Apparel Size Curve Template
A structured size curve template for apparel brands — covers size distribution by category, channel, and season, with prior sell-through inputs and buy quantity allocation by size.
What this template is for
The Apparel Size Curve Template provides a structured framework for building and applying size distribution curves — the percentage of total units that should be bought in each size — across a seasonal assortment.
Size curves are among the most consequential decisions in apparel buying. Buying the right total quantity but in the wrong size distribution produces the same financial outcome as buying the wrong quantity: stockouts in productive sizes, markdowns in overweight sizes, and lower overall sell-through than the plan required.
Best for: Apparel brands buying across multiple size ranges (XS–3X, 00–18, XS–XL, etc.) who want to move from applying generic size curves to building size curves from their actual sell-through history.
Template structure
Tab 1: Size Curve Library
| Column | What to enter | |---|---| | Curve ID | Reference name (e.g., "Tops-Standard", "Bottoms-Petite", "Outerwear-FW") | | Category | Department this curve applies to | | Channel | DTC / Wholesale / Both | | Season | SS / FW / Year-round | | Size range | e.g., XS–XXL, 00–18, S–3X | | XS % | % of total units in XS | | S % | % of total units in S | | M % | % of total units in M | | L % | % of total units in L | | XL % | % of total units in XL | | XXL % | % of total units in XXL | | Source | Prior season data / Category average / Estimated | | Season applied | The season this curve was last validated | | Notes | Any exceptions or caveats |
Tab 2: Sell-Through by Size (Prior Season Input)
| Column | What to enter | |---|---| | Style # | Internal style identifier | | Style name | Description | | Category | Department / category | | Season | The prior season this data is from | | Size | Each size on its own row | | Units bought | Units purchased in this size | | Units sold | Units sold through at full price | | Units markdown | Units sold on markdown | | Units remaining | Unsold inventory (bought − sold − markdown) | | STR % | Sell-through rate: (sold + markdown) ÷ units bought | | Buy % | Units bought in this size ÷ total units bought | | Sold % | Units sold in this size ÷ total units sold | | Variance | Sold % − buy % (positive = undersupplied, negative = oversupplied) |
Tab 3: Curve Calculation (from Sell-Through Data)
| Column | What to enter | |---|---| | Curve ID | The curve being calculated | | Category / channel | Scope this curve applies to | | Styles included | # of styles used to build this curve | | Season range | Seasons included in the calculation | | Weighting method | Equal weight / Revenue weight / Units weight | | XS sold % | Weighted average sold % in XS | | S sold % | Weighted average sold % in S | | M sold % | Weighted average sold % in M | | L sold % | Weighted average sold % in L | | XL sold % | Weighted average sold % in XL | | XXL sold % | Weighted average sold % in XXL | | Recommended buy % | Suggested buy distribution based on sold % | | Prior buy % | What was actually bought last season | | Adjustment | Recommended buy % − prior buy % |
Tab 4: Buy Quantity Allocation
| Column | What to enter | |---|---| | Style # | Internal style identifier | | Planned total units | Total units for this style | | Assigned curve | Curve ID from Tab 1 | | XS units | Total units × XS % | | S units | Total units × S % | | M units | Total units × M % | | L units | Total units × L % | | XL units | Total units × XL % | | XXL units | Total units × XXL % | | Total check | Sum of all sizes (should equal planned total units) | | Rounding variance | Units added/removed to reconcile rounding |
How to use this template
Step 1: Pull prior-season sell-through data by style and size in Tab 2. The minimum useful data set is one full selling season — two seasons produces a more stable curve.
Step 2: Calculate the variance between what was bought by size and what sold by size (buy % vs. sold %). This variance is the core diagnostic: sizes where sold % consistently exceeds buy % are chronically undersupplied; sizes where buy % exceeds sold % are overweight.
Step 3: In Tab 3, build a recommended buy curve by applying the sold % (not the buy %) as the starting point. This is the key conceptual shift — the curve should reflect demand, not prior buying habits.
Step 4: Review the recommended curve against category conventions and channel signals. DTC size curves should be built from DTC sell-through; wholesale curves from wholesale. Mixing channel data produces a curve that is accurate for neither channel.
Step 5: Add the validated curves to the Curve Library in Tab 1. Assign a Curve ID and document the source and season applied. This library becomes your reference for the buy.
Step 6: In Tab 4, apply curves to each style's planned depth to calculate size-level buy quantities. Cross-check that size quantities sum to planned total units (rounding will introduce small variances — reconcile manually).
When to build separate curves
A single curve per category is rarely sufficient. Common reasons to build separate curves:
By channel: DTC customers self-select size differently than wholesale buyers. DTC curves are typically weighted toward smaller sizes relative to wholesale for women's categories.
By season: Summer fabrications may produce different size performance than winter — especially in categories where layering affects fit perception.
By price tier: Entry-price styles often skew larger than premium styles in the same category. If your assortment has material price tier spread, consider separate curves by tier.
By silhouette: Fitted and relaxed silhouettes in the same category may produce meaningfully different size curves. If you have both in the assortment, evaluate whether a single curve is appropriate.
Template limitations
This template provides a structured framework for building and applying size curves but does not:
- Auto-update curves as in-season sell-through data arrives
- Alert when a style is tracking against an incorrect curve
- Cascade size curve changes through the full buy plan automatically
- Connect size-level buy quantities to vendor order minimums
When size curves need to update in real time and cascade through the assortment and buy plan — especially above 150 styles where manual curve assignment becomes error-prone — a connected planning platform replaces the template.
See how RetailNorthstar builds size curves from actual sell-through history and applies them at buy time — with size-level quantities updating as total depth changes.
Book a Demo →Get the size curve template.
Pre-built size distribution template for apparel buying teams. Input your sell-through history and output size curves by category.
- Size distribution input by category and product type
- Historical sell-through by size with recommended curve output
- Adjustable targets for run sizes vs. fashion styles
- Works alongside your existing buy plan — add size depth from the curve output
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