What is open to buy planning? Formula, example, and steps

By Elie Dufeu, CTO & Co-Founder, Metreecs. Published 4 September 2026.

Open-to-buy planning is the budgeting method that tells a buyer exactly how much they can purchase in a given period without overbuying: it starts from planned sales, adds planned markdowns and desired ending stock, then subtracts what's already on hand and on order. The result is a spending ceiling, not a target. If you're over it, you cut. If you're under it, you have room.

Every buyer who has ever placed a purchase order larger than the budget allowed already knows the feeling: stock arrives, it sits, and by markdown season it's eating margin instead of generating it. Sofia, a buyer at a mid-market footwear chain, learned this the hard way in her second season on the job. She committed to a new boot line in August without checking her remaining October OTB, reasoning that boots "always sell." By December, $34,000 of that order was still on the shelf, and the category's planned markdown budget for the quarter was already gone before the slower-moving styles even got their turn.

Open-to-buy planning exists to catch that mistake before the purchase order gets sent, not after the boxes land. This guide walks through the formula, a full worked example, where the standard method runs into trouble, and what a more responsive version looks like for retailers managing hundreds or thousands of products across multiple locations.

Key Takeaways

• Open-to-buy (OTB) equals planned sales plus planned markdowns plus planned ending stock, minus beginning stock and stock already on order.
• A retailer with 55% of surveyed SMBs holding at least 20% excess stock, per Netstock's 2025 Supply Chain Planning Benchmark, is a sign that OTB discipline (or the lack of it) shows up directly on the shelf.
• Static, month-level OTB plans lose accuracy the moment actual sell-through diverges from the forecast that built them, which is most of the time.
• The fix isn't abandoning OTB. It's updating the plan more often, and building it from product and location-level demand data instead of a single category number.
• IHL Group's 2026 research puts global inventory distortion (combined out-of-stock and overstock cost) at 6.2% of global retail sales, split roughly two-thirds out-of-stocks and one-third overstocks.

What is open-to-buy (OTB) in retail?

Open-to-buy is a budget, expressed in units or currency, that shows how much a buyer can purchase in a given period without exceeding planned inventory levels. It's calculated from planned sales, planned markdowns, and the inventory a retailer wants to hold at the end of the period, minus what's already in stock or already ordered. A buyer with a $50,000 open-to-buy number for March can commit up to that amount in new purchase orders before the plan is exceeded. See how AI-driven inventory optimization catches overbuys before they ship for a closer look at how this budget connects to the stock that eventually sits on the floor.

The concept dates back decades in fashion and department store retail, where seasonal buying cycles made a hard spending ceiling necessary. It still applies just as directly to beauty, home décor, footwear, and any product category with a defined buying window and a finance team watching working capital.

The open-to-buy formula (and the variants you'll see)

The basic version of the formula is:

OTB = Planned Sales + Planned Ending Inventory − Beginning Inventory − Inventory On Order

Most retailers use a fuller version that accounts for markdowns, since planned price reductions also consume budget:

OTB = Planned Sales + Planned Markdowns + Planned EOM Stock − BOM Stock − On Order

Where:

• Planned Sales: forecasted sales revenue or units for the period
• Planned Markdowns: expected reductions from promotions or clearance
• Planned EOM (End of Month) Stock: the inventory level you want to be carrying at period close
• BOM (Beginning of Month) Stock: what you're actually holding at period start
• On Order: purchase orders already placed but not yet received

Some retailers run OTB at the dollar level for financial planning and a parallel unit-level version for actual buying decisions. Both are legitimate. The dollar version controls capital; the unit version controls what actually shows up on the floor.

A worked example: from budget to purchase order

A home décor retailer is planning March. Here's the input data:

• Planned sales for March: $90,000
• Planned markdowns: $2,000
• Desired ending inventory (March 31): $60,000
• Beginning inventory (March 1): $70,000
• Inventory already on order for March delivery: $15,000

Applying the full formula:

OTB = $90,000 + $2,000 + $60,000 − $70,000 − $15,000 = $67,000

That's the ceiling. The buyer can commit up to $67,000 in new purchase orders for March delivery before exceeding the plan. If a promising new product line needs $72,000 to hit minimum order quantities, the buyer now has a concrete decision to make: cut planned ending inventory elsewhere, negotiate a smaller opening order, or pass on the line this cycle. That's the entire value of the exercise. It turns a vague "can we afford this" conversation into a specific number.

Where static open-to-buy plans break down

A monthly OTB plan is built on a sales forecast that was accurate on the day it was written. The problem is what happens after that day.

If actual sell-through in week one runs 20% ahead of forecast, the plan doesn't know yet. The buyer is still working from a March 1 number in week three, even though the real picture on the ground has already shifted. By the time the monthly review catches the gap, the reorder window on a fast lead-time product may have already closed, or the retailer has already overcommitted on a line that's cooling off.

The same gap shows up at the category level. A single OTB number for "outerwear" or "small appliances" assumes uniform performance across every product and variant inside that category. In practice, one style sells out in the first ten days while a similar one sits untouched. Category-level OTB gives the buyer no way to see that split until the month-end report, at which point the fast mover is already stocked out and the slow mover has consumed budget that should have gone elsewhere.

This is the same granularity problem behind managing seasonal demand spikes without overstocking: a single number for a group of products always hides the products it's wrong about.

None of this means the OTB method is wrong. Planned sales, beginning stock, and desired ending inventory are still the right inputs. What breaks is the update cadence and the level of granularity, not the underlying formula.

