Case Study: UK’s Leading Fashion Retailer achieves 8% higher product availability with Mu Sigma’s demand forecasting solution

Improved labor forecasting and planning for a large retailer

Mu Sigma helped the UK’s Leading Fashion Retailer improve demand forecasting and inventory planning for its online channel across apparel and home furnishing products. The retailer struggled with stock availability because many fashion items had short shelf lives, seasonal demand was hard to predict, and new products had little or no historical sales data, making gut-based planning unreliable.

Mu Sigma implemented an automated, SAS-based forecasting and replenishment solution that used regression, conjoint analysis, cluster analysis, and business rules to identify “look alike” SKUs and estimate both seasonal demand and early-season launch quantities. The new process improved product availability by 8%, helped avoid more than £1 million in annual lost sales, and created a repeatable forecasting process for 6,000–8,000 SKUs while improving collaboration across internal teams.