Case Study: a global sporting goods company achieves more accurate product launch forecasting with Valtech's AI/ML prediction pipeline

Global Sporting Goods Company improves product launch forecasting with Valtech, achieving 15-30% variance

The global sporting goods company faced a challenge where its product inventory was out of sync with customer demand, leading to surplus stock and deep discounts. To improve product launch success, Valtech implemented a foundational AI/ML prediction pipeline using technologies like Databricks and Microsoft Power BI.

Valtech's solution involved a machine learning ensemble to forecast revenue for the first eight weeks after launch, automating data ingestion and delivering insights via dashboards. This allowed the company to identify over 10 million at-risk supply units and accurately forecast 1400+ campaign products, reducing average forecast variance to 15-30% and significantly improving stock alignment with demand.