Case Study: Leading Global Consumer Goods Company improves new product forecast accuracy with e2open

How one company streamlined the forecasting process for successful new product launches

The Leading Global Consumer Goods Company needed to improve new product introduction (NPI) forecasting, as its manual, spreadsheet-based process consistently overestimated demand and produced much higher error than its mature-product forecasts. It partnered with e2open and used E2open Demand Sensing to address the challenge of forecasting products with no demand history.

e2open implemented AI-driven clustering and pattern matching to group new items with similar existing products, then generated forecasts using historical demand signals and automated feature engineering. The result was a 10–34% improvement in new product MAPE and a 32–59% reduction in forecast bias, helping improve planning accuracy and reduce human bias in the forecasting process.