As part of the agreement, Jack Technology will deploy Siemens’ Intelligence Center X industrial AI orchestration platform, beginning with the Mendix low-code development environment and Designcenter™ engineering software from the Siemens Xcelerator portfolio. These digital tools will help simplify product development, accelerate software deployment, and support the design and integration of humanoid robots within apparel manufacturing operations.

The partnership marks an important milestone in Jack Technology’s digital transformation strategy. Operating in more than 160 countries, the company is responding to growing demand for faster production, increased product customisation, and more flexible manufacturing by integrating AI-enabled systems into conventional sewing workshops.

The new technology platform will enable Jack Technology to rapidly develop and deploy manufacturing applications while supporting advanced engineering, digital simulation, and production planning for complex systems, including humanoid robots. Siemens will also provide specialised technical training to assist in building a dedicated digital platform tailored to the sewing equipment industry.

By introducing AI-powered apparel manufacturing solutions, Jack Technology expects to improve product quality, streamline engineering processes, optimise factory operations, and reduce manufacturing costs. The company estimates the collaboration could increase overall production efficiency by up to 30%, strengthening its competitiveness in the global apparel machinery market.

Commenting on the collaboration, Hu Wenhai, President of New Product Development at Jack Technology, said; “Collaborating with Siemens marks a critical step in advancing our digital strategy. 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 initiative also reflects a broader shift within the apparel sector, where manufacturers are increasingly adopting industrial AI, digital twins, low-code software, and intelligent automation to create more responsive and data-driven production environments. Humanoid robots are expected to complement these technologies by performing repetitive and labour-intensive tasks while working alongside human operators.

According to Siemens, combining AI-driven software with advanced product engineering will enable manufacturers to move more efficiently from product concept to commercial production while improving innovation across the textile value chain. The collaboration is expected to strengthen Jack Technology’s ability to deliver intelligent manufacturing solutions for apparel producers worldwide.

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

Leo Liang, Senior Vice President and Managing Director for Greater China at Siemens Digital Industries Software, added; “This collaboration highlights how digital technologies are reshaping apparel manufacturing. 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.”

As apparel manufacturers continue investing in smart factories, the partnership between Jack Technology and Siemens demonstrates how AI, digital engineering, and humanoid robotics are becoming central to the industry’s long-term transformation. By combining software-driven innovation with intelligent automation, both companies aim to support more agile, efficient, and resilient apparel manufacturing worldwide.

A New Era for Onshore Production

This nationally significant initiative is supported by Epson, serving as the strategic print and projection partner, along with Investment NSW through its Fostering Innovation Sponsorship Program. The proposed facility is envisioned as a manufacturing incubator designed for the modern era, allowing brands to conduct research and development while managing small-batch fashion manufacturing. By establishing a technology-enabled environment, the project aims to make onshore production a viable and cost-effective reality for local designers once again.

Technical Infrastructure and Sovereign Manufacturing

The feasibility research will investigate a comprehensive Cut, Make, and Trim (CMT) facility. This blueprint will incorporate digital pattern making, CNC cutting, automated production lines, and digital textile printing. A research consortium including RMIT, Swinburne, and UTS is leading the study, pooling expertise to address the future requirements of sovereign manufacturing.

Epson is contributing its Monna Lisa digital textile printing technology to the project, which utilizes precision water-based pigment inks. This hardware is essential for on-demand manufacturing, helping brands reduce waste and energy consumption while responding rapidly to market trends. Craig Heckenberg, Managing Director of Epson Australia and New Zealand, noted that this technology is transforming garment production.

Similarly, Sam Delgos, General Manager of the AFC, emphasized that the Clothing Smart Factory Study provides a blueprint for long-term investment in skills and advanced manufacturing. This vision is shared by the NSW Government, as Minister Anoulack Chanthivong highlighted the potential for creating skilled local jobs. The study is expected to conclude in late 2026, aiming to establish a scalable model for a national network of advanced manufacturing hubs.

