Apparel inventory planning: a guide to sizes and buys

By Elie Dufeu, CTO & Co-Founder, Metreecs. Published 28 October 2026.

Apparel inventory planning is the process of deciding how many units of each style to buy, in which colors and sizes, for which stores and channels, and how much of that buy to commit before the season versus during it. It works when depth is set at size and color level from clean sales history, and when part of the budget is kept back to chase what actually sells.

Hannah buys knitwear for a womenswear chain with 40 stores, and for autumn she bought a new crew-neck sweater in five colors and split each color evenly across six sizes, because the style had no history and an even split felt neutral. Three weeks after launch, medium and large were gone in most stores in the two best colors, while extra small and double extra large sat on the shelf until the January sale. Total sell-through on the style looked acceptable on the weekly report, which is why nobody caught the problem until the size grid was broken. (Hannah is an illustrative example, but buyers in the category will recognize the pattern.)

Most apparel planners already know that sizes and colors multiply the work. The hard part is turning a season budget into a buy that matches demand variant by variant, then correcting it fast once real sales arrive. This guide to apparel inventory planning covers how to build size curves that hold up, how to split pre-season and in-season buys, a six-step monthly cycle, and where the method still has limits. If you want to test it on your own range, you can see how variant-level forecasts look on your sales history.

Key Takeaways

• US clothing stores sold about $222.7 billion in 2025, and December sales ($27.5 billion) were roughly double January's ($13.5 billion), according to US Census Bureau monthly retail data, so timing errors in an apparel buy are expensive.
• November and December together brought in about 22% of 2025 clothing store sales in the same Census series, which is why the pre-season commitment for the holiday window matters so much.
• The NRF and Happy Returns 2025 Retail Returns Landscape expects 15.8% of US retail sales to be returned in 2025, rising to 19.3% for online sales, so size curves built on gross sales overstate the sizes that come back most.
• McKinsey found that early adopters of AI-enabled supply chain management improved inventory levels by 35% and service levels by 65% compared with slower competitors.
• On a 1,200-unit style, an even split across six sizes leaves the medium 160 units short and the extra small 128 units over when the true curve peaks at medium (our own illustrative arithmetic, shown below).

What is apparel inventory planning?

Apparel inventory planning is the process of converting a season's sales and margin targets into buy quantities for every style, color, and size, by store and channel, then managing those quantities through the season with reorders, transfers, and markdowns. It decides depth per variant and timing per commitment, within a budget set by open-to-buy.

It helps to separate three layers that often get mixed up. The budget layer sets how much money can be spent on inventory by month and category, which is the job of open-to-buy planning. The seasonal layer sets the shape of demand over the weeks of the season, which our guide to seasonal demand planning in fashion covers in detail. Apparel inventory planning sits underneath both: it takes the budget and the seasonal curve and turns them into units per style, color, size, and location.

The variant tree is what makes the layer heavy. A shirt in four colors and six sizes is 24 variants, and across 40 stores that becomes 960 store-level decisions for one style. A range of 300 styles quickly produces hundreds of thousands of quantities, far more than a buying team can set by hand with any precision.

Why apparel inventory planning is harder than in most categories

Five features of clothing push a standard retail plan off course, and each one changes how apparel inventory planning has to be set up.

Demand splits across sizes that cannot substitute for each other. A customer who wants a medium will not buy a small. A style can be selling well overall while losing sales every day in its core sizes, and a report at style level hides it.

Seasons are short and the buy comes first. A seasonal collection may sell at full price for 10 to 14 weeks, but the main order is often placed four to six months earlier. The Census data shows how uneven the year is, with December clothing store sales about twice January's in 2025, so a buy that lands late misses the window it was meant for.

Returns distort the signal. With the NRF expecting 19.3% of online sales to be returned in 2025, gross sales by size can be badly misleading. Sizes with fit problems sell twice: once on the way out and once as a return that goes back into stock weeks later.

Suppliers ship in packs and minimums. Many vendors sell fixed size packs (for example one extra small, two small, three medium, two large, one extra large) and set color minimums. The order can only approximate the target curve.

Many styles have no history. New styles, new fits, and new colors make up a large share of each season, so the curve has to be borrowed from something similar.

The buyers we work with often underestimate how much of their end-of-season markdown sits in two or three fringe sizes rather than in whole styles. Across Metreecs' work with fashion and apparel retailers, the first gain usually comes from fixing size depth on styles that already sell, before any change to the range itself.

