Case Study: Leading CPG Company boosts market share with Sigmoid's ML-driven recommendation engine

ML-driven recommendations to power real-time sales analytics

Leading CPG Company, a global food, snack, and beverage producer, worked with Sigmoid to address inconsistent sell-out forecasting, basic sell-in order sizing, and the lack of a scalable way to identify whitespaces across 1,100–1,200 retail stores in Columbia. The company needed a standardized approach to recommend which products to stock and in what quantities, while avