Case Study: a leading American consumer health company achieves more accurate category-level demand forecasting with Sigmoid

Leading American Consumer Health Company reduces forecast error by up to 80% with Sigmoid

The customer, a leading American consumer health company, faced challenges with its manual, spreadsheet-based demand forecasting process for its OTC products. This inefficient method was prone to errors and subjectivity, leading to inaccurate sales and inventory forecasts. Sigmoid was engaged to implement an automated, data-driven solution to address this.

Sigmoid implemented a customized demand forecasting solution using advanced ML algorithms. Their approach automated data pipelines to ingest and process large volumes of internal and external data, generating weekly category-level forecasts. This solution from Sigmoid significantly improved forecasting accuracy, reducing MAPEs by up to 80% and saving planners 50% of their time, leading to greater efficiency.