Size curves: the core of apparel inventory planning

A size curve is the expected share of demand for each size within a style and color. It turns one style forecast into size-level quantities, and in apparel inventory planning it often decides whether a good style ends the season at full price or on the sale rail.

Here is what an even split costs. Take a style forecast of 1,200 units across six sizes, with a true demand curve of 6% extra small, 18% small, 30% medium, 26% large, 14% extra large, and 6% double extra large.

SizeTrue demand shareUnits demandedEven split buyGapXS6%72200128 overS18%21620016 shortM30%360200160 shortL26%312200112 shortXL14%16820032 overXXL6%72200128 over

(These figures are our own illustrative arithmetic.) The style sells 912 of its 1,200 units before medium and large run out, and 288 units stay in the fringe sizes. The forecast for the style was perfect, and the season still ends with lost sales and markdowns at the same time.

There are four common ways to build the curve, and most retailers end up using more than one.

ApproachProsConsIdeal use caseEven split across sizesSimple, no data neededUnder-buys core sizes, over-buys fringe sizesNever as a default; only for tests of a few unitsOne company-wide curve per categoryEasy to maintain, stableIgnores fit, color, and store differencesBasics with little fit variation, such as socks or teesCurve by style family, color group, and store clusterMatches local demand, catches regional size skewsNeeds clean history and regular refreshCore and repeat styles with at least one full season of salesPooled curve borrowed from similar stylesWorks for new styles, shares data across siblingsCan carry over the wrong fit if the new style is cut differentlyNew styles, new colors, and small stores with thin data

Whatever the approach, three rules keep a curve honest:

1. Build it from weeks when every size was in stock and selling at full price. Stockout weeks and markdown weeks push the curve toward whatever sizes were left.
2. Use demand net of returns, by size. A size that sells well and comes back often is weaker than its gross sales suggest.
3. Refresh it every order cycle, and check whether color changes it. Dark colors and pale colors of the same style can skew to different sizes.

When packs limit the order, choose the pack mix that comes closest to the curve on the core sizes, then top up broken sizes from the warehouse or through store transfers in season.

Pre-season vs in-season buys

The second decision in apparel inventory planning is how much of the buy to commit before the season and how much to keep back. Committing everything early gets the best unit cost and secures production capacity, but it locks in every forecast error. Holding back budget costs some margin on unit price and needs a supplier who can react, but it lets the plan follow real demand.

A practical way to set the split is by product role:

• Core and repeat styles with steady demand and a reliable curve can be committed mostly pre-season, with replenishment orders on a fixed rhythm.
• Seasonal fashion styles get a smaller first buy, enough to fill stores with the full size range, and a reserve of budget for chase orders on the colors and sizes that sell.
• Trend and test styles are bought shallow, read after the first weeks of sales, and either chased or dropped.

The share kept back depends on supplier lead times and minimums, so there is no universal number. What matters is that the reserve is written into the open-to-buy plan from the start, rather than found by cutting other orders mid-season.

Marcus plans menswear for a direct-to-consumer brand that sells chinos in six colors and 14 waist and length combinations. For spring he committed the two core colors in full, bought the four seasonal colors at about half the planned depth, and kept the rest of the budget open with a supplier who could repeat within seven weeks. After four weeks the olive chino was selling at twice the plan in three waist sizes, and the sand color was well behind, so Marcus placed the chase order on olive in those sizes only and cancelled the reserve on sand. (Marcus is an illustrative example.) The in-season read only worked because the variant forecast was updated weekly, which is what demand planning at product and variant level is built for.

In season, the other lever is moving stock that already exists. When a size sells out in one region and sits in another, a transfer is usually cheaper than a chase order, and our article on rebalancing allocation between stores covers how to decide when a transfer pays for itself.

A monthly apparel inventory planning cycle in six steps

This cycle runs once a month, with a weekly check on sales by variant during the season. It assumes at least one full season of sales history by style, color, size, and store, with returns recorded against the original sale.

