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.

Under the agreement, Retraced’s upstream traceability capabilities will be connected with Trimco Group’s labelling, packaging, RFID and data services. The combined set-up is intended to support product-level supply chain transparency from the earliest sourcing stages through to finished goods, giving brands a single, integrated workflow for supplier and product data.

A key element of the joint offer is on-product QR labelling. Brands will be able to apply QR codes generated by Trimco Group to items, allowing consumers, retailers and regulators to view structured details on origin and supply chain journey by scanning the label. Depending on how a client chooses to implement the solution, the information can be presented through Trimco Group’s Digital Manager with ProductDNA or through Retraced’s platform.

The partners said the approach is designed to support compliance with existing and incoming rules, including the EU’s Ecodesign for Sustainable Products Regulation, by ensuring that verified data is not only collected but also linked to specific products in a consistent format.

Trimco Group sustainability and compliance director Camilla Mjelde said the value of transparency depends on whether it can be delivered in a usable way. “Transparency only creates value when it is accessible, structured, and connected to the physical product. By integrating our ProductDNA platform and labelling expertise with Retraced’s upstream traceability technology, we are making it significantly easier for brands to turn complex supply chain data into clear, credible product information.”

Retraced said its platform already supports brands with supplier mapping, data administration, claim verification and collaboration across partner networks. By combining these functions with Trimco’s on-product labelling and identification tools, the companies say brands can validate data and share it more effectively with external stakeholders—while keeping compliance checks closer to real time.

Retraced’s policy compliance and partnerships director Roman Houlbreque said brands need traceability that does not remain trapped in back-end systems. “Supply chain data on its own doesn’t create value if it stays in disconnected systems. Brands need to bring that information to the product level in a way that is usable for compliance and communication. By partnering with Trimco Group, we are closing that gap — linking upstream data with on-product solutions in a way that is practical and scalable.”

By anchoring verified upstream information to QR-enabled labels, the partnership aims to make product-level supply chain transparency operational—supporting both regulatory disclosure and the wider industry push for clearer, more credible product claims.

The implementation will begin in Q2 2026 with The North Face, with additional VF brands expected to follow in phases. VF said the goal is to build a stronger operational base for end-to-end inventory visibility, improving day-to-day store execution while enabling more data-driven decisions on replenishment, fulfilment and customer service.

VF’s focus is on creating a unified view of stock, reducing the inconsistencies that can appear when inventory data sits in separate systems across stores, warehouses and vendor networks. With Nedap’s platform, the company expects higher stock accuracy and better product availability, which in turn supports more consistent omnichannel experiences across regions.

The initiative is not limited to store floors. VF said it is extending visibility deeper into distribution channels, a move it links to efforts to curb grey-market activity and reinforce brand protection measures. By tightening traceability and transparency through the network, the company expects to improve control over where product is and how it moves.

“Our consumers expect the same level of product availability and service whether they shop online, in-store or through any of our brand touchpoints,’ says Carsten Trenz, VP of Digital at VF Corporation. “Unified visibility across our operations allows us to deliver that consistency and build long term customer loyalty.”

Hope Waldron, VP of Supply Chain Strategy, VF Corporation adds: “Extending our RFID program beyond stores to include distribution centers and vendor partners at the source gives us greater transparency across our entire supply chain. That visibility improves our ability to ensure product availability, strengthen brand protection, and deliver a more consistent consumer experience.”

VF said it chose Nedap after reassessing its long-term needs following a pilot using a different solution. The company cited requirements around scalability, platform design and global support as key factors in selecting a system capable of handling the complexity of a multi-brand, multi-region rollout.

Nedap framed the deal as part of a broader shift toward unified commerce that depends on reliable inventory data. “In today’s retail landscape, unified commerce only works when brands can rely on one consistent source of truth for their inventory,” says Hilbert Dijkstra, Managing Director Retail at Nedap. “VF’s decision to invest in end-to-end visibility reflects a clear vision for the future: the ability to serve consumers seamlessly across any channel. Through ongoing innovation of our platform, we help VF operate with confidence, agility and precision.”