Unlike traditional industrial robots designed for repetitive, fixed tasks, humanoid robots are engineered to operate in workspaces originally built for people. Their human-like structure enables them to perform a wider variety of production activities with minimal changes to existing factory layouts, making them an attractive solution for the labour-intensive apparel sector.

Garment manufacturing has historically remained one of the most difficult industries to automate because fabrics are soft, flexible, and constantly change shape during handling. Activities such as separating fabric layers, aligning garment panels, and feeding materials into sewing machines require a level of precision and adaptability that conventional automation has struggled to achieve.

Recent developments in embodied AI, advanced sensors, and machine learning are helping overcome these challenges. New-generation humanoid robots are now capable of identifying fabrics, grasping delicate materials, positioning textile components, and preparing them for sewing with significantly higher levels of accuracy than earlier robotic systems.

Several technology companies have already begun deploying humanoid robots in industrial environments to perform repetitive and physically demanding tasks. While early applications have focused on logistics and material handling, apparel manufacturing is emerging as one of the next major sectors for commercial deployment.

Industry experts believe humanoid robotics in apparel manufacturing could help address persistent labour shortages while improving production efficiency. Rather than replacing skilled workers immediately, current deployments are largely intended to complement human operators by taking over repetitive processes and allowing employees to focus on quality control, complex sewing operations, and production supervision.

China has become one of the first countries to test humanoid robots directly on garment production lines. In one large-scale initiative, robots are being evaluated for tasks such as separating fabric pieces, laying materials flat, aligning components, and preparing garments for stitching. Developers report high success rates in both fabric handling and sewing quality, highlighting the growing commercial potential of robotic apparel production.

Researchers also point out that the combination of digital twins, AI-driven production planning, and collaborative robotics is making automated garment manufacturing more practical. These technologies enable factories to validate workflows before deployment, reduce commissioning time, improve operational efficiency, and simplify integration with existing sewing equipment.

Despite this progress, experts acknowledge that humanoid robots are unlikely to replace human garment workers entirely in the near future. Complex sewing techniques, creative craftsmanship, fabric inspection, and decision-making tasks continue to require human expertise, while robots are expected to take on repetitive, physically demanding, and standardized operations.

As investment in robotics and AI continues to accelerate, apparel manufacturers are expected to adopt more flexible automation systems capable of improving productivity without requiring major factory redesigns. The evolution of humanoid robotics in apparel manufacturing is therefore likely to reshape production models by combining human skills with intelligent automation, creating more resilient and efficient textile supply chains in the years ahead.

The collaborative initiative combines textile recycling with AI to tackle one of the sector’s biggest challenges—finding sustainable uses for donated clothing that cannot be resold or reused. The project focuses on converting unusable textiles into valuable products while also modernizing donation sorting through artificial intelligence.

According to Western University, more than 43 million pounds of donated goods were received across Ontario’s Great Lakes region during the last fiscal year. While many items are resold or repurposed, a significant volume remains unsuitable for reuse, leaving organizations with limited alternatives other than disposal in landfills.

To address this growing issue, Goodwill joined forces with researchers from Western University and Fanshawe College, combining expertise in sustainable materials, recycling technologies, and intelligent automation.

Scientists from Western’s Institute for Chemicals and Fuels from Alternative Resources (ICFAR), working alongside Fanshawe’s Centre for Research and Innovation, are using pyrolysis to transform discarded clothing into biochar. The thermal conversion process breaks down mixed-fibre textiles—including materials containing dyes and chemical additives—into a carbon-rich product suitable for environmental applications.

Biochar is widely recognized for improving soil quality by enhancing nutrient retention, increasing water-holding capacity, and supporting beneficial microbial activity. Preliminary findings suggest that biochar produced from discarded textiles may promote healthier plant growth compared with untreated soil.

Explaining the challenge of textile recycling, Heejin Lee, a postdoctoral research associate at Fanshawe College collaborating with ICFAR, said:

“Textile waste is often difficult to recycle using conventional methods because it’s a mixture of different kinds of fibres with dyes and additives. That means many textiles end up in landfills.”

