Merchandising & Buying
Forecasting, assortments, allocation and the numbers behind them.
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.
How does AI allocation and replenishment work across stores and channels?
Allocation decides where stock goes first; replenishment decides what follows. AI makes both decisions at size and location level. Here is how it works, what it needs and where it struggles.
What is size curve optimisation and how does AI help buy the right sizes?
Buying the wrong mix of sizes leaves fringe sizes on the rail and core sizes sold out. Here is how AI estimates true size demand, why stockouts mislead the data and what to watch for.
How can AI support range and line planning in fashion?
Range planning decides how many styles, at which prices and in what depth a collection should have. AI can forecast new items and test scenarios, but the creative and strategic call stays human.
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.
Why is sell-out data essential for AI in fashion wholesale?
Brands that sell through retailers see what they ship, not what consumers buy. Sell-out data closes that gap, and without it most AI forecasting for wholesale works half blind.
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.
How Stitch Fix uses AI: algorithms plus human stylists
Stitch Fix pairs recommendation algorithms with about 1,700 part-time stylists, and has added generative AI tools such as Vision. How the hybrid model works and what the numbers show.
AI for fashion buyers: what changes in buying and how to start
Where AI already helps fashion buyers with trend scanning, line reviews, quantities and re-orders, what data it needs, what stays a human judgement and a 30-day plan to start.
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.
AI in fast fashion: speed, demand signals and the overproduction problem
Fast fashion lives on reading demand early and reacting fast. AI sharpens both, but it can also accelerate volume, and new EU rules make unsold stock more costly.
AI in childrenswear and basics: forecasting replenishment-driven ranges
Basics and childrenswear sell steadily, in many sizes, at thin margins. That makes them ideal for AI forecasting and replenishment, and sensitive when the customer is a child.
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.
Using AI in assortment planning without losing the brand
AI can sharpen depth, breadth and size decisions in assortment planning. The risk is a range that drifts towards safe averages. How to use the tools while keeping the brand's point of view.
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.
Fundamentals
The business context every AI project in this area depends on.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.