Forum Energy Technologies · Manufacturing · Midland, TX · 20 weeks
Demand forecasting AI deployed across 3 product lines
An oil and gas equipment manufacturer relied on 12-month rolling averages for demand forecasting, causing frequent overstock on slow-moving SKUs and stockouts on fast movers. We developed an AI-driven forecasting model that improved accuracy, lowering inventory carrying costs by $1.8M per year and reducing stockouts from 34 to 4 each quarter.
Challenge
The manufacturer held $28M in inventory over three product lines, relying on a 12-month rolling average to forecast demand. This approach ignored oil price fluctuations, drilling permits, and seasonal shifts. As a result, slow movers piled up excess stock while fast movers ran out 34 times per quarter. Each stockout cost roughly $42K in lost sales and rush shipping.
Outcome
We cut inventory carrying costs by $1.8 million a year by adjusting safety stock levels based on demand patterns. Stockouts dropped sharply from 34 to 4 per quarter. We lowered the inventory balance from $28 million to $21.4 million without increasing stockout risk. The new forecasting model improved 12-week accuracy to 84%, up from 61% using the old rolling average approach.
Results
- $1.8M Annual inventory carrying cost saved
- 34 to 4 Quarterly stockout incidents
- $6.6M Inventory balance reduction
- 61 correct of 100 to 84 correct of 100 forecasts 12-week forecast accuracy
We had $28 million stuck in inventory but still kept running out of the parts customers needed. Thinklytics built a model using oil prices and drilling permits to predict demand. Since then, we’ve cut stockouts from 34 down to 4 per quarter and freed up $6.6 million in working capital.