There’s an everlasting discussion in retail about whether to include edge sizes such as 3XS/ XXS, and XXL/3XL — in what store listing, at what quantities and which ones to carry in the next season’s assortment. While these sizes are essential for offering a complete and inclusive range, they often come with financial risk.
Retailers frequently overestimate demand, which leads to unsold inventory, deeper markdowns, and weaker margins. Edge sizes tend to sell more slowly and are often left behind at the end of a season, resulting in inventory write-downs, increased storage costs, and lost profit opportunities.
Edge sizes can be tricky in assortment planning — they don’t sell in high volumes, but they still matter. Sales data often shows that these sizes move slowly, making it hard to justify ordering large quantities. At the same time, not including them can disappoint loyal customers who expect a full size range. As profit margins get tighter, many suppliers choose to drop edge sizes to avoid overstock. This puts retailers in a tough spot: ordering too few can lead to lost sales and unhappy customers, while ordering too many increases the chance of markdowns, leftover stock, and lower profits.
QL Assortment helps retailers optimize purchasing and inventory management for edge sizes by providing precise, actionable sales insights. The system highlights which edge sizes are selling, when those sales occur, and whether they happen at full price or during markdowns. This is critical because sizes that mostly sell late in the season or only on discount often indicate weaker demand than the raw numbers suggest. With this insight, retailers can adjust their buy volumes to avoid overstock and unnecessary losses.
Going further, QL Assortment provides concrete recommendations on how many units of each edge size to buy, based on historical sales patterns. It takes into account key metrics such as actual sales and trends at both the SKU and category level, current and recommended purchase quantities, planned inventory, available stock, open orders, committed blocks, and estimated inventory at season start. This makes it easy to see how past stock matched demand and identify potential gaps.
By turning gut feeling into data-driven decisions, retailers can better forecast demand, reduce excess stock, minimize markdowns, and protect margins. Many have already lowered overstock by adjusting purchases to actual sales patterns, avoiding unnecessary losses and improving negotiation power with suppliers.
At the same time, QL Assortment helps keep niche sizes available without tying up capital in slow-moving inventory. With insights into how and when different sizes sell, retailers can balance inclusivity with smart inventory planning, maintain customer satisfaction, and drive long-term growth. By making data-driven decisions, retailers not only optimize their assortments but also strengthen brand trust and ensure a more resilient, profitable business for the seasons ahead.