Researchers are also exploring enzyme-based recycling techniques capable of breaking down natural fibres such as cotton and hemp into sugars that could serve as raw materials for future industrial processes. The team noted that a large proportion of donated textiles across North America still ends up in landfill once resale opportunities have been exhausted.

Alongside its textile recycling with AI initiative, Western University is developing an intelligent sorting system designed to improve how donated goods are processed. At present, most items are inspected and sorted manually, making the process both labour-intensive and susceptible to reusable products being discarded.

The AI-powered platform uses smart cameras positioned along a conveyor belt to identify, classify, and monitor donated items in real time. The technology is intended to assist workers by flagging reusable products and reducing the likelihood of valuable materials being mistakenly sent to landfill.

Discussing the system, Professor Min Xia from Western University’s Faculty of Engineering said:

“Cameras will help to identify the items, and if they miss them, the system will give the workers a signal to easily identify them. Then again at the end of the conveyor, there will be another camera to guarantee nothing ends up in the landfill that shouldn’t.”

Since the textile recycling project began more than four years ago, the partnership has successfully diverted approximately 5,000 pounds of clothing waste from landfill. The research team has also applied for federal funding to expand the programme and develop solutions for the remaining volume of unsellable textiles.

The AI-enabled sorting system is expected to become operational within the next year, with researchers continuing to refine both the automation platform and textile recycling technologies to further reduce landfill waste and strengthen circular economy practices.

Engineering Shape-Shifting Fabric

By employing a technique known as plating, the research team utilized elastic yarns to dictate the material’s behavior. The internal stresses generated during the manufacturing process allow the programmable knitted textiles to maintain various stable shapes without the need for constant external force. This multistability functions much like a mechanical switch, where the fabric snaps between configurations upon the application of pressure, offering tactile feedback without complex mechanical assemblies.

Integrating Electrical Functionality into Textiles

The researchers enhanced these fabrics by embedding conductive yarns directly into the structure. This integration allows the material to act as a soft switch or motion sensor. Because the sensing capability is built into the fabric itself, it eliminates the need for rigid components, making these smart fabrics ideal for wearable technology.

Practical Applications and Prototypes

The team demonstrated several functional prototypes to validate the technology:

• A multistable shell that manages LED lighting states based on shape changes.
• Wearable sensors designed to track movement when positioned over joints like the knee or elbow.
• A reconfigurable lampshade featuring multiple switches that control various light outputs.

Scalability and Future Implications

A primary benefit of this research is its compatibility with existing machinery. Since the materials are produced using standard double-bed weft-knitting equipment, the process is highly scalable. These soft sensors and smart fabrics represent a significant step in the evolution of mechanical intelligence, providing a foundation for future innovations in health monitoring, interactive apparel, and responsive architectural designs.

The warning arrives at a sensitive moment for the country, which is preparing for its graduation from least developed country status. The apparel industry has long served as one of its largest employers and a key driver of export earnings, making large-scale workplace disruption a matter of significant economic and social impact.

Labour Market Indicators and Education Disconnect

The broader labor market is already showing signs of strain. National employment shrank by approximately 1.3 million jobs in 2024, with female workers accounting for nearly nine out of every ten positions lost. While manufacturing output has continued to rise, total employment within the sector has remained virtually frozen at around 8.1 million workers.

To analyze potential future outcomes, researchers examined 27 domestic and global indicators to model labor market scenarios through 2035. Across varying assumptions, all scenarios pointed to consistent themes: rapid digitisation, escalating demand for higher-value skills, persistent skill shortages, vulnerability to international economic shocks, and the need for stronger institutional capacity to navigate the transition.

Vocational Education Deficit

The research highlights a growing gap between current educational output and industrial requirements. Technical and vocational education continues to attract under one-fifth of secondary-level students, while public expenditure on human capital development remains modest compared to the speed of technological change. Without improved workforce preparation, many workers may remain ill-equipped for the positions created by advanced manufacturing.

Balancing Modernisation with Worker Safeguards

The challenge extends beyond factory floors. Although technology is projected to generate millions of digital roles globally, it will simultaneously displace millions of traditional roles. In Bangladesh apparel automation, displaced workers face the risk of shifting into low-paying, informal, or gig-based positions offering minimal social protection.

