An underwear size run for e-commerce is an inventory decision, not a measurement exercise: the size curve decides which sizes sell out, which sizes become dead stock, and where the returns come from. According to Finetex’s current underwear page, its full-customization MOQ of 1,000-3,000 pieces per design allows mixed sizes, which means the buyer controls the curve inside the minimum. The brand that treats the size mix as a plan, and adjusts it with sell-through data, reduces returns at the source instead of absorbing them.
Why Size Mix Decides Your Underwear Margin
Underwear margins are thin enough that the size mix decides the profit. A first order that follows the market curve sells through the middle sizes and keeps the tails small; one that orders equal quantities per size creates dead stock at one end and stockouts at the other, and both outcomes cost the same margin.
The returns add a second layer. Underwear is bought without trying on, so the size that does not fit comes back, and the return reason is usually the chart, not the customer. The size curve, the chart, and the fit notes are one system, and the brand that designs them together protects the margin twice.
Building a Size Curve From Market Data
The size curve starts with the market and the channel. An Amazon basics listing follows the category’s historical curve, which can be read from market data or from the brand’s own early listings; a streetwear line skews differently; a subscription program follows the subscriber body profile. A wholesale program follows the retailers’ order patterns, which are usually wider in the middle and thinner at the tails than a DTC line.
The curve also shifts with the fit profile. A slim-fit line moves the distribution toward smaller sizes than a classic-fit line, because the same body measurement maps differently in the two profiles. The curve is therefore two decisions at once: the fit story the brand tells and the quantity plan that delivers it.
| Size | First-order share | Why |
|---|---|---|
| S | Small | The tail, tested, not stocked deep |
| M | Largest | The core of the curve |
| L | Largest | The core of the curve |
| XL | Small | The tail, tested, not stocked deep |
The percentages should come from data, not intuition: the category curve when the brand has none, and the brand’s own sell-through after the first order. The curve is a starting plan, and the plan is updated with evidence.
The curve also changes with geography. A North American basics line and a European line can share the fit but not the size distribution, because body profiles and apparel sizing conventions differ by market. If the brand sells in multiple markets, the curve should be built per market and the order split accordingly, rather than forcing one ratio onto all of them.
Mixing Sizes Within the MOQ
The factory minimum and the size mix are separate decisions. Finetex’s current underwear page states a full-customization MOQ of 1,000-3,000 pieces per design with mixed sizes, so the buyer can allocate the total across sizes rather than buying per-size minimums.
Write the ratio as a percentage per size in the order spec, and confirm the ratio at the cutting stage, because that confirmation is the first quality gate of the order. The factory cuts to the mix when it knows the mix; a factory left to decide will cut equal blocks, which is exactly the pattern that creates dead stock.
A worked example makes the rule concrete. On a 1,000-piece order with a 15/30/35/20 split across S, M, L, and XL, the spec reads 150/300/350/200, and the factory confirms the cut against those numbers. The same order with equal 250s per size produces the dead-stock pattern the curve was designed to avoid, which is why the ratio belongs in the spec and the confirmation belongs at the gate.
Returns Data as the Next Size Curve
The first order’s returns are the best data the brand will ever get. Cluster the return reasons by size and fit profile: if the returns cluster on “too small” in the slim profile, the grade rule is off; if they cluster across the board, the chart itself is wrong. If the returns cluster on the waist, the issue is the grading or the fabric recovery; if they cluster on the leg, the issue is the cut.
The review should run at the SKU and the size level together. A SKU-level return rate hides the size story, and a size-level rate without the SKU context hides the product story. The two views together point to the fix, and the fix belongs in the next order rather than in the next customer service reply, because a corrected size curve prevents the return before it happens.
The return reason field, not just the return count, is what turns the data into a fix. The second order rebalances the size ratio toward the sell-through curve, and the rebalance is a normal part of the basics business, not a sign of failure.
FBA Size Segments and Replenishment
Amazon restocks by size segment, and the replenishment plan should follow the size curve rather than the SKU total. If the M and L sizes sell out first, the reorder should weight them heavier, and the trigger should fire before the fastest-moving size hits zero.
The replenishment math belongs to the basics replenishment guide, and the underwear version adds one rule: rebalance the ratio from the data at every review. The size curve is not a one-time decision; it is a living plan that the weekly and monthly reviews update.
The FBA side adds its own timing rule. Because inbound processing and storage are part of the calendar, the reorder must fire before the fastest-moving size hits zero, and the lead time should be mapped backward from the restock window. The size curve and the replenishment calendar are one system, and the brand that reviews them together avoids both the stockout and the dead stock.
Fit Notes That Set the Right Expectation
The size curve only works if the chart and the fit notes set the right expectation. Underwear has no fitting room, so the chart is the closest thing to one, and the fit notes should name the profile: regular, slim, or athletic, and how the garment fits at the waist and the leg.
The chart structure should be simple enough for a phone screen: the waist measurement, the hip measurement, and the size the customer should choose for their usual apparel size. Over-complicated charts create confusion, and confusion creates returns. The fit notes add the expectation the numbers cannot: how the garment should feel at first wear and how it behaves after washing.
A sizing analyst’s view on the size curve
A sizing analyst who works with marketplace apparel data describes the size curve as the quiet driver of the business. In their experience, brands obsess over the fabric and the print while the return data quietly blames the chart, and the fix is almost always the mix, not the product. Their standard practice is to review the curve after every 500 units of sell-through, not once a season, because the market moves faster than the quarterly review.
Finetex’s men’s underwear manufacturing page grades the boxer briefs, trunks, briefs, and jockstraps programs by the size runs an e-commerce order needs, and its contact page accepts size distribution briefs with the order plan. Bring the size distribution with the order, and the factory can confirm the mix at the cutting stage.
The size curve is the quiet part of the underwear business, and the brands that plan it, measure it, and adjust it are the ones whose margins survive the first season. Treat it as a system, update it with every sell-through review, and the returns shrink as the data grows, while the stockouts shrink with it, and the margin follows both.
Frequently Asked Questions
Why does the size mix matter more than the fit?
The mix decides which sizes sell out and which become dead stock, while the fit decides the returns. Both protect the margin, and the mix is the one most brands ignore.
How do I build the first underwear size curve?
Start with the category curve for your channel, weight the middle sizes, and keep the tails small. Update the curve with your own sell-through after the first order, because the market data is a starting plan and the brand’s own data is the correction.
Can I mix sizes inside the underwear MOQ?
Yes. Finetex’s current underwear page states a full-customization MOQ of 1,000-3,000 pieces per design with mixed sizes, so the buyer controls the allocation inside the minimum. Write the ratio as a percentage per size in the order spec and confirm it at the cutting stage.
How do returns data improve the next order?
Cluster the return reasons by size and fit profile, rebalance the size ratio toward the sell-through curve, and update the chart and the fit notes before the next order.
When should the reorder fire?
Before the fastest-moving size hits zero. Track sell-through by size, not just by SKU, and use the basics replenishment guide for the reorder point math.
What should the fit notes say?
The fit profile (regular, slim, or athletic) and how the garment fits at the waist and the leg. The fit notes set the expectation the chart cannot, and they reduce the returns that the chart alone cannot prevent.
