Fashion Merchandising Optimization
Fashion merchandising optimization uses data-driven models to improve decisions across design, assortment, buying, pricing, allocation, and replenishment in fashion retail. It connects demand forecasting with assortment planning and inventory decisions so brands put the right styles, sizes, and quantities in the right channels and locations. The goal is to reduce guesswork that traditionally relies on intuition, trend-spotting, and manual spreadsheets. This application matters because fashion is highly seasonal, trend-sensitive, and prone to overstock, markdowns, and missed sales due to stockouts. By predicting demand at granular levels (SKU, store, region, channel) and automating routine decisions such as tagging, pricing, and recommendations, retailers can cut waste, improve margins, and speed time-to-market for new collections. It also enables large-scale personalization of shopping experiences, aligning merchandising decisions with individual customer preferences across online and offline touchpoints.
The Problem
“You’re guessing demand, so markdowns rise while customers hit stockouts in key sizes”
Organizations face these key challenges:
Merch plans and buys are built in spreadsheets with stale data; by the time approvals land, demand has shifted
Inventory is imbalanced across channels (DC vs stores vs e-com) and sizes; best sellers stock out while slow movers pile up
Markdown and promotion decisions are reactive and inconsistent by region/store, eroding margin and brand price integrity