However, the report notes that automation does not need to be viewed purely as a threat. Supported by modern vocational training, continuous reskilling programs, robust labor market data systems, workforce development incentives, and protection for vulnerable groups, technology could boost productivity while maintaining inclusivity.

Industry Initiatives and Future Outlook

Taking steps toward technological integration, Ha-Meem Group, a prominent domestic garment producer, signed a Memorandum of Understanding (MoU) with a robotics and automation firm. The partnership aims to accelerate Industry 4.0 adoption and prepare for AI and automation through three core pillars: workforce development, industrial automation, and strengthened industry-academia collaboration.

Ultimately, Bangladesh apparel automation functions as a double-edged sword. While it offers potential for greater operational efficiency and long-term industrial competitiveness, the absence of prompt policy measures and sustained workforce training could risk unravelling a major employment foundation.

The funding round was led by eBay Ventures and the Google AI Futures Fund, providing the company with additional resources to enhance its AI-driven styling tools, improve resale integration, and expand its retail data platform.

Since launching in 2021, Whering has attracted more than 10 million users, reflecting growing consumer interest in digital wardrobe management and more mindful approaches to clothing ownership. The platform believes this trend signals a shift away from excessive fashion consumption toward smarter and longer-lasting use of existing garments.

Founder and CEO Bianca Rangecroft said: “We have access to such a vast amount of data that hasn’t existed before – not just what people buy, but what people actually wear, what they wear it with and how it makes them feel. For us, what’s next is helping people not just when they get dressed in the morning, but every time they interact with clothes, whether that’s buying, selling or styling – and we have the data to do that in an incredibly personalised way.”

The company’s next phase of innovation will focus on expanding its AI wardrobe platform with intelligent styling features that deliver personalized outfit recommendations based on factors such as weather conditions, daily activities, personal mood, and special occasions. These capabilities are intended to make wardrobe planning more convenient while helping users maximize the value of the clothes they already own.

Whering also plans to introduce upgraded image-processing technology that automatically enhances user-uploaded wardrobe photos to retailer-quality standards. Additional improvements will allow users to extract clothing items directly from images stored in their camera gallery and organize them within the app more efficiently. A virtual try-on function is also under development, enabling users to visualize outfits digitally before wearing or purchasing garments.

These enhancements are designed to provide users with greater visibility into their personal wardrobes, making it easier to coordinate outfits, avoid unnecessary purchases, and extend the lifespan of existing clothing.

Beyond AI-driven styling, the company is investing heavily in strengthening its circular fashion strategy. Future updates will integrate broader resale capabilities into the platform, allowing users to seamlessly manage wearing, re-wearing, and reselling garments within a single digital ecosystem. The objective is to encourage clothing to remain in circulation for longer, reducing waste while supporting more responsible consumption patterns.

David Benjamin of Google AI Futures Fund said: “Whering’s vision for a digital, AI-powered wardrobe that prioritises both personal style and the planet deeply resonates with us. We are thrilled to welcome them to Google AI Futures Fund and to support their journey in redefining the future of sustainable fashion through cutting-edge technology.”

Since its launch, Whering has consistently focused on helping consumers make more deliberate clothing decisions. According to the company, 84% of users report wearing garments more frequently, while nearly 70% say they have reduced purchases of fast fashion after adopting the platform.

The latest investment will also support new partnerships and technology initiatives that further strengthen the company’s commitment to building a more circular fashion economy through digital innovation and data-driven solutions.

eBay vice president and fashion global head Alexis Hoopes said: “At eBay we not only power recommerce, we empower the infrastructure that keeps fashion in rotation longer. This is exactly what Whering brings to industry and closets everywhere. The fashion industry’s buy, use, dispose model has been long overdue disruption, and with over 10 million users and digitised wardrobes, Whering is well positioned to be the catalyst.”

