Rebuilding Size Curves for a Menswear Brand After Two Seasons of Stock-Outs
After two seasons of stock-outs, a menswear brand’s size curves showed its best-selling sizes as unpopular. Size curve analysis on cleaned sales history, with stock-out weeks and returns removed, produced new curves by category, fit and market.
Menswear Size Curve Challenge After Repeated Stock-Outs
Two seasons of stock-outs had taught the size curves the wrong lesson: sizes that sold out early looked unpopular in the data, so they were bought even thinner next time.
Returns were counted as sales, and one curve was used for every market regardless of local fit preferences.
- Stock-outs recorded as low demand
- Returns counted as sales
- One size curve for all markets
What Was Done to Rebuild the Menswear Size Curves
Menswear Sales History Cleaned of Stock-Outs and Returns
Weeks with broken size runs and returned units removed before any calculation.
Size Curves Recalculated by Market, Category and Fit
Size ratios calculated separately where demand really differs.
Weekly Size Integrity Monitoring
A Power BI view that flags broken size runs while there is still time to react.
Menswear Size Curve Results in Numbers
broken size runs
full-price share
Tools Used in the Menswear Size Curve Project
Excel Size Curve Model
Cleaned history and size ratios by market.
Power BI Size Integrity View
Weekly monitoring of broken size runs.
Related Service: Assortment Planning and Merchandise Planning Consulting
Size and colour curves calculated from cleaned sales history, by category and market, as part of the assortment plan.
A sold-out size is not an unpopular size.
Related Retail Analytics Articles
ABC and XYZ Analysis for Retail Assortment in Excel
How to run ABC analysis by sales contribution and XYZ analysis by demand stability in Excel, and use the matrix for assortment decisions.
How to Build a Range Plan in Fashion Retail
What a fashion range plan contains, the steps from brief to buy, and how options, depth and price tiers fit into one range plan template.
How to Build a Retail Size Curve from Last Season’s Sales Data
How to clean sales data of stock-outs, returns and promotions before calculating size ratios per category, market and store cluster.
Menswear Size Curve Analysis Case Study FAQ
The project took four weeks. Cleaning the sales history took the most care; recalculating the curves and setting up the weekly size integrity view followed.
Two seasons of sales, stock and returns by size, week and market, with category and fit for every product. Weekly stock by size was essential, because it showed which weeks had broken size runs and had to be removed from the history.
Buyers now work from size curves by market, category and fit, calculated on cleaned sales rather than on history distorted by stock-outs and returns. A weekly Power BI view flags broken size runs while there is still time to react.
Retail and Fashion Analytics Case Studies
Standardising Product Data Across European and Asian Markets in a PLM System
A fashion brand selling in Europe and Asia replaced local naming habits with one PLM attribute dictionary and validation at entry.
Rebuilding the Womenswear Range Plan for a European Fashion Distributor
Fewer options, more depth in proven sizes and colours, and a new open-to-buy plan for a womenswear distributor.
Building a Price Architecture and Margin Report for a Fashion E-commerce Retailer
Clear entry, core and top price points per category, one set of pricing rules for all markets and a weekly margin report.