1. Clean the demand history. Remove stockout weeks and markdown weeks from the curve base, net out returns by size, and flag promotions so they can be added back as explicit events.
2. Forecast at style and color level by channel. Use the seasonal profile for the category and the planned launch date. For new styles, borrow the history of the closest existing style and say which one in the plan.
3. Apply size curves by store cluster. Split each style and color forecast into sizes with the right curve from the table above, then check the result against supplier packs and minimums.
4. Split the buy into commitment and reserve. Commit core styles and the opening depth of seasonal styles, and keep the reserve budget open in the open-to-buy plan with a date by which it must be used or released.
5. Read sell-through by variant after launch. Compare sales with the forecast by size and color, not only by style. Chase the variants running ahead, slow or cancel the ones running behind, and move stock between stores before ordering more.
6. Set markdown triggers early. Agree in advance which variants get marked down at what level of cover, so the fringe sizes are cleared in a planned way rather than in a single end-of-season sale.

A monthly apparel inventory planning review then compares forecast and actual sales by size, color, and store cluster, and tracks bias separately from error. In our experience deploying variant-level forecasting at Metreecs, the weekly read in step 5 is where most of the value shows up, because it is the first moment the plan meets real customers. A curve that is always short on large in one region points to a bias you can correct at the next refresh.

Where apparel inventory planning still falls short

A plan narrows uncertainty without removing it, and three gaps stay open in apparel.

The first gap in apparel inventory planning is true trend items. A style with a new silhouette, a viral color, or a collaboration has no close sibling to borrow from, so the forecast is a judgment call however good the model is. Buy shallow, read early, and keep the reserve ready. Borrowing signals from attributes such as fabric, price band, and silhouette helps, but it narrows the range of outcomes rather than closing it.

The second is timing on returns. Returned units arrive weeks after the sale, often after the size has been reordered, so stock levels can swing for reasons unrelated to demand. Planning on net demand helps, but the operational lag stays.

The third is fit changes. When a supplier changes a pattern, last season's curve can be wrong overnight, and the first signal is a jump in returns for one size.

Aiko plans kidswear for a chain of 25 stores. A new supplier cut its trousers slightly smaller, and within a month the returns rate on the age 6 size doubled while the age 8 size sold out. Because she tracked returns by size each week, she spotted the shift before the reorder, moved the next order one size up, and asked the supplier to correct the pattern for the following season. (Aiko is an illustrative example.)

One mistake we repeatedly see is a size curve set once at range launch and copied into every following season, which keeps buying the fringe sizes that marked down last year.

FAQ

How do you plan inventory for a clothing store?

Apparel inventory planning starts with the open-to-buy budget by month and category. Then forecast each style and color by channel and split it into sizes with a curve built from clean, full-price, in-stock weeks. Commit core styles before the season, keep a reserve for chase orders on seasonal styles, and review sell-through by size and color every week once the season starts.

What is a size curve in apparel inventory planning?

A size curve is the share of a style's demand that each size is expected to take, for example 30% medium and 6% extra small. In apparel inventory planning it turns one style forecast into a buy per size. It should be built from demand net of returns and refreshed every order cycle, ideally by store cluster.

How much of an apparel buy should be committed before the season?

It depends on the product role and on supplier lead times. Core styles with a reliable curve can be committed mostly pre-season, seasonal styles usually get a smaller opening buy with budget held back, and trend styles are bought shallow and chased only if they sell. The reserve should be planned in the open-to-buy budget from the start.

How do returns affect apparel inventory planning?

Returns make gross sales overstate demand for some sizes, especially online, where the NRF expects 19.3% of 2025 sales to come back. Build size curves on net demand by size, record returns against the original sale, and watch return rates by size as an early warning of fit problems.

How do you manage stock across sizes and colors in several stores?

In apparel inventory planning, treat each size and color as its own variant, apply curves by store cluster rather than one national curve, and use transfers between stores before placing a chase order. A weekly check of broken size grids by store shows where stock is in the wrong place rather than missing.

Conclusion

Apparel inventory planning comes down to two decisions made well: how deep to buy each size and color, and how much of the buy to hold back until real sales arrive. A good first step is to rebuild last season's size curves from full-price, in-stock weeks, net of returns, and compare them with what was actually bought. To see where your size depth and in-season reserve could change, book a demo with your own range data.

Sources

• US Census Bureau, Monthly Retail Trade, Retail sales: clothing stores (series MRTSSM4481USN, via FRED): 2025 monthly sales, not seasonally adjusted, used for the annual total, the December versus January gap, and the November plus December share.
• National Retail Federation and Happy Returns, 2025 Retail Returns Landscape: expected share of total and online US retail sales returned in 2025.
• McKinsey, "Succeeding in the AI supply-chain revolution": inventory and service level gains for early adopters of AI-enabled supply chain management.