Size Curve Optimisation

A size curve (also: size profile, size distribution, size run) is the percentage breakdown of customer demand across sizes within a single style-colour combination. Given a total style-colour volume forecast, the size curve disaggregates it to unit quantities per size — if size M is expected to represent 30% of demand, 30% of the buy is allocated to M. Getting the curve wrong is a direct path to two simultaneous failures: stockouts in popular sizes and unsellable excess in fringe sizes, both hitting margin at season end.

How size curves are built

Impact Analytics (2026) describes the standard two-stage method: first, forecast total demand at style/colour level; second, disaggregate to size using a size curve expressed as percentage contributions summing to 100%. o9 Solutions (2024) outlines three phases: (1) historical analysis — aggregate past sales by brand, colour, and fit attributes, then adjust for stockout-driven lost sales; (2) profile application — link each new product to an appropriate size profile and apply the disaggregation; (3) seasonal post-game — refine profiles after each selling season.

Impact Analytics (2026) cites Nike as an example: if size Small sells 10% of volume for a given style/variety, the size curve sets the Small allocation to 10% of the total buy.

Practitioners on r/fashionbuying (2024, 56 upvotes) describe the industry baseline as Excel plus prior-year sell-through: "Excel + prior year sell-through is the industry standard for 90% of retailers, regardless of size. I've worked at three retailers over 15 years, two of them with > £500M revenue, and size curves lived in Excel at all of them. The enterprise planning tools do have size allocation modules but they're often the least mature part of the suite."

Input data quality matters critically. Eightx (2026-06-08) recommends building curves from "clean weeks only" — weeks when every size was in stock and nothing was on markdown — because promotional and stocked-out weeks distort the curve and bias it toward whatever happened to be available (as-of 2026-06-08).

Fringe size treatment. Eightx (2026-06-08) advises buying fringe sizes (XS, XXL) shallower than even a sell-through-weighted curve suggests, because the cost of stranding fringe units into a 50% markdown exceeds the cost of a fringe-size stockout for most retailers.

Curve library approaches

Practitioners on r/fashionbuying (2024, 49 upvotes) report that category-segmented curves — separate pre-built curves for basics, trend/fashion-forward, activewear, and occasionwear — outperform a single generic house curve in backtests. r/supplychain practitioners (2024, 41 upvotes) describe building 8–12 canonical size curves from historical data, clustered by product type, price tier, and trend vs. basic, then classifying each new style into the appropriate cluster at buy time. The four strongest predictors in order: (1) garment type/silhouette, (2) target customer segment, (3) price tier, (4) trend score.

The censored demand problem

The most structurally important failure mode in size curve building is demand censoring: when a size sells out mid-season, the lost sales disappear from the data entirely and register as a clean sell-through. r/supplychain (2024, 54 upvotes) describes this directly:

"The deeper issue is that observed size sell-through is a biased signal. If you run out of size M first, your sell-through data shows M at 100% and L at 70% — but the true demand for L was probably higher. You need to do censored demand estimation before feeding that into a size curve model. Most retailers skip this step and their size curves systematically under-buy the middle sizes as a result."

This creates a self-reinforcing bias (r/supplychain, 2024, 31 upvotes): under-buy M → M sells out → record lower demand signal for M → under-buy M again. A practitioner on r/fashionbusiness (2024, 45 upvotes) describes the standard correction: for any size that hit zero stock before season end, estimate uncensored demand by projecting the pre-stockout velocity over the full season.

o9 Solutions (2024) explicitly calls out the same correction: historical sales data must be "rectified" to estimate lost sales during observed stockouts before building the size curve.

Returns create an inverse censoring problem for ecommerce. r/supplychain (2024, 28 upvotes) notes that larger sizes return at higher rates in fashion ecommerce, so the net demand signal for XL is artificially suppressed compared to true purchase intent. r/supplychain (2025, 24 upvotes) identifies the practical barrier: "Our planning system sees net sales, not gross purchases. The returns signal lives in a different system. The 'obvious' fix requires a data integration project that's been deprioritised for 2 years."

Why size curves fail

1. Geographic / store-level aggregation error

o9 Solutions (2024) identifies applying a single national size curve across all stores as the most common error: size performance shifts between stores due to demographic, regional, and cultural factors — urban locations with younger demographics may favour smaller sizes while regional centres require a broader range. StyleMatrix (2026-02-02) describes AI-driven store-level curve differentiation as the primary value proposition.

2. Channel aggregation error (stores vs ecommerce)

r/supplychain (2025, 51 upvotes) reports that ecommerce skews toward extreme sizes vs stores: "Effects can push online and in-store size curves 10–15 percentage points apart on the extremes." The explanation: in-store, fit anxiety is mitigated by trying on; online, customers buy 'safe' sizes or multi-size orders. Impact Analytics (2026) adds that physical stores are migrating toward smaller size bands faster than digital channels, and most planning systems aggregate both channels, diluting the shift signal.

3. Pre-pack constraints

WAIR.ai (2025-09-12) identifies pre-pack constraints as a common operational failure: suppliers sell products in pre-determined size assortments that rarely match the specific demand of an individual store, forcing retailers to accept the imbalance or pay for more expensive loose packs.