For VF, the partnership is positioned as an infrastructure upgrade—using RFID-driven, item-level tracking to deliver end-to-end inventory visibility not just within stores, but across distribution and supplier networks, where accuracy and speed increasingly determine both customer satisfaction and operational efficiency.

Gap said Inspectorio’s platform is being introduced to strengthen AI supply chain visibility, giving teams a clearer, more consistent view of what is happening across vendors and factories. The company plans to use the technology to support end-to-end product traceability, automate routine task execution and consolidate supply-chain data into a centralised system that can be accessed across functions.

Inspectorio chief executive Chirag Patel said the partnership reflects how major retailers are moving beyond isolated pilots toward scaled deployment of AI across complex supplier ecosystems. “Gap Inc. sets a new global standard for how leading retailers use AI to streamline supply chain performance and deliver for consumers. We’re proud to support the company’s vision with AI-powered technology that turns transparency into a competitive advantage and helps Gap Inc. make faster, smarter decisions across a complex global supplier network.”

The move sits within a series of AI announcements by Gap over recent months, as the group adds new tools across both operations and customer-facing commerce. Earlier this month, Gap introduced fit and shopping technologies through partnerships with Bold Metrics and Google. The company now offers personalised sizing recommendations using Bold Metrics’ Agent Sizing Protocol and has adopted Google’s Universal Commerce Protocol, which is designed to support conversational and agent-based purchasing journeys.

Gap’s supply chain AI efforts are also linked to its cloud strategy. In October last year, the retailer signed a multi-year agreement with Google Cloud to advance its technology roadmap, including AI applications across brands. In November, the group said it planned to introduce new AI-enabled features to its digital platforms in time for Cyber Monday 2025, aimed at improving style discovery and helping customers choose the right fit.

By bringing Inspectorio into its global sourcing network, Gap is signalling that AI supply chain visibility is becoming as central to its operating model as the AI features it is adding for shoppers—tying customer experience improvements back to upstream execution, traceability and quality control.

unspun is moving closer to commercial deployment of its automated manufacturing technology in the United States after securing support from major brands and industrial partners, including Walmart. The company said it has signed arrangements—structured as letters of support—with a group of backers as it advances plans to establish AI-enabled weaving facilities designed to produce garments domestically at scale.

The initiative centres on unspun’s AI-enabled 3D weaving platform, which the company says can manufacture garments directly from yarn in minutes. By consolidating what are traditionally dozens of cut-and-sew steps into a single automated workflow, unspun argues it can dramatically shorten lead times and make US-based apparel production more responsive to real demand.

Walmart is among the brands endorsing the plan. Supply chain partners Bethel Industries, Peckham and PDS Limited have also signed on to help enable the build-out, as unspun assesses what it needs to launch its first production sites. The company said initial output is “on the near-term horizon,” though it remains in the site-selection and planning phase.

CEO Arne Arens framed the project as an execution push rather than a theoretical experiment in reshoring. “We are not exploring whether domestic apparel manufacturing can work. We are building it,” explained Arne Arens, CEO at unspun. “Our clients are looking for a new production model because they see the economics: manufacturing closer to the customer, responding to demand within the same season, and creating skilled American jobs in the process.”

unspun has raised more than $50 million in venture capital to develop its 3D weaving approach. The company’s pitch to brands is that ultra-short production cycles can unlock a different retail operating model—one where products can be replenished within the same season, reducing the likelihood of overbuying and the markdown-heavy clean-up that often follows.

According to unspun, AI-enabled 3D weaving could improve gross margins by 400 to 500 basis points, largely by cutting excess inventory and limiting the write-offs that hit when demand forecasts miss. The promise is not only faster manufacturing, but a shift toward demand-led ordering that aligns production more tightly with what customers actually purchase.

Walmart’s endorsement builds on prior work with the company. The retailer previously collaborated with unspun on a pilot that produced workwear chinos using the 3D weaving system, an effort aligned with Walmart’s broader initiatives around renewable energy and emissions reduction within its supply chain.