As demand grows for intelligent wardrobe management and sustainable retail solutions, the AI wardrobe platform is positioning itself at the intersection of artificial intelligence, resale commerce, and circular fashion. By combining personalized styling with resale and wardrobe analytics, Whering aims to help consumers make better purchasing decisions while supporting a more sustainable future for the fashion industry.

Advancing Supply Chain Transparency

The initiative focuses on tracking every stage of the manufacturing process, from the initial fiber sourcing to the final garment production. This move by the company is designed to meet increasing global demands for textile transparency and to comply with evolving international regulations regarding environmental and social governance.

Integration of Digital Technologies

The Shinwon Traceability System leverages blockchain fashion protocols to create an immutable record of material origins. This ensures that every component used in their collections is accounted for, providing verifiable data to stakeholders and consumers. Furthermore, the application of AI supply chain tools allows for real-time monitoring and data analysis, enhancing the efficiency of raw material tracking throughout the global network.

The company is collaborating with various industry partners to standardize these digital records. The implementation of the Shinwon traceability program is a response to the growing necessity for textile transparency in the global market. By prioritizing raw material tracking, Shinwon is positioning its operations to align with future digital product passport requirements.

Navigating the Regulatory Landscape of Artificial Intelligence

As production methods evolve, the U.S. regulatory environment is attempting to transition from a patchwork of state-level mandates to a unified federal baseline. This shift has created a notable tension between innovation and oversight. The White House National Policy Framework for Artificial Intelligence, released in March, outlines a “light-touch” approach designed to favor national economic competitiveness and industry-led standards. This framework intends to respect federalism while maintaining the state’s power to protect consumers, prevent fraud, and ensure child safety.

On the legislative front, there is a significant push to codify these standards through the Trump America AI Act. However, various states are asserting their own authority. The Colorado AI Act, specifically the SB 26-189 model, has gained attention for its focus on transparent notifications regarding the role of AI in textile production within employment and corporate decision-making. Sourcing executives now face the dual challenge of leveraging these new tools to combat shipping disruptions while navigating this volatile compliance environment.

Operational Shifts and the Rise of Autonomous Systems

The footprint of AI in sourcing continues to expand, moving from a secondary differentiator to a baseline necessity across global supply chains. One of the most significant applications is predictive procurement. By processing massive datasets, technology allows decision-makers to track minimum order quantities, geographic logistics, and supplier certifications in real-time. This agility enables companies to pivot to alternative mills instantly if port disruptions or tariff spikes occur.

Advancements on the Factory Floor

On the manufacturing floor, computer vision systems are currently slashing factory batch rejections by up to 60 percent. Additionally, AI is being deployed to screen global databases for eco-friendly fibers and track ESG compliance certificates to meet transparency mandates. Industry leaders are also preparing for the mass scaling of autonomous production scheduling. Static spreadsheets are being phased out in favor of real-time engines that automatically restructure mill schedules when yarn lots fail or shipping routes change.

The deployment of digital twins—virtual factory replicas—is also expected to move out of pilot phases and onto mainstream cutting floors. Analysts suggest this will drive a 10 to 15 percent reduction in raw material waste. As these systems become more prevalent, apparel brands must also ensure that consumer-facing tools, such as virtual try-on software, align with strengthening data collection and child safety laws.

Future Investment and Market Outlook

The transition toward a digital-first industry is backed by significant capital. Market data indicates that investments in AI by textile companies are projected to jump from the current $2.6 billion to approximately $12 billion by 2029. This massive scaling of textile manufacturing technology will define the next era of the sector.

Ultimately, the intersection of cutting-edge innovation and evolving regulation will determine which brands succeed. Sourcing executives must balance the immense efficiency gains of production automation against a shifting policy landscape. Those who successfully integrate AI in textile production while remaining agile amid regulatory changes will be best positioned to lead the multi-billion-dollar digital overhaul already underway.

That inconsistency has made online size and fit one of fashion e-commerce’s most expensive operational headaches. Across European online fashion, return rates can reach about 50%, with size and fit responsible for as much as half of all returns. In denim, the challenge is even more pronounced, with returns reported as high as 65% in some cases.

