Case Study: a major automotive parts manufacturer boosts demand forecast accuracy and rationalizes inventory with Tredence

Demand Forecasting for Automotive Parts Allowing for Revenue Gain and Rationalized Inventory

The client, a major automotive parts manufacturer, faced the challenge of creating long-term demand forecasts for parts with unpredictable failure rates. Their previous method, based on business heuristics, led to significant revenue loss from under-forecasting and high costs from over-forecasting and inventory pile-up. They engaged Tredence to develop an improved forecasting solution.

Tredence implemented a robust analytical solution using product segmentation, material-level forecasting, and champion-challenger ML models that incorporated external data to account for part obsolescence. This approach achieved over 90% forecast accuracy for parts covering 80% of sales, beating the existing forecast accuracy by an average of 10% for the majority of materials. The result was a significant recapture of opportunity cost and rationalized inventory for the client.