish fashion company behind brands such as JACK & JONES, VERO MODA, and ONLY. The investment was made through Invest FWD, BESTSELLER’s dedicated innovation and investment platform. Current investors also participated in the financing.

BESTSELLER, CFO Thomas Børglum Jensen states, “At BESTSELLER, we look for innovations that can support progress and changes in the fashion industry. We believe that Softwear Automation can help address some of the key challenges we face across the industry – from speed and flexibility to reducing the environmental impact of garment production. We are pleased to support their development and look forward to exploring how their advanced technology can help us move forward.

As part of the collaboration between Softwear Automation and BESTSELLER, CFO Thomas Børglum Jensen, on behalf of BESTSELLER, joins the board of Softwear Automation.

“This partnership with BESTSELLER and the support from Invest FWD are not just a vote of confidence in our technology — they are a powerful catalyst for the future of on-demand and localised apparel manufacturing,” said Palaniswamy “Raj” Rajan, Chairman and CEO of Softwear Automation.

The funding round aims to accelerate the completion of the robotics technology dedicated to t-shirt production, initiate the scaling of production capacity, and strengthen R&D.

The investment from BESTSELLER’s innovation and investment platform, Invest FWD, follows a series of other investments made by BESTSELLER in recent years. In total, BESTSELLER has invested more than DKK 200 million in innovative companies that aim to create the materials of the future and contribute to breakthroughs that can assist in the transformation of the fashion industry.

A significant criterion for the investments is that they not only meet BESTSELLER’s needs but also help push the entire fashion industry forward.

“When it comes to transforming the fashion industry, it is necessary with a diversified focus. The fashion industry is still linear, so it is crucial for us to also invest in innovative recycling technologies that can transform the clothes we wear today into new garments for the next generation. We have been doing this for several years, and we will intensify these efforts going forward,” says Dorte Rye Olsen, Head of Sustainability at BESTSELLER.