Size & Pack Optimization for Apparel: How to Stop Leaving Revenue on the Rack

What is size and pack optimization?

Size and pack optimization is the merchandising process of determining how many units of each size to produce or purchase for every style in an assortment, and how those units are grouped into packs for shipment.

For startup and small apparel brands, this decision sits at the intersection of two realities: limited capital and zero tolerance for dead inventory. Getting the size mix wrong doesn't just leave unsold units on the shelf — it creates markdowns, erodes margin, and ties up open-to-buy dollars that should be funding next season's winners.

Why size optimization matters more for emerging brands

Enterprise brands can absorb sizing mistakes across hundreds of doors. A 50-door retailer can redistribute slow sizes through markdowns or off-price channels. Emerging brands — especially those operating 1–5 DTC channels or a small wholesale book — don't have that luxury.

Consider the math:

• →A brand produces 1,000 units of a style across 5 sizes
• →The vendor-default size curve allocates 10% / 20% / 30% / 25% / 15% (XS through XL)
• →Actual demand is 5% / 15% / 35% / 30% / 15%
• →Result: 50 excess XS units, 50 excess S units — 10% of the buy is dead before the first markdown

At a $40 wholesale cost, that's $4,000 in stranded capital per style. Multiply by 30 styles in a season, and the brand is sitting on $120,000 in unsellable inventory.

The size curve: your most important planning input

A size curve is the percentage distribution of units across sizes for a given style, category, or channel. Effective size curves are:

Built from sell-through data, not production data

The most common mistake emerging brands make is using last season's production ratios as this season's size curve. Production ratios tell you what you made — sell-through ratios tell you what customers actually wanted. These are rarely the same.

Category-specific, not brand-wide

A fitted dress and an oversized hoodie should not share a size curve. Body-conscious silhouettes typically skew toward the center of the size range, while relaxed-fit styles see more even distribution across sizes. Build curves at the category or silhouette level.

Channel-aware

DTC customers and wholesale accounts often have different size demand profiles. A wholesale account in the Midwest may index heavier on L/XL than your DTC customer base. If you're shipping the same size ratio to both channels, one of them is getting the wrong mix.

Pre-pack vs. singles: which strategy fits your brand?

Pre-packs

Pre-packs group units into fixed size assortments (e.g., 1-2-3-2-1 across S-M-L-XL-XXL). They reduce warehouse handling costs and are required by some wholesale accounts.

The risk: Pre-pack ratios are set by the manufacturer and rarely match your actual demand curve. If your customers skew M/L but the pack includes equal XS and XXL, you're buying dead inventory inside every carton.

Singles ordering

Singles (or "broken size" ordering) lets you specify exact quantities per size. More expensive per unit — but for emerging brands with tight budgets, the precision often pays for itself in reduced markdowns.

Best practice for brands under $20M revenue: Order singles for your top 20% of styles (where size accuracy matters most) and use pre-packs for basics and replenishment styles where the size curve is stable.

Common sizing mistakes that cost emerging brands

1. Copying the vendor's suggested size curve

Vendors optimize for their own production efficiency, not your sell-through. Always validate vendor ratios against your own sales data.

2. Not accounting for returns by size

If your XL return rate is 22% but your S return rate is 8%, your net size curve looks very different from your gross size curve. Plan to net demand, not gross shipments.

3. Ignoring size-specific sell-through velocity

A size that sells through at 85% in 6 weeks is very different from one that sells through at 85% in 14 weeks. Fast-moving sizes deserve deeper buys; slow sizes need conservative depth even if they eventually sell.

4. Using a single size range across all styles

Not every style needs XXS and XXXL. Offering sizes that have near-zero demand dilutes your buy and creates guaranteed residual. Match the size range to the customer demand profile for each category.

How to build a size optimization workflow

1. →Pull sell-through data by category and channel — at least 2 prior seasons, filtered to full-price sales only
2. →Calculate net demand curves — subtract returns from gross sales by size
3. →Identify outliers — flag styles where actual sell-through deviated more than 5% from the planned curve
4. →Build category-level curves — set a default curve per category, then override at the style level for outlier silhouettes
5. →Tie curves to OTB — recalculate unit quantities whenever the curve or the OTB budget changes
6. →Review mid-season — adjust curves for replenishment orders based on in-season sell-through

Size optimization and OTB planning

Size decisions directly impact open-to-buy utilization. A size curve that over-allocates to slow sizes effectively locks up OTB dollars in future markdowns. Connected planning systems ensure that size changes automatically flow through to:

• →Unit quantities per style
• →Receipt plans by delivery window
• →Cost and margin projections
• →Warehouse capacity planning

When size planning lives in a disconnected spreadsheet, none of these downstream impacts are visible until it's too late.

Related resources

• →What Is Assortment Planning? — How size optimization fits within the broader assortment process
• →Size Curve — Formula, common errors, and how to build a size curve from scratch
• →Breadth vs Depth — The tradeoff that determines how many sizes you can afford to carry
• →Replace Spreadsheet Planning — Why spreadsheet-based size planning breaks down at scale
• →SKU Rationalization — How to identify and cut unprofitable size/color combinations