Demand Intelligence Cuts Inventory 20% While Improving Forecast Accuracy

Client: Consumer Product Manufacturer  |  Industry: Consumer Packaged Goods / Manufacturing  |  Services: Data Intelligence, Productivity Improvement

The Situation

This consumer product manufacturer operated in a high-promotion environment. Hundreds of SKUs cycled through promotional pricing on different schedules for different retail partners, and every promotion created a demand spike that the client's forecasting process couldn't handle.

The result was chronic forecast errors that rippled through the entire supply chain. At 65% forecast accuracy, the company was guessing wrong more than a third of the time — causing missed service targets, strained retailer relationships, and excess inventory that tied up working capital and warehouse space. Management knew the problem lived in the intersection of demand variability and forecasting methodology, but lacked the analytical tools to identify which customers, SKUs, and promotional patterns were driving the worst errors.

What ADecTec Delivered

ADecTec approached the problem as a data intelligence challenge, not a forecasting software problem. Our team conducted a deep segmentation analysis to identify which customer–SKU combinations were introducing the greatest variability into the demand signal. The analysis revealed that a relatively small number of high-promotion, high-variability segments were responsible for a disproportionate share of the forecast error.

Armed with this insight, ADecTec helped the client establish collaborative forecasting practices with the specific customers driving the highest errors — sharing promotional calendars, point-of-sale data, and demand expectations directly with key accounts to create a closed-loop forecasting process that captured demand signals algorithms alone missed. Simultaneously, we redesigned inventory policies to reflect segmented demand variability: high-variability SKUs received different safety stock calculations, replenishment triggers, and review frequencies than stable-demand products.