Making structure effective: AI as a cost lever in the fashion industry

However high the cost pressure may be, compromising on quality is not an option in the fashion industry. A reliable planning rhythm, a robust data foundation and practical AI building blocks reduce friction costs along the fashion value chain. Thoughtful governance is key during implementation to ensure that savings potential is realised in practice.

Several trends are driving up costs in the fashion industry. Among raw materials, wool is becoming more expensive whilst cotton remains stagnant. In logistics, the "Red Sea effect" is keeping spot rates and planning volatile. On the demand side, households in certain segments are switching to cheaper alternatives through outlets, off-price retailers, resale or "dupes." In Asia, more mature Asia-Pacific (APAC) markets are gaining importance alongside China. In established sales regions, the Silver Generation (over 50s) accounts for a disproportionate share of spending growth. Product ranges, pricing and messaging must therefore be more finely differentiated. Without a uniform "average consumer" as a reference point, planning becomes more complex.

Additional costs from rising material prices, uncertain logistic scenarios and stricter compliance requirements cannot simply be passed on to increasingly price-sensitive consumers. Those who cut corners on the quality of "invisible" components such as lining fabrics or hardware risk their brand promise, particularly in the premium sector. Cost management does not emerge from individual measures but from end-to-end digitalisation of the entire process chain.

From patchwork to data thread

Demographically, the fashion industry sees Silver Agers meeting young, digital managers who must address reflexes such as "We've always done it this way" or "Can I download this as a .csv?" when implementing digitalisation initiatives. In practice, this "Excel reflex" often keeps parallel efforts alive, decoupling decisions and confining best practices for using digital tools to silos. For example, some fashion houses use their PLM system as a "collection point" for sketches and data rather than as an active tool for orchestrating design processes.

Whilst the automation level of many fast fashion brands approaches that of the automotive industry, many premium and luxury labels manage dense networks of small suppliers with a high proportion of artisanal production. Where volumes fluctuate, medium to long-term framework agreements with clear service levels provide planning security. At the same time, a consolidated supplier base reduces complexity and coordination effort without compromising artisanal quality.

The entire process chain benefits from a continuous data thread. Information on material origin, process and location stamps, as well as complete bills of material (BOM) with approved alternatives feed into forecasting, demand and capacity planning, allocation and procurement. This extends to customer communication, such as Digital Product Passport (DPP) information at the point-of-sale. Pragmatic gradations are possible regarding the level of data transparency. For standard goods, batch tracking is often sufficient. For high-value, regulation-relevant or reputation-sensitive items, individual item tracking is advisable.

For this data to have an impact, all functions need a common language: unique IDs, clear taxonomies and well-maintained master data. Only then can artificial intelligence (AI) deliver practical added value, primarily through cost savings.

AI is no end in itself: five applications with concrete savings potential

For genuine AI acceptance, AI must not remain an IT topic. Every use case needs a "sponsor" from the relevant department. An inventory or logistics manager who incorporates a use case into their own roadmap experiences the added value of AI beyond buzzwords.

Which use cases have proven successful in the fashion industry? Fundamentally, AI most effectively reduces friction costs in processes that combine repetition, data volume and decision pressure.

AI automation can save four to six hours per week in recurring market and raw material analyses. Since this is based on public reports and statistics, the risk to proprietary data remains low.
Fashion companies manage terabytes of images from shoots, sketches and visual merchandising. Traditional database searches often depend on historical tagging discipline, making them both time-consuming and error-prone. Multimodal AI models, however, recognise objects, shapes and colours down to precise colour codes, suggest consistent tags and find related motifs contextually. Research times decrease and existing material is reused more frequently.
Every update to a major retailer's rulebook can result in hours of searching. Conversational large language models (LLMs) provide context-related answers for practical application, such as refurbishment rules, and flag relevant updates. This shortens onboarding time and stabilises processes.
Consolidations, payment and invoice reconciliations, and external market data analyses tie up many hours in the back office. Agent-based pipelines handle retrieval, cleansing and standard calculations with whitelists for sources. Experience from fashion finance shows approximately half a working day of efficiency gains per week, particularly at month and quarter ends.
In well-documented residual and semi-finished inventories, AI can derive production-ready variants based on approved BOM alternatives to fully utilise existing stock in the interest of cost efficiency and sustainability.

A safe starting point for AI implementation is workflows that process external information. Consistent review by human employees ("human in the loop") prevents any AI hallucinations from propagating into subsequent process steps.

No transformation without leadership

A digitalisation project such as AI implementation only succeeds with professional change management that engages corporate culture as much as IT. Such modernisations affect far more than design and operations. Procurement, logistics, retail, finance and compliance feel the effects, often months later without being prepared. Early onboarding reduces resistance: stakeholders are informed from the outset, impacts are made transparent and feedback loops are established.

Integration of the project into existing committees is also crucial for buy-in. If a company maintains established jour fixes or steering committees, transformation components should ideally flow into these. When innovations do not result in meeting inflation, stakeholders tend to respond more openly.

Tools with high usability, clean knowledge management and targeted upskilling ensure acceptance and correct usage at the operational level, from dispatchers to store teams. Training and clear role descriptions support teams where long-standing workshop knowledge and digital responsibility come together.

Continuous data cycle instead of crystal ball

The impact of digitalisation measures is reflected in sell-through rates and out-of-stock rates by location, inventory turns and the proportion of aged stock, forecast bias and mean absolute percentage error (MAPE) by category. Delivery reliability and variance by supplier reflect governance quality in procurement and logistics. In content and visual merchandising processes, search and throughput times as well as reuse rates set the pace. At the data level, DPP completeness and master data quality count.

When governance and IT pull together, data speaks a common language, AI specifically addresses friction costs and a stable governance mode emerges. Systems guide the process and teams decide based on consistent signals. Digitalisation and AI then transform from cost drivers into concrete levers for cost efficiency.

This article was translated to English using an AI tool.

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