Case Study: Leading American Fast-Fashion Retailer achieves 87.5% faster range planning and 3% higher full-price sell-through with Algonomy Assortment Edge

A Leading American Fast-Fashion Retailer Improves Assortment Planning

Leading American Fast-Fashion Retailer, a US chain with 700+ stores selling apparel, accessories and footwear to trend-focused young consumers, faced rising inventory spend and lost sales from a sub‑optimal assortment mix. Assortment planning consumed some 840,000 man‑hours monthly across 300+ product groups, produced a 12% variance between range and financial plans and resulted in large overstock—so the retailer engaged Algonomy and its Assortment Edge solution.

Algonomy deployed Assortment Edge’s AI-driven, one‑click automation (granular forecasting, store clustering, optimized size packs and demand‑led localized assortments), moving the business from manual to algorithmic planning. The implementation cut range planning time by 87.5% (from 4 hours to 30 minutes), saved over 840,000 man‑hours/month, reduced variance between range and financial plans from 12% to 3%, lowered overstock/understock by 4%, delivered a 3% higher full‑price sell‑through and 2% fewer end‑of‑season leftovers, while significantly improving planner productivity.