4. Vanity sizing drift

r/supplychain (2024, 58 upvotes): "The dirty secret of ML size curve tools: they're only as good as your size labeling consistency. We had years where 'S' in 2019 mapped to different measurements than 'S' in 2023 (vanity sizing drift). The vendor's model just ingested that confusion and produced garbage. Data harmonization before ML is never in the sales pitch. If you've ever re-graded your size charts, your historical data has a structural break models treat as noise."

5. GLP-1 body composition shift (2025–2026)

Impact Analytics (2026-06-12) reports that GLP-1 drug adoption is compressing the timeline during which body-level demand changes occur — planning systems were built on the assumption that body-level demand changes slowly. Their analysis estimates over 400 million apparel units at risk of misalignment, with over $5 billion in inventory value and margin exposure if retailers plan against pre-GLP-1 historical assumptions (as-of 2026-06-12). A single percentage point decline in large size share shifts roughly 120 million units annually.

[!unverified] The 400 million units and $5 billion figures are from Impact Analytics' proprietary analysis (vendor source, 2026-06-12); no independent verification was found.

6. Plus-size chronic under-buying

r/fashionbusiness (2024, 87 upvotes): "Extended size customers learn items sell out quickly, so they stop browsing. If they bounce without buying, that registers as low demand when it's actually low confidence in availability." This is a demand censoring problem compounded by customer behavioural adaptation.

7. New style cold-start

When a style has no sales history, there is no historical curve to apply. This is the "cold-start problem" — covered in detail in New Product Forecasting. Springer Nature peer-reviewed research (2025) identifies it as the primary remaining limitation of conventional forecasting in fashion.

Fit type as the dominant predictor

r/fashionbuying (2024, 38 upvotes) identifies fit type (slim, regular, relaxed, oversized) as the strongest predictor of size curve shape:

"Oversized styles skew small because customers size down deliberately. An oversized hoodie and an oversized coat will have similar size curve shapes even though they're different products."

This is confirmed in a separate thread (31 upvotes). Fit type is listed as attribute #1 in the curve clustering hierarchy ahead of garment type.

AI/ML approaches

Two-stage compositional model: r/supplychain (2024, 67 upvotes) describes the technically sound approach: (1) forecast total style demand independently; (2) use Dirichlet regression on the size proportions conditioned on style attributes. "It's more internally consistent than treating sizes as independent" — sizes are compositional (must sum to 100%), so independence assumptions are structurally wrong.

Embedding-based cold-start: r/fashionbuying (2024, 27 upvotes) describes a zero-shot approach: encode style attributes as embeddings using a fine-tuned sentence transformer on product descriptions plus CLIP visual embeddings from product images, then find nearest-neighbour historical styles and use their size curves as priors. Text and image embeddings combined outperformed either alone.

AI vendor tools: StyleMatrix (2025-12-28) describes ML models that factor in sales velocity, seasonality, and regional buying patterns, recalibrating curves at each product launch via continuous updates. o9 Solutions (2024) describes automated size curve generation, store clustering, and pack optimisation. WAIR.ai (2025-09-12) describes "Forecast-GPT" AI and a "Wallie" allocator agent for automation.

In-season demand sensing vs pre-season ML: r/supplychain (2024, 37 upvotes) reports that early-season sell-through data by size, used to update the planned size curve, was more valuable than any pre-season ML model. This aligns with the Inditex/Zara model below.

Realistic ML performance: r/supplychain (2024, 71 upvotes): "ML size curve vendor deployment: ~12% reduction in size-driven stockouts, ~8% reduction in size-related markdowns. Not the 30-40% the vendor pitched. Biggest gains on styles with strong comparable history. For genuinely new silhouettes, it defaulted to a generic curve and was no better than manual."

The Zara model vs mid-market reality

r/fashionbusiness (2024, 134 upvotes) documents the Inditex approach:

"Zara doesn't pre-optimize the size ratio at initial buy. They buy conservatively (~60% of expected demand), ship small quantities, observe size sell-through in weeks 1–4, then issue a follow-on buy adjusted for the actual size distribution. The fast fashion model changes size optimization from pre-season prediction to in-season adaptation."

The enabling constraint: Zara's vertical integration and 2–3 week supplier response times. r/fashionbusiness (2024, 72 upvotes): "Most brands have 12–16 week lead times from Asian suppliers. Zara solves the size problem with speed; everyone else has to solve it with better prediction."

Mid-market options (r/fashionbusiness, 2024, 44 upvotes): (1) better pre-season curve prediction, (2) safety stock planning to buffer size-level uncertainty (see Safety Stock Optimisation), (3) store transfer programs to rebalance in-season without new buys.

Channel-specific vs unified curves

r/supplychain practitioner (2025, 33 upvotes) reports moving to channel-specific size curves produced 9% online stockout reduction and 7% in-store reduction vs shared curves. [https://www.reddit.com/r/supplychain/comments/1jk8wq3/]

An opposing practitioner (27 upvotes) in the same thread argues: "Channel-specific curves only make sense if inventory pools are siloed. Unified inventory with store fulfillment = single blended curve probably makes more sense." Both sides agree the right answer depends on the inventory architecture. No consensus on the general rule.

