Fashion’s biggest operational challenge is no longer how to move faster, but how to work with clarity. For all the investment poured into customer experience, branding and speed to market over the past decade, one of the sector’s most commercially decisive functions, product development, still often runs on fractured systems, manual hand-offs and partial information.
That matters because product development sits at the point where creativity meets commercial reality. It is ...
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where collections are judged not only for design appeal, but for manufacturability, cost, compliance and scale. Yet in many businesses, the process still depends on spreadsheets, email threads and specialist software that do not talk to one another. The result is predictable: late changes, repeated sampling, avoidable waste and teams working from different versions of the truth.
Industry observers increasingly argue that the problem is not a shortage of technology. It is the absence of visibility.
Lectra has identified data silos, poor supply-chain visibility, weak demand forecasting and the exclusion of eco-design from early creative work as four major barriers to more sustainable product development. Its argument reflects a wider shift in the sector: brands can no longer treat product information as something to be assembled at the end of the process. Environmental and commercial decisions now need to be built into development from the outset.
That pressure is being sharpened by regulation and traceability demands. Material provenance, environmental impact and production data are becoming harder to reconstruct retrospectively, which is pushing fashion businesses towards more structured, materials-led workflows. In practical terms, that means connecting design, sourcing, development and manufacturing around a single source of truth, rather than relying on disconnected files and local expertise.
Avery Dennison’s research adds another layer to the picture. According to a report highlighted by Fashion Capital, a quarter of retailers in the UK and US have limited or no visibility of textile goods in factories and distribution centres, while only a small minority describe their supply chains as efficient and responsive. That lack of line of sight is not just an operational inconvenience; it feeds directly into delays, stock errors and lost margin.
The human dimension is just as important as the technical one. Fashion product development still depends heavily on highly specialised knowledge in patternmaking, grading, fit and construction, much of it carried by experienced individuals rather than codified in systems. At the same time, newer employees often expect collaborative, intuitive digital environments. Bridging that gap is becoming a management issue as much as a software one.
This is why many brands are rethinking digital transformation as an operating model rather than a procurement exercise. The aim is not simply to add more platforms, but to create an integrated environment in which CAD, PLM and collaboration tools support each other. When product, material and sourcing data are joined up, teams can resolve problems earlier, reduce interpretation errors and cut unnecessary sampling.
That matters because time pressure in fashion is only intensifying. FashionInsta.ai has pointed to development cycles that can stretch so long that products reach market after the trend has already moved on. For startups and established labels alike, the commercial risk is the same: a beautiful product is not enough if the business behind it cannot forecast, source, fit, price and deliver it properly.
Vogue’s recent reporting on the difficulty consumers now face in discovering where to shop also underlines how unforgiving the market has become. Brands need visibility internally to build collections efficiently, but they also need visibility externally to earn attention in a crowded, high-cost market dominated by major players.
Artificial intelligence is entering this picture, but its usefulness depends on what sits beneath it. Without clean, contextualised product data, AI simply accelerates confusion. With reliable information in place, it can help flag inconsistencies earlier, test scenarios, anticipate cost or timing issues and support better decisions through the development cycle. It is, in other words, an amplifier rather than a replacement for expertise.
The next competitive divide in fashion is therefore likely to be between companies that can see across product development and those that cannot. In an era of tighter margins, more demanding regulation and more volatile supply chains, visibility is becoming a form of resilience. The brands most likely to succeed will not be those that merely digitise old habits, but those that connect people, processes and data well enough to make better decisions, earlier.
Source: Noah Wire Services
