Jonas Hartmann
Writes about business models, margins, pricing and international expansion in fashion.
Articles by Jonas Hartmann
How Revolve uses data and algorithms to buy, price and reorder
Revolve tests styles in small quantities and uses a proprietary dashboard to decide what to reorder, with full-price selling at the centre. How the model works, where AI now fits, and what is not disclosed.
How does Amazon Fashion use AI for size recommendations and fit insights?
Amazon combines deep-learning size advice, AI review summaries and a fit insights tool for brands, with a shopping assistant now called Alexa for Shopping. What it has published and what brands should do.
How do you forecast demand for a fashion product with no sales history?
Most of a fashion range is new each season, so classic time-series forecasting has nothing to work with. How AI borrows history from similar products, images, attributes and early sales, and where it still falls short.
How can AI set the right initial price for a fashion product?
The initial price decides how much of a product sells at full price. How elasticity models estimate customer response to price for new items, what data they need, and how to use them without eroding the brand.
How to choose a demand forecasting tool: a checklist for fashion brands
Demand forecasting software promises less stock and fewer stockouts, but results depend on fit with fashion's product cycles and on your data. A neutral checklist for evaluating tools before you sign.
What is store clustering and how does AI improve it?
Store clustering groups doors with similar customers and demand so each group gets the right assortment and depth. How machine learning builds clusters, what data it uses and where the method has limits.
How do weather and external signals improve fashion demand forecasting?
Unseasonal weather regularly moves fashion sales, and retailers often cite it in results. What weather, events and search data can add to AI demand planning, how much, and where the evidence is thin.
Which AI KPIs should fashion companies track in each function?
A function-by-function checklist of AI metrics for fashion businesses: what to measure, how to set baselines, and how to separate usage from real business impact.
AI in fashion merchandising: what it does, what it needs and where it fails
Machine learning now supports forecasting, assortment, pricing and planning decisions in fashion. This guide explains the use cases, the data they depend on and how merchants stay in control.
How does AI inventory optimisation reduce deadstock and stockouts in fashion?
Fashion stock is perishable: too much ends in markdowns, too little in lost sales. Here is how AI models balance the two, what evidence exists and what they need to work.
What is demand sensing and how does it work in fashion?
Demand sensing updates short-term forecasts with the latest sales and external signals. In fashion it mainly improves in-season decisions: replenishment, transfers and markdowns.
How can AI improve open-to-buy and merchandise budget planning?
Open-to-buy tells buyers how much they can still spend without breaking sales and stock targets. AI can keep that number current and more realistic, provided the plan behind it is sound.
How H&M uses AI: demand, allocation and digital twins
H&M has applied analytics and AI to buying quantities, store allocation and pricing since 2018, and to campaign imagery since 2025. What is documented, and what it has not disclosed.
AI for merchandisers and planners: a practical guide
How AI supports merchandise financial planning, forecasting, allocation, replenishment and markdowns in fashion, what data it needs, what planners still decide and how to start.
Getting sell-out data via EDI: SLSRPT, INVRPT and what to do with them
Sales and inventory reports from retail partners show brands what actually sells, store by store and size by size. How the two EDI messages work and how merchandising teams can use them for re-orders.
How do you turn AI trend reports into range and buying decisions?
AI trend platforms describe what is rising; a range plan needs quantities, prices and risk limits. A practical workflow for moving from signal to buy.
What can resale prices tell designers about fashion trends?
Second-hand prices and searches reveal which designs hold value, which revivals are coming and which brands are regaining desire. How to read them, and where they mislead.
AI demand forecasting in fashion: how it works and where it fails
Machine learning can improve fashion forecasts for carry-over lines and replenishment, but new styles, short seasons and trend shifts remain hard. What leaders should expect, and what not to.
AI and pricing: markdown optimisation explained
Markdown optimisation uses demand models to decide when and how deeply to discount. How it works, what it needs, and why brand and wholesale relationships must set its limits.
The AI vocabulary every fashion buyer should know
Suppliers, brands and internal teams increasingly talk about models, agents and embeddings. A practical glossary for fashion buyers, with what each term means for buying decisions.
Open-to-buy explained: how fashion retailers budget their buying
Open-to-buy tells a buyer how much stock can still be purchased without breaking the plan. Here is how it is calculated, used and commonly misused.
The retail buying calendar: a year in the life of a fashion buyer
Fashion buyers work across several seasons at once. How the buying year is structured, what happens at each stage and how the calendar is shifting.
Retail metrics explained: sell-through, GMROI and weeks of cover
Three metrics answer most questions about how fashion stock is performing. How to calculate sell-through, GMROI and weeks of cover, and read them together.
How margins work in fashion, from factory to shop floor
From the factory cost to the price on the swing tag: how landed cost, wholesale markup and retail markup combine, and why markdowns decide what margin is really earned.
Pricing architecture: wholesale price, RRP and currency price lists
How fashion brands structure price points, wholesale prices, recommended retail prices and currency lists into one coherent system that retailers trust and finance can defend.