Business impact benchmarks

Metric	Figure	Source	Confidence
Global retail loss from inventory-demand mismatch (incl. size)	$1 trillion/year (as-of 2024)	IHL Group via o9 (2024-11)	med (vendor-cited secondary)
End-of-season markdown exposure from size curve errors	30–40%	r/supplychain (2024-09, 29 upvotes)	med (practitioner estimate)
Conversion drop for styles with 3+ sizes OOS	–22% vs full-size-run (as-of 2025-05)	r/ecommerce (2025-05, 31 upvotes)	med (AB test, practitioner-reported)
Gross margin improvement from size precision	130–210 bps (as-of 2026-06)	Impact Analytics (2026-06-12)	low-med (vendor analysis, unverified)
Inventory holding cost reduction from accurate curves	"up to 25%" (as-of 2026-02)	StyleMatrix (2026-02-02)	low (vendor claim, no source cited)
Apparel operating margin range (public companies)	4.08% (AEO) to 19.9% (Lululemon) (as-of 2026)	Eightx / SEC 10-K filings (2026-06-08)	high
Apparel industry inventory turns	6.78× TTM Q1 2026 (as-of Q1 2026)	CSIMarket via Eightx (2026-06-08)	high
Industry apparel overstock (2023)	20–40% of total production	Eightx (2026-06-08, secondary)	low (original source not cited)

Vendor landscape

Vendor	Positioning	Notes
True Fit	62.7% sub-segment share, size-and-fit engine (as-of unknown date)	Fit Hub GenAI announced 2024-06; original market share source not identified
Nextail	"Most mature size curve capability among newer players" (practitioner view)	r/fashionbuying (2024-11, 31 upvotes); data integration from ERP takes ~4 months
o9 Solutions	Size Profiler module; graph neural network; automated store clustering and pack optimisation	Vendor source (2024-11); 9–12 month deployment timeline (practitioner)
Impact Analytics	"SizeSmart" product and "Sizing as a Service" consulting	Vendor source (2026-06-12)
StyleMatrix	Store-level size curve analytics; mid-season real-time adjustment	Vendor source (2026-02-02); Australia-based
WAIR.ai	Agentic AI for size curve; "Forecast-GPT" and "Wallie" allocator agent	Vendor source (2025-09-12)
Blue Yonder	Broad supply chain platform; size curve as one capability among many	27B AI predictions/day claimed; Gartner MQ Leader (as-of 2026)
Aptos	Named in practitioner discussion; specific size curve capabilities not retrieved	r/fashionbuying (2024-11)

Key terms

Term	Meaning
Size curve / size profile / size run	The percentage distribution of demand across sizes for a style-colour combination
Broken sizes	When in-demand sizes sell out and the size range is no longer complete
Demand censoring	Unmet demand invisible in sell-through data because a size stocked out before period end
Pre-pack	Supplier-defined multi-size bundle that retailers must accept as-is
Compositional forecasting	Forecasting a set of proportions that must sum to 100% (Dirichlet regression is one approach)
Clean weeks	Weeks in which all sizes were in-stock and no markdowns were active — the only unbiased input for size curve building
Fit type	Garment silhouette fit category (slim/regular/relaxed/oversized); strongest single predictor of size curve shape

What practitioners report

r/fashionbuying (2024): "What separates good buyers from bad ones is whether they adjust for (1) the season's trend direction, (2) any fit changes in the style (a tighter fit shifts the curve smaller), (3) whether last year's curve was distorted by a stockout." (38 upvotes)
r/fashionbuying (2024): Documenting implicit rules ("for this brand, fitted denim always skews S/M; casualwear skews M/L") as a curve library is "worth more than an ML model you can't explain to a buyer." Knowledge transfer when senior buyers retire is flagged as a structural risk. (20 upvotes)
r/supplychain (2024): ML tools show best gains on styles with strong comparable history; for genuinely new silhouettes, they default to a generic curve and perform no better than manual. (71 upvotes)

Gaps (from this harvest)

No independent academic/government quantification of size curve accuracy improvement from AI vs rule-based methods
RELEX and ToolsGroup specific size curve capabilities not retrieved
Fit Analytics/Snap current product state post-acquisition unverified
No practitioner-reported case of returns data successfully integrated into live size curve workflow
Numeric sizing (EU 36/38/40; denim 28/30/32) not covered — different distribution shape from alpha sizing
Colour-size interaction not covered (whether black vs cream colourways of the same style require different curves)
Country/channel size variation not quantified (e.g. size M in Germany vs Japan)
Marketplace/multi-brand contexts (Zalando, ASOS) not covered

Frontier links

Demand Sensing · Size Curve Optimisation by Channel · Inventory Optimisation Software · Dirichlet Regression (Compositional Forecasting) · Pre-Pack Planning · Store Transfer Optimisation · Fit Analytics (Snap) · Nextail · GLP-1 Effect on Apparel Demand · Colour-Size Interaction in Demand Planning