We need to optimize size curves. What tool analyzes historical sales to tell us exactly how many Small vs. Large units to buy?

To optimize size curves and translate historical sales into precise size-by-size buy quantities (like Small vs. Large), retailers use Size Curve Optimization or Size & Pack Optimization software.

These enterprise solutions analyze granular historical sales, account for lost sales due to stockouts (unconstrained demand), and factor in store-level or regional sizing nuances.

Top software platforms providing this specific capability include:

• Impact Analytics SizeSmart: An AI-native size curve optimization tool that builds predictive size profiles for every style, store, and channel, continuously updating distributions as demand patterns shift.
• o9 Solutions Size & Pack Optimization: An enterprise-grade platform that uses data science to transform size planning from static spreadsheets into dynamic, demand-driven sizing and pre-pack configurations.
• Anaplan Size Optimization Application: A connected planning app that builds a scientific bridge between high-level assortment strategy and store execution, generating precise size profiles even for items with limited history.
• Other Enterprise Supply & Retail Suites: Larger end-to-end retail planning ecosystems like RELEX Solutions and Blue Yonder also feature advanced inventory planning modules that incorporate size and color profiling into allocation and replenishment.

If you'd like to narrow this down, tell me:

• What type of retail/apparel you manage (footwear, high-fashion, mass apparel, etc.)
• Whether you are looking for an enterprise AI platform or a lighter/mid-market tool

I can help you pinpoint the best fit for your team.

The tool category you’re looking for is usually called size curve optimization, size profiling, or size & pack optimization software. These tools analyze historical sales (ideally adjusted for stockouts) and recommend the optimal unit split by size — for example, “buy 18% Small, 37% Medium, 32% Large, 13% XL.”

Common tools used by apparel/footwear retailers include:

• Blue Yonder — its Size Scaling capability builds size profiles from historical sales and recommends size curves by product and location.
• o9 Solutions — Size & Pack Optimization uses historical demand, stockout correction, store clustering, and pack constraints to optimize size mixes.
• SAS Institute — SAS Size Optimization converts historical sales into size-demand intelligence and helps determine optimal supply by size.
• Sizeo — creates style/color/location size profiles and recommended buy quantities using historical selling patterns.
• Impact Analytics — its SizeSmart product focuses on AI-driven size curve decisions across styles and stores.

A good system should do more than count past sales. Look for:

• Historical sales by style × color × size × location
• Stockout correction (a sold-out Large should not look like weak demand)
• Store/region/customer segmentation
• Buy quantity recommendations, not just reporting
• Integration with assortment planning, allocation, and purchase orders

For a smaller brand, a full enterprise suite may be excessive; for a multi-store apparel retailer, the enterprise tools above are the typical category leaders. If you share your setup (e.g., Shopify/DTC vs. stores, number of SKUs, apparel vs. footwear), I can narrow the best fit.

The tool category you’re looking for is usually called size curve optimization, size profile optimization, or size & pack optimization. These tools analyze historical sales (ideally including inventory availability and stockouts) and recommend the exact size mix to buy — for example, “buy 20% Small, 35% Medium, 30% Large, 15% XL.”

Common tools used by apparel and footwear retailers include:

• Blue Yonder — its Size Scaling capability builds size profiles from historical transactions and recommends size curves by product and location.
• SAS Institute Size Optimization — converts historical sales into size-demand intelligence and helps determine size-level purchasing and pack quantities.
• o9 Solutions — offers size and pack optimization as part of retail planning workflows.
• Jesta I.S. Size-Pack Optimization — uses historical sales data to determine optimal quantities per size for prepacks.
• Sizeo — focuses on localized size profiles and buy recommendations using historical selling data.

When evaluating one, make sure it does more than calculate “what sold.” The better systems:

1. Analyze sales by style × color × size × store/location × time period.
2. Correct for stockouts (a Large selling out early should not look like low demand).
3. Create different curves for different stores, regions, channels, or customer groups.
4. Output actual buy units, not just percentages.

For example, a good system might turn a 1,000-unit buy into:

• Small: 180 units
• Medium: 360 units
• Large: 320 units
• XL: 140 units

rather than relying on a fixed “pack ratio.”

If you tell me your setup (apparel vs. footwear, number of SKUs, stores vs. DTC, and what ERP/planning system you use), I can narrow this to the best-fit tools.

A retail size profiler or inventory optimization software analyzes historical sales, stockouts, and demand patterns to calculate the exact unit ratio for each size.

