Data science redused the work intensity for 10%.
Utilizing data science techniques for demand forecasting streamlines warehouse functions and product flow for the client.
Baseline situation: A prominent sports equipment retailer, operating nearly 200 physical stores and an online shop, needed to anticipate demand for products significantly affected by various factors, particularly weather changes, which complicated their warehouse management.
Our approach: Revolt BI developed a predictive model that accurately forecasts the sales turnover of specific products at the SKU level across all locations and online, achieving 80-90% accuracy. This enables the optimization of intralogistics processes, especially in stock placement, order picking, and preparing for increases in demand.
Achieved success: Through effective demand forecasting, intralogistics operations were optimized, leading to a reduction in the time required for picking goods, improved planning of warehouse resources, and a 10% decrease in overall labor intensity of warehouse operations.