How can AI improve open-to-buy and merchandise budget planning?

KEY TAKEAWAYS Summary by the editors

1. Open-to-buy (OTB) is the value of inventory a business can still purchase in a period while meeting its sales and stock targets.
2. A common formula is: planned sales plus planned markdowns plus planned end-of-period inventory, minus planned beginning-of-period inventory, minus stock already on order.
3. AI improves OTB mainly by making its inputs better: more accurate sales and markdown forecasts, and faster re-forecasting during the season.
4. Mango announced in March 2025 that it would modernise merchandise financial planning together with assortment and demand planning on an integrated, AI-powered platform.
5. OTB remains a management control: AI can recommend where to release or hold budget, but finance and merchandising must agree the targets and the rules for overriding them.

AI can improve open-to-buy by forecasting the inputs of the calculation (sales, markdowns and stock) more accurately and updating them more often, so the budget buyers can still spend reflects the season as it is unfolding rather than the plan made months earlier. The OTB logic itself stays simple; what changes is the quality and timeliness of the numbers that feed it.

What is open-to-buy?

Open-to-buy is a budgeting control used in retail and wholesale buying. Shopify defines it as "the value of inventory a retailer can purchase during a set period and still meet their sales and stock targets" and gives the formula:

OTB = (planned sales + planned markdowns + planned end-of-month inventory) minus planned beginning-of-month inventory

In practice, businesses also subtract stock already on order for the period, so OTB shows the budget genuinely still available. It is usually calculated at retail or cost value, by category and month, and sits inside merchandise financial planning (MFP), the process that sets sales, margin and stock targets for the season.

Why is traditional OTB planning difficult in fashion?

• Most of the budget is committed early, before any sales data for the season exists.
• Plans are built top-down in spreadsheets and are rarely re-forecast in detail once the season starts.
• Markdown plans are often optimistic, so OTB looks larger than it really is.
• Category, channel and wholesale plans are maintained separately and drift apart.
• When sales run ahead, buyers lack a fast, trusted view of how much extra budget can safely be released for re-orders.

The result is a familiar pattern. In a strong season, buyers run out of budget just as re-order opportunities appear, because OTB still reflects the original plan. In a weak season, stock builds up, markdowns exceed plan and OTB remains open on paper even though the business is already overstocked. Both errors come from the same cause: inputs that are not updated at the speed at which the season changes.

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Where does AI add value in OTB and budget planning?

The markdown line deserves attention. Zalando's published forecasting work models how demand responds to discounts and describes pricing as a major lever for reducing the risk of end-of-season overstock. When markdowns are forecast from current stock and sell-through rather than assumed as a fixed percentage, OTB stops overstating the budget available in a slow season.

Which retailers are modernising financial planning with AI?

Merchandise financial planning is increasingly part of integrated planning programmes. In March 2025 Mango announced it would modernise merchandise financial planning, assortment planning and demand planning on an integrated, AI-powered and data-driven platform, replacing fragmented systems. In January 2024 Marks & Spencer announced a three-year programme for clothing and home starting with merchandise planning, sales, stock and intake, and range planning. The common thread is connecting the financial plan to product-level forecasts, so that changes at one level flow through to the other.

How does an AI-assisted OTB process work in practice?

1. Set the frame: finance and merchandising agree season targets for sales, margin and stock by category and channel.
2. Build bottom-up forecasts: models forecast sales and markdowns for planned and existing products.
3. Reconcile: planners compare top-down targets with bottom-up forecasts and resolve gaps explicitly.
4. Calculate OTB: by category and month, net of stock on order.
5. Re-forecast in season: weekly updates of sales and markdown forecasts adjust OTB and trigger alerts.
6. Release or hold budget: buyers use the updated OTB for re-orders, new drops or cancellations, within agreed approval rules.

The reconciliation step is where most of the value lies. A bottom-up forecast that differs sharply from the top-down target is not an error to be hidden; it is information. Either the target needs a credible plan to close the gap (more options, deeper buys, a marketing push) or the target should change. Making these gaps visible early is more useful than any single forecast number.

Generative AI is starting to appear as an interface on top of this process, for example to answer questions such as which categories are over plan, or to draft the weekly trading summary. McKinsey and The Business of Fashion reported in The State of Fashion 2026 that more than 35 percent of executives surveyed already use generative AI in areas such as customer service, copywriting and product discovery. In budget planning, such tools are only as reliable as the numbers and definitions behind them.

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What should finance and merchandising watch out for?

• Consistent definitions: retail value versus cost value, gross versus net sales, and treatment of returns must match across systems.
• Wholesale timing: for brands, wholesale pre-orders arrive in batches and are later partly cancelled; OTB for own retail and for wholesale supply needs separate logic.
• Forecast bias: if forecasts are systematically optimistic, AI will make OTB faster but not more accurate; track bias, not only error.
• Explainability: buyers will not trust a budget number they cannot trace to its drivers.

Start by automating the re-forecast of one category's OTB weekly, alongside the existing plan, and compare outcomes at the end of the season. The goal is not a budget set by a machine, but a budget that reflects reality soon enough for buyers to act on it.

Frequently asked questions

What is the open-to-buy formula?

A common version is: planned sales plus planned markdowns plus planned end-of-period inventory, minus planned beginning-of-period inventory. Many businesses also subtract stock already on order to show the budget still available.

How does AI help with open-to-buy?

AI improves the inputs to OTB by forecasting sales and markdowns at product level and updating them during the season. This makes the remaining budget more realistic and flags when to release or hold spending.

What is merchandise financial planning?

Merchandise financial planning sets sales, margin and inventory targets by category, channel and period. Open-to-buy is derived from it and controls how much buyers can still commit.

Should open-to-buy be calculated at cost or retail value?

Both are used. Retailers often plan at retail value to align with sales targets, while finance may track cost value for cash and margin. The key is to use one definition consistently across plans and systems.

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