Zalando says it has been tackling the issue at scale since 2018 and claims it is the only e-commerce platform offering a broad suite of size-and-fit solutions across its ecosystem. The company now has an in-house team of around 80 specialists working across fashion, machine learning, AI, computer vision and 3D technologies. Zalando reports that in 2025 its tools prevented 8% of size-related returns overall, reinforcing the company’s view that reducing misfit is achievable but requires multiple layers of solutions rather than a single fix.

Rebuilding the feedback loop that mass production removed

Zalando frames the roots of the problem as structural. Tailoring once relied on direct dialogue between maker and wearer; industrialisation made clothing widely available, but removed much of that feedback. Digital commerce, Zalando argues, provides a chance to restore the loop using data: product measurements and cut, customer preferences, body dimensions and fit feedback can be combined to generate more accurate guidance and to identify where sizing is repeatedly failing.

Its strategy is built in stages. First, Zalando developed systems to interpret how garments run across brands and categories, using machine learning to analyse product data, return behaviour and customer feedback. Second, it layered in customer-specific signals body measurements and fit preferences to personalise recommendations. Third, it aimed to bring the experience closer to an in-store try-on through its Virtual Fitting Room.

Foundational tools: flags, recommendations and fitter insight

Zalando’s earliest work focused on building a baseline understanding of product fit across a diverse assortment. That foundation powers the “size flags” and “size recommendations” shoppers see on product pages today. Flags indicate whether an item typically runs small or large; recommendations suggest the most likely size to fit a specific customer.

The system draws on multiple inputs: brand-provided information, purchase and return patterns, customer fit feedback and internal “fitting models” trained specialists who physically try on items to spot sizing issues before they become widespread. Zalando says these foundational solutions now cover roughly 70% of its assortment and that the insights can also be shared with brands, helping them see whether certain fits or collections drive repeat returns.

Personalisation: from size history to body measurements

Product-level guidance becomes sharper when the platform understands the shopper. Zalando’s Size Profile lets customers indicate which brands and items fit well, including products bought outside Zalando, and to rate fit on past orders. Those signals help tailor browsing, filtering and recommendations toward items more likely to match a customer’s typical sizing and preferred fit.

The Body Measurement tool adds another layer by capturing measurements from two photos or a short video recorded on a phone. Zalando says more than 1.5 million customers have used the feature so far, helping build what it describes as Europe’s largest anonymised dataset of fashion body measurements. These insights are also incorporated into size charts so shoppers can compare their own measurements with product dimensions and choose sizes aligned with how they want items to sit looser, slimmer or more oversized.

Virtual Fitting Room: bringing fit into view

Once product and customer data are connected, Zalando uses that information to create a 3D avatar for its Virtual Fitting Room, allowing shoppers to visualise how different sizes might look on their body shape.

The company highlights jeans as a prime use case: a single style can come in more than 30 size combinations, and differences in waist, hips, leg shape and length make denim hard to buy online with confidence. About 21% of Zalando customers purchase jeans annually, yet the category is also among the worst for size-related returns.

Zalando says pilots of the Virtual Fitting Room reduced returns by up to 40%. After successful tests, it is moving from pilots to a permanent, scaled experience, with the goal of making it available to all customers and expanding the eligible assortment.

Fewer returns, better insight, lower impact

Zalando’s immediate objective is to reduce avoidable returns by helping shoppers choose correctly the first time cutting unnecessary shipments and associated emissions. The longer-term ambition is broader: using fit intelligence to show where products repeatedly fail, guide brands toward better sizing decisions and gradually reduce friction across the industry.

“Our ambition is to help online shoppers find the right fit for the first time wherever and however they shop. We are building towards a digital tailor for online fashion that combines our understanding of products with each customer’s unique measurements and fit preferences. This can help people find what fits them best and, over time, give brands better insight into the products their customers need and want.” Pelin Anli Bedirhanoglu, Director of Size & Fit at Zalando.

As online size and fit becomes central to customer satisfaction, logistics costs and sustainability goals, Zalando is betting that technology combined with human fitting expertise can turn one of e-commerce fashion’s biggest pain points into a competitive advantage.