What it means
An apparel buyer chooses both how many units to order and how to split them across sizes. A size curve handles the split, and equal quantities are not always right.
A curve may be written as percentages adding to 100% or a simple ratio, and it should name the sizes in order, because the same numbers without labels are ambiguous. A fictional shop plans 400 jackets: 10% small, 30% medium, 35% large and 25% extra large.
That implies 40, 120, 140 and 100 units, and the totals equal 400. Historical sales provide a starting point: divide unit sales for each size by the relevant total, and check whether stockouts distorted the data.
A size curve is specific to customers, style, fit and sometimes location, so a universal industry curve may not fit a brand and actual data should be tested. Different product categories need different curves, since shoes, fitted dresses and unisex hoodies may have distinct patterns and one apparel ratio should not be applied to everything.
New products lack direct history, so similar styles, customer research and a cautious initial buy are the best guide, with revision after early sales instead of pretending precision. Rounding is necessary when percentages produce fractions, and quantities should be rounded so the final quantities sum to the total without losing or adding units unnoticed.
Suppliers may sell prepacked size assortments, so a retailer might not control every unit and should compare the offered pack with the desired curve before committing. Too few core sizes create early stockouts, while too many slow sizes create markdowns, and sell-through by size helps see both problems.
Returns can also signal fit problems rather than poor size allocation, as a high return rate in one size may reflect inconsistent measurements, so product feedback should be checked. Seasonal changes can affect demand too, since a winter coat may be layered over clothing while a summer top may fit differently, so appropriate periods should be compared.
A size curve also helps distribute stock across channels, because online customers may buy differently from store visitors, and separate inventory pools make sense when evidence supports it. The curve should be reviewed after each selling cycle, as data changes with fashion, demographics and product fit, and the method should stay visible so decisions can be challenged.
A sound size curve turns a total buy into useful size-level stock, and it depends on honest demand data and regular adjustment.
In practice
Real-world examples.
Example
A 400-unit jacket order is split by expected size demand. A fictional urban branch sells more small sizes than a suburban branch, so the allocator separates the two rather than using one chain-wide curve that leaves both with poor stock.
Example
A planner corrects for a historical small-size stockout. A fictional store sold only 20 small shirts because small was unavailable for weeks, so raw sales understate demand, and the planner adjusts after reviewing lost sales. A fictional footwear buyer likewise uses shoe-size demand from comparable shoes and does not copy the T-shirt size split.
Example
A supplier's fixed pack differs from the desired ratio. A fictional supplier offers a 1:2:2:1 pack, but the retailer wants more large sizes, so it negotiates or adjusts its order mix with other packs. A fictional shop that sells out of medium and large shirts in two weeks while extra large remains for months revises the next buy.
Formula
Calculation
Planned units in a size = total planned units x that size's demand share, with rounding that preserves the total.
Worked example. A fictional shop plans 400 jackets with shares of 10% small, 30% medium, 35% large and 25% extra large. The units are 400 x 10% = 40, 400 x 30% = 120, 400 x 35% = 140 and 400 x 25% = 100, and 40 + 120 + 140 + 100 = 400.
Rounding example. For a 101-unit order with the same shares, the raw quantities are 10.1, 30.3, 35.35 and 25.25. Rounding each down gives 10 + 30 + 35 + 25 = 100, one unit short, so the spare unit goes to the size with the largest remainder, which is large at 0.35. The final order is 10, 30, 36 and 25, and 10 + 30 + 36 + 25 = 101.Case study
Seen in the real world.
In this fictional case, Maple Apparel buys equal quantities across four sizes. Medium and large sell out early while small remains unsold. The planner reviews size-level sales, stockout periods and returns. The next order uses a revised curve rather than the old equal split.
She also notices that one dress runs small and draws many returns in medium, so the retailer improves the size chart before simply ordering fewer mediums. Demand and fit are different issues, and fixing the chart protects both sales and customer goodwill. After each season the planner compares forecast and actual sales by size, and the differences guide the next order. Maple Apparel is invented for illustration, and the story describes a method rather than a real company's results.
Watch out
Common mistakes.
- Treating sales during stockouts as full demand.
- Copying one size curve across unrelated styles.
- Rounding quantities without reconciling the total.
Questions
People also ask.
Do shares have to total 100%?
Yes, when expressed as a complete percentage distribution.
Can a supplier dictate the curve?
A fixed pack may limit the buyer's choice.
Should it ever change?
Yes. Review sell-through, fit and changing demand.
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