No matter how high the cost pressure may be, saving on quality is out of the question in the fashion industry. A reliable planning rhythm, a reliable data foundation and practical AI modules reduce friction costs along the fashion value chain. When it comes to introduction, well-thought-out control is the key to ensuring that the potential savings are implemented in practice.
Several trends are driving up costs in the fashion industry. Among the raw materials, wool is becoming more expensive and cotton is stalling. In logistics, the “Red Sea effect” keeps spot rates and therefore planning volatile. On the demand side, households in sub-segments are switching to cheaper alternatives through outlets, off-prices, resales or “dupes.” In Asia, more mature Asia-Pacific (APAC) markets are gaining importance alongside China. In the established sales regions, the Silver Generation (over 50s) accounts for a disproportionate share of the growth in spending. Assortments, pricing and messaging must therefore be differentiated more precisely; Without the uniform “normal consumer” as a fixed point, planning becomes more complex.
Additional costs resulting from rising material prices, uncertain logistics and stricter documentation requirements cannot easily be passed on to – increasingly price-sensitive – consumers. And anyone who skimps on the quality of “invisible” components such as lining materials or fittings risks their brand promise, especially in the premium sector. Cost management does not arise from individual measures, but from the end-to-end digitalization of the entire process chain.
About the author
Giovanni Cara is an expert in fashion, retail and consumer goods at BIP Group. He heads the fashion department at the consulting company. With more than 15 years of experience in business and technology transformation programs, he ensures the effective implementation of innovation trends in the day-to-day business of his clients. He holds a degree in industrial engineering and an international MBA.
From patchwork to data strand
Demographically, Silver Agers in the fashion industry meet young, digital managers who, when faced with digitalization initiatives, have reflexes such as “We have always done it this way” or “Can I also download it as a .csv?” need to address. In practice, this “Excel reflex” often keeps secondary logic alive that decouples decisions and keeps best practices in using digital tools in silos. For example, some fashion houses use their PLM system as a “repository” for sketches and data – not as an active tool for orchestrating design processes.
While the level of automation of many fast fashion brands is close to that of the automotive industry, many premium and luxury labels control dense networks of small suppliers with a high proportion of manufacturing. Where volumes fluctuate, medium to long-term framework agreements with clear service levels create planning security. At the same time, a consolidated supplier base reduces complexity and coordination effort – without compromising on quality craftsmanship.
The entire process chain benefits from a consistent data stream. Information on material origin, process and location stamps as well as complete bill of materials (BOM) with approved alternatives feed forecasting, demand and capacity planning, allocation and procurement – right up to customer communication, for example via information about the digital product pass (DPP) at the point of sale. Pragmatic gradations are possible when it comes to the level of detail of data transparency. For standard goods, batch tracking is often sufficient; Individual part tracking is ideal for high-priced, regulatory-relevant or reputation-sensitive items.
In order for this data to be effective, 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 – especially through cost savings.
AI is not an end in itself: 5 applications with concrete savings potential
For true AI acceptance, AI must not remain an IT issue. Every use case needs a “sponsor” from the department. An inventory or logistics manager who incorporates a use case into her own roadmap experiences the added value of AI beyond buzzwords.
Which use cases have proven successful in the fashion industry? In principle, AI is most effective at reducing friction costs in processes that combine repetition, data abundance and pressure to make decisions.
Four to six hours per week can be saved in recurring market and raw material analyzes with the help of AI automation. Since we work with public reports and statistics, the risk to our own data remains low.
Fashion companies manage terabytes of images from shoots, sketches and visual merchandising. The classic database search often depends on the historical tagging discipline – and is therefore as time-consuming as it is error-prone. Multimodal AI models, on the other hand, recognize objects, shapes and colors down to precise color codes, suggest consistent tags and find related motifs depending on the context. Research times are reduced and existing material is reused more frequently.
Every update to a major retailer’s rulebook can result in hours of search effort. Conversational Large Language Models (LLMs) provide context-related answers for practice – for example with conversion rules – and highlight relevant updates. This shortens the training period and stabilizes the processes.
Consolidations, payment and invoice reconciliations as well as external market data analyzes tie up many hours in the back office. Agent-based pipelines handle retrieval, cleansing, and standard calculations with source whitelists. Experience from fashion finance shows an efficiency gain of around half a working day per week, especially at the change of month and quarter.
In clearly documented remaining and semi-finished stocks, AI can derive production-ready variants based on approved BOM alternatives in order to fully utilize what is available in the interests of cost efficiency and sustainability.
A safe starting point for the introduction of AI are workflows in which external information is processed. Consistent checking by human employees (“human in the loop”) prevents any AI hallucinations from propagating into the next process steps.
No transformation without leadership
A digitalization project such as the introduction of AI can only succeed with professional change management that integrates the corporate culture as well as IT. Because such modernizations affect much more than design and operations. Purchasing, logistics, retail, finance and compliance feel the effects – often months later, without being prepared. Early onboarding reduces resistance: stakeholders are informed from the start, impacts are made transparent, and feedback loops are anchored.
The 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 ideally flow into them. Experience shows that when innovations do not result in meeting inflation, stakeholders react more openly.
Tools with high usability, clean knowledge management and targeted upskilling ensure acceptance and correct use at the operational level – from the dispatcher to the store team. Training and clear role descriptions support teams in which many years of workbench knowledge and digital responsibility come together.
Continuous data cycle instead of a crystal ball
The effect of the digitalization measures can be seen in sales rates and unavailability rates per location, in inventory turns and the proportion of obsolete stocks, in forecast distortion and the metric for evaluating the accuracy of forecast models (MAPE – Mean Absolute Percentage Error) per category. Adherence to delivery dates and variance per supplier reflect the control quality in procurement and logistics. In content and VM 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 speak a common language and AI specifically addresses friction costs, a stable control mode is created. Systems guide the process and teams make decisions based on consistent signals. Then digitalization and AI will turn from cost drivers into concrete levers for cost efficiency.