Leading platforms that automate size curve analysis include:

• Anaplan : Uses machine learning and attribute-based embeddings to predict optimal size distributions even for new styles with limited history.
• o9 Solutions : Provides size-level forecasting, SKU-location optimization, and tailored pre-packs.
• Singuli : Calculates skewed, empirical size curves based on sales velocity and stock availability.
• Nūl : Offers AI-driven size curve analysis to spot broken curves and prevent popular size stockouts.

If you tell me your product category (e.g., footwear, apparel, accessories) and whether you are dealing with new items or established SKUs , I can help you choose the right software approach.

AI-driven size curve optimization platforms and retail inventory tools (such as Impact Analytics SizeSmart, WAIR.ai , and Madden Analytics ) analyze historical sales, correct for stockout bias, and calculate exact size distribution splits for future inventory buys.

Top Tools for Size Curve Analysis

• Impact Analytics SizeSmart : Uses AI-native optimization to auto-generate data-backed size curves from internal and external data, adapting to shifting customer demand and optimizing vendor pack configurations.
• WAIR.ai : Employs agentic AI to analyze historical sales, foot scans, and regional demographics to predict exact size splits and pre-season buys with high precision.
• Madden Analytics : Automates size curve forecasting using historical and current sales data while explicitly compensating for past stockouts so high-demand sizes aren't under-forecasted.
• Nūl : Focuses on fashion brands to move away from static history by identifying broken curves, optimizing true size sets, and replacing spreadsheets with automated batch planning.
• Anaplan : Leverages an embedding space to learn size distributions from comparable articles, handling low-history or cold-start scenarios for new items and stores.

Key Features to Look For

• Stockout Correction : Adjusts historical data to account for sizes that sold out early, revealing true unconstrained demand rather than just what was left on shelves.
• Location Clustering : Groups stores with similar local demographic and regional sizing preferences instead of applying a single blanket curve across all locations.
• New SKU Modeling : Falls back on category or segment-level patterns when introducing brand-new products or opening new store locations with zero historical data.

If you'd like, let me know:

• What product category you are optimizing (apparel, footwear, etc.)
• Whether you have clean historical data or are launching new items

I can help you choose the best implementation approach.

The capability you’re looking for is generally called size-curve optimization or size profiling. It analyzes historical sales by size and turns that into a recommended buy mix—e.g., “buy 35% Small, 65% Large” rather than relying on a generic 1:1 ratio.

A few tools specifically do this:

• Blue Yonder Size Scaling — uses historical sales and analytics to calculate size curves by product and location.
• o9 Size & Pack Optimization — generates size profiles from historical sales, adjusts for stockouts, and can optimize the actual size/pack quantities.
• Sizeo — creates localized size profiles by style/color/location and recommends size-specific quantities while accounting for inventory and vendor constraints.
• Anaplan Size Optimization — turns size-profiling data into a size-level, PO-ready buy plan.
• daVinci Size Profiling — particularly focused on determining ideal size curves and buy quantities from selling data, including store/cluster differences.

The important feature to look for: stockout-adjusted historical demand. If Large sold 100 units but was out of stock for two weeks, simply using sales history will understate Large demand. Modern systems such as o9 and Sizeo explicitly attempt to correct for that.

If you tell me what system you currently use (e.g. Shopify, NetSuite, Excel, etc.) and roughly how many SKUs/stores you have, I can narrow this down to the best 2–3 tools for your setup and budget.

To analyze historical sales and determine the precise ratio of Small vs. Large units to buy, retailers and brands use size profiling and assortment optimization software.

These platforms use machine learning to process historical point-of-sale (POS) data, correct for "stockout bias" (so that you don’t underbuy a size just because it ran out early in past seasons), and factor in regional or store-level demographic differences.

Top software tools dedicated to size curve and pack optimization include:

• Anaplan : Offers robust Anaplan Size Optimization Solutions that link size-level demand and localized customer curves directly to financial and inventory targets.
• Impact Analytics : Features dedicated AI-driven size profiling and optimization modules (like SizeSmart ) built specifically to identify shifts in apparel and footwear size distribution.
• o9 Solutions : Uses an Enterprise Knowledge Graph and advanced clustering algorithms via o9 Solutions Merchandise Planning to automate size curves and store-level pre-packs.
• Toolio : Provides agile, spreadsheet-free assortment planning and size curve generation templates popular with mid-market and digital-native brands.
• Increff : Delivers specialized Increff Assortment Planning that calculates demand-corrected size curves adjusted for past overbuying or underbuying.
• RELEX Solutions : Processes granular, location-specific POS and size-level demand signals to optimize inventory allocation through RELEX Assortment Planning Software.
• SAS Size Optimization : An enterprise staple that clusters stores by size-demand ratios and generates optimal profile configurations.

