Case Study: a large automotive parts manufacturer achieves more accurate demand forecasting and rationalized inventory with Tredence

a large automotive parts manufacturer improves forecasting accuracy by 10% with Tredence

The client, a large automotive parts manufacturer, faced significant challenges in forecasting long-term demand for its products due to erratic order patterns, unpredictable part failure rates, and obsolescence. Their existing SAP-generated forecasts were inaccurate, leading to both lost revenue from under-forecasting and high costs from inventory pile-up. They partnered with Tredence to develop a more robust and accurate forecasting solution.

Tredence developed a scalable, AI-driven forecasting solution that incorporated product segmentation, material-level models, and ensemble ML techniques to normalize erratic data and account for part obsolescence. This solution beat the existing forecast by an average of 10% accuracy for the majority of materials. As a result, the manufacturer achieved greater than 90% forecast accuracy for parts covering 80% of its sales, leading to recaptured opportunity costs and a significantly rationalized inventory.