Avisnash Bhasker, Vice President, Apparel Production Development at Walmart, said, “Our customers are proud to buy apparel made in America, and the demand keeps growing. We are excited about Unspun’s commitment and effort in helping rebuild domestic manufacturing capability that is faster, smarter, and designed for how customers actually shop.”

For now, unspun said it is continuing to evaluate potential US locations, infrastructure needs and workforce training programmes—practical requirements that will determine how quickly the first facilities can move from planning into operation.

The company is positioning the rollout as part of a broader rebuild of its digital foundation. Using unified data on Google Cloud alongside AI-ready architecture and governance controls, Gap said it is embedding AI across customer journeys and internal workflows. The objective, it said, is to make intelligence a core part of its operating model rather than a bolt-on feature.

Gap chief technology officer Sven Gerjets said the focus is practical outcomes rather than novelty. “We are not pursuing AI for novelty. These partnerships are about solving real customer problems – helping shoppers feel confident about fit and making it easier to complete a purchase. They also reflect the holistic AI strategy we’ve built to scale intelligence across the enterprise in a disciplined way that drives measurable value over time.”

Sizing is a persistent friction point in online apparel, and Gap is targeting that directly by integrating Bold Metrics’ predictive fit technology into its AI-driven shopping flows. Instead of directing customers to static size charts, the system is designed to deliver personalised size recommendations within live, conversational interactions—placing fit guidance inside the purchase path rather than outside it. Gap said the intent is to make sizing advice an always-on component of the transaction experience and a key enabler for conversational shopping.

Alongside fit personalisation, Gap is preparing for discovery and checkout to happen increasingly inside AI-powered environments. By supporting Google’s Universal Commerce Protocol, the retailer aims to keep product listings accurate and transaction-ready within services such as AI Mode in Google Search and the Gemini app. In practice, this could allow customers to complete purchases directly from these interfaces, shortening the distance between finding an item and paying for it.

Gap said the pairing of predictive sizing and AI-native commerce should “reduce friction” both in size selection and at checkout, improving confidence and conversion while meeting customers in the digital spaces where they are increasingly browsing.

Gap sells clothing, accessories and lifestyle products across Old Navy, Gap, Banana Republic and Athleta. The company reported total net sales of $15.4 billion for fiscal 2025, reaching the upper end of its outlook

The report suggests retailers expect tangible operational benefits to arrive quickly. Many anticipate “meaningful” impact within the next 12 to 18 months, while clearer and more scalable returns on investment are expected in roughly two years. That timeline, Retail Economics argues, creates a narrowing window: delaying capability building risks leaving retailers exposed as AI shifts from optional pilots to a baseline requirement for competing.

Today, the report estimates around 32% of marketing and ecommerce tasks are already exposed to being supported, enhanced or partly automated by AI. The impact spans core activities including personalisation, analytics, campaign execution and product discovery—areas where AI can compress time-to-decision and increase the precision of targeting. Data and analytics functions are among the most immediately exposed, the report notes, because AI systems excel at pattern recognition, forecasting and optimisation. Customer personalisation and experience execution are also highly exposed, reflecting retailers’ push for more relevant, real-time engagement across channels.

Brand and creative functions appear less automatable in direct terms, but still represent a meaningful share of exposed budgets because creative and brand-building remain large line items in marketing spend. In other words, even modest automation or augmentation in these areas could move substantial amounts of AI retail marketing spend.

The research covers four European regions—Benelux, DACH, the Nordics and the UK—and paints a market in transition. Almost all retailers (95%) have tested AI tools in marketing, with many beginning in 2023 through early generative AI and large language model experimentation. But the report finds a split between those still exploring what AI can do and those embedding it into daily operations.

Around one in four retailers remain in exploration or pilot-scaling phases, often constrained by data quality, limited in-house skills or uncertainty over governance. By contrast, 45.3% are described as “operational,” meaning AI is integrated across multiple workflows and is influencing day-to-day execution. Another quarter say AI is embedded at a strategic level, shaping planning, prioritisation and decision-making across functions rather than being limited to isolated use cases.