How to keep the plan accurate between reviews

The fixes here don't require new software. They require tightening the two variables that make static OTB brittle: how often the plan updates, and how granular the sales forecast underneath it is.

1. Move from monthly to weekly OTB reviews. A month is a long time for actual sell-through to diverge from plan. Weekly reconciliation catches the gap while there's still time to act on a reorder or cancel an order in transit.
2. Break the category number into products and locations where lead time allows it. A category-level OTB hides which specific products are driving the number. Where the reorder window is short enough to react, product-level visibility is worth the extra tracking effort.
3. Feed the plan from a live sell-through number, not a static forecast. The gap between "planned sales" and "actual sales" is exactly what makes static plans go stale. Updating the sales input as real data comes in keeps the OTB number honest.
4. Separate markdown risk by product, not by category average. A category-wide markdown assumption smooths over the fact that some products need a much deeper reduction than others to clear, and some need none at all.
5. Reconcile on-order against actual lead times, not calendar months. If a product has a 12-week lead time and the plan reviews monthly, the buyer is working three cycles behind the commitment window. Align the review cadence to the lead time that actually governs the reorder decision.

Across Metreecs' work with retailers running open-to-buy alongside AI-driven demand forecasts, the shift that matters most isn't a new tool. It's replacing a single category-level sales assumption with a product x location forecast that updates as sell-through data comes in, so the OTB number reflects what's actually happening on the floor instead of what was planned a month ago. One mistake we repeatedly see is treating the OTB spreadsheet as the source of truth for demand, when it should be a budget constraint applied on top of a forecast, not a forecast itself.

For retailers managing hundreds of active products and variants, that distinction determines whether OTB catches an overbuy in week two or in the month-end report. Automated replenishment recommendations that respect a buyer's OTB ceiling, rather than ignoring it, close that gap.

Metreecs' product x location demand forecasting is one example of what makes that shift possible in practice, replacing the single category assumption with a live signal for every product in every location.

Common open-to-buy mistakes to avoid

Setting the plan once and not revisiting it. An OTB number built in week one and never touched again is only accurate by coincidence. Treat it as a living budget, not a one-time calculation.

Ignoring lead time when checking remaining OTB. A buyer with open budget in week 12 of a 16-week lead time cycle doesn't actually have time left to use it. The commitment window closed before the spreadsheet noticed. Priya, a beauty brand buyer, found $18,000 of unused OTB in week 13 of a 14-week supplier lead time and placed a rush order to use it, paying an air-freight premium that erased most of the margin the extra units would have earned.

Letting personal judgment override the sales input. OTB is only as reliable as the sales forecast feeding it. Swapping in a gut-feel number because "this line always sells" reintroduces the exact bias the formula was built to remove, and it's the same root cause behind the hidden cost of an inaccurate sales forecast showing up everywhere else in the plan.

Running the whole assortment through one blended markdown rate. Products don't clear at the same rate, and a single markdown assumption for the whole category understates risk on slow movers while overstating it on fast ones.

Relying on manual spreadsheets past the point they can keep up. A single-location retailer with a narrow catalog can run OTB in a spreadsheet indefinitely. Once the product count and location count grow, formula errors and forgotten updates start costing real margin.

Marcus, a planning manager at a home goods chain, had been running a single blended OTB spreadsheet across three regional warehouses for two years. A formula referencing the wrong column meant the ending inventory target for one warehouse had been overstated by roughly 15% for four consecutive months, and nobody caught it until a physical count flagged the gap. Separating the plan by warehouse and automating the roll-up closed the gap, not a more careful spreadsheet.

Frequently asked questions

What is the difference between open-to-buy and a buying budget?
A buying budget is often a fixed annual or seasonal cap set by finance. Open-to-buy is the operational tool that translates that cap into a specific number available for purchase right now, adjusted for current stock, sales pace, and orders already placed.

How often should open-to-buy be recalculated?
Weekly is the practical minimum for any retailer with meaningful sales volume. Monthly recalculation is common but leaves too large a gap between what the plan assumes and what's actually happening on the sales floor.

Can open-to-buy be negative?
Yes. A negative OTB means the retailer is already overbought relative to the plan, whether from slower-than-expected sales, orders placed without checking remaining budget, or planned ending inventory being set too low. It's a signal to cancel or delay open orders, not an error in the formula.

Does open-to-buy work for retailers outside fashion?
Yes. The formula applies to any retailer with a defined buying period and inventory investment to manage, including beauty, home décor, footwear, and food and beverage franchise networks. The mechanics don't change; only the review cadence and lead times shift by category.

What's the difference between dollar OTB and unit OTB?
Dollar OTB controls the capital a buyer can commit and is what finance usually tracks. Unit OTB translates that budget into actual product and variant quantities, which is what the buyer needs to place a purchase order. Most retailers run both in parallel.

Conclusion

Open-to-buy planning gives buyers a hard number instead of a guess, and that number is only as good as the sales forecast and update cadence behind it. The formula hasn't changed in decades. What separates retailers who avoid overstock from those who don't is how often the plan gets checked against reality and how granular the sales data feeding it actually is.

Start with a weekly review cadence and a markdown assumption that varies by product instead of category. From there, the next lever is tightening the forecast itself, since a more accurate sales input makes every OTB calculation downstream more reliable. Book a demo to see how product-level demand forecasting can keep your open-to-buy plan aligned with what's actually selling, not what was planned a month ago.