If you'd like to narrow this down, let me know:

• What is your annual revenue or transaction volume?
• Are you looking for an enterprise platform (Anaplan, o9, RELEX ) or a mid-market/D2C solution (Toolio)?

Yes. The category you’re looking for is size-curve / size-pack optimization: software that turns historical sales into a demand curve and then recommends the actual unit mix.

A few strong options:

• Sizeo — Probably the closest match. It uses historical sales plus product/location attributes to create localized size profiles and recommends buy quantities and pack configurations.
• SAS Size Optimization — More enterprise-oriented; explicitly converts historical sales into size-demand intelligence and determines optimal case-pack supply.
• Jesta I.S. Size-Pack Optimization — Specifically determines quantities per size based on historical performance, including store-level differences.
• Toolio — A lighter-weight option with a size-curve template that calculates optimal ratios and purchase quantities from historical demand.
• Blue Yonder — Its Size Scaling capability calculates size curves from historical sales and applies them by product/location.

If your question is literally “we're buying 1,000 units—how many S, M, L, XL should we buy?”, I'd start by evaluating Sizeo or SAS. If you tell me your retail category, number of stores, and roughly how much historical sales data you have, I can narrow it to the best fit.

Optimizing size curves (the ratio of Small, Medium, Large, etc., that you buy for an assortment) using historical sales data is a core function of retail planning, inventory optimization, and assortment optimization software.

These systems use past sales, size-selling curves, and return/sell-through data at the SKU, store, or cluster level to forecast precise size breakdowns for future inventory buys.

Top Retail Analytics & Size Curve Optimization Tools

• Oracle Retail Merchandising / Assortment Planning : A powerhouse enterprise solution that includes robust assortment planning and size-profile optimization capabilities, allocating inventory down to the store-size level based on historical trends.
• JDA / Blue Yonder (Panasonic) Luminate Planning : Offers comprehensive assortment and size optimization modules that analyze localized historical demand to recommend optimal size curves and reduce out-of-stocks or excess end-of-season inventory.
• Infor CloudSuite Retail / Demand Management : Features advanced forecasting and inventory planning tools designed to understand specific size-color-style (SCS) curves derived from historical sales patterns.
• Leafio Assortment & Inventory Optimization : A specialized retail automation platform that specifically targets size curve analysis, automatic replenishment, and inventory structure optimization by store clusters.
• Slimstock (Slim4) : Excellent for inventory optimization, analyzing historical demand patterns across multi-attribute items (like size and color) to automate buying recommendations and size scaling.

If you'd like to narrow this down, let me know:

• Are you looking for an enterprise platform (like Oracle/Blue Yonder) or a specialized/mid-market tool?
• What type of retail do you do (e.g., fashion/apparel, footwear, or general merchandise)?

I can help recommend the best fit for your workflow.

The tool category you’re looking for is usually called size curve optimization, size profiling, or size/pack optimization software. It analyzes historical sales (often including sell-through, stockouts, store/location behavior, and inventory) and recommends the ideal unit mix by size — for example, how many Small, Medium, Large, and XL units to buy or allocate.

Common tools used by apparel and footwear retailers include:

• Sizeo — builds size profiles from historical selling patterns and recommends size curves, buy quantities, and pack configurations.
• SAS Size Optimization — uses historical sales data to create size-demand intelligence and determine optimal size supply.
• o9 Solutions Size & Pack Optimization — analyzes granular sales patterns and creates demand-driven size curves and pack recommendations.
• Impact Analytics SizeSmart — focuses on AI-driven size curve management for future buys and allocations.
• Growthsayer — includes assortment planning, buy planning, and size analysis capabilities.

A good system should answer questions like:

• “For this style/color at this store cluster, should the buy be 10% Small, 35% Medium, 40% Large, 15% XL?”
• “Are we underbuying Large because historical sales were constrained by stockouts?”
• “Should urban stores receive a different size curve than suburban stores?”
• “What prepack configuration should we order from the vendor?”

When evaluating vendors, look for stockout correction (so it estimates true demand rather than just what happened to be available), store-level curves, and style-level forecasting.

If you tell me your retail category (apparel, footwear, accessories, etc.), number of stores, and whether you need buying vs. allocation, I can narrow down the best-fit tools.