Despite growing adoption, commercial proof remains uneven. Only 5% of retailers say AI is currently delivering clear, scalable ROI—highlighting a gap between activity and outcomes. Still, most expect that to change quickly as tooling improves and organisations mature their data and operating models.

Skills and governance are cited as major barriers in the UK. The report notes that cultural hesitation and governance concerns constrain three quarters of UK retailers, while two thirds point to a lack of internal expertise as a core obstacle to deploying AI effectively. Data compliance concerns also slow progress, particularly as retailers balance personalisation ambitions with privacy and regulatory expectations.

Voyado’s chief product officer Felix Kruth argued that the next phase of value will come from autonomous systems operating behind the scenes. “What’s most exciting is that we’re still very early in the journey, and the AI we’re using today is likely the least impressive version we’ll ever see. This is still young technology. Generative AI has already delivered significant efficiency gains, but it’s agentic AI, built on the right data foundations, that will prove real commercial value.

“In the race to demonstrate AI progress, it is easy to focus on what is visible: new interfaces, chatbots, and features. But without the right data foundation and context, the impact simply does not materialise. The real value in retail is created by AI working in the background – prioritising the right customers, optimising decisions and ensuring the right thing happens at the right moment.”

Retail Economics CEO Richard Lim described the next two years as decisive. “The next two years represent an inflexion point as AI shifts from experimentation to competitive necessity. Retailers are on a journey, and while most have begun testing and deploying AI, few have reached a stage where it is delivering consistent commercial returns.

“As AI transforms retail tasks, it is reshaping how marketing and ecommerce spend is executed. The retailers that succeed will be those building the right data foundations, skills and operating models now, as AI becomes a core requirement for competing effectively in retail.”

As AI adoption accelerates, the report’s central argument is that AI retail marketing spend will increasingly be determined by data readiness and organisational capability, not just tool availability—separating retailers that industrialise AI from those that remain stuck in pilots.

The retailer is positioning the move as more than a set of disconnected experiments. While much of the industry is still piloting narrow AI features, Gap Inc. says it has reworked its digital backbone so AI can run through the entire customer journey. The foundation is built on unified Google Cloud data, an AI-ready architecture and governance standards designed to scale machine-driven decisioning across the business, rather than treating AI as a standalone initiative.

“We are not pursuing AI for novelty,” said Sven Gerjets, Chief Technology Officer, Gap Inc. “These partnerships are about solving real customer problems; helping shoppers feel confident about fit and making it easier to complete a purchase. They also reflect the holistic AI strategy we have built to scale intelligence across the enterprise in a disciplined way that drives measurable value over time.”

Sizing uncertainty remains one of e-commerce apparel’s most persistent conversion blockers, and Gap Inc. is targeting that pain point directly. Through its partnership with Bold Metrics, the company is embedding predictive fit guidance into AI-driven shopping interactions. Instead of sending customers to static size charts, the system is intended to deliver personalised size suggestions within conversational flows—right when shoppers are close to purchasing. By making fit intelligence part of the purchase path, Gap Inc. is effectively treating sizing as a core capability for agentic commerce, where the “shopping assistant” experience guides decision-making in real time.

At the same time, the company is preparing for a shift in how consumers discover and buy products as search behaviour migrates toward AI answer engines. Gap Inc. said its support for Google’s Universal Commerce Protocol is designed to ensure its product data appears correctly and remains ready for transaction in conversational AI contexts, allowing shoppers to move from product discovery to checkout with fewer handoffs.

Under UCP, Gap Inc. expects to make its catalogue purchasable across AI-native environments, including Google Search’s AI Mode and the Gemini app. The aim is to let customers complete purchases while interacting with AI tools, rather than forcing them to revert to traditional browsing and checkout patterns. In practical terms, it is an attempt to keep Gap’s brands present and “buyable” wherever the next generation of shopping journeys occurs—an approach that aligns with the company’s broader push to operationalise agentic commerce rather than treat it as a future concept.

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.