Case Study: Leading Top 10 Retailer improves forecast accuracy by 600 BPS with Tredence

Improving Demand Forecast Accuracy by 600 Bps for a Top 10 Retailer Using Machine Learning

Leading Top 10 Retailer Company, a top 10 retailer, worked with Tredence to address major demand planning challenges. Its 50+ person FP&A team relied on manual, spreadsheet-based forecasting processes that were subjective, time-consuming, and prone to error, leading to high MAPE, forecast accuracy below 90%, and limited visibility across categories and channels.

Tredence implemented a machine learning-based demand forecasting and causal analytics solution using ensemble models, customized by item type, along with anomaly detection and automated alerts. The approach created over 10,000 models, improved forecast accuracy by more than 600 basis points, reduced planner forecasting time by over 50%, and delivered a 6% improvement in inventory costs for seasonal products, with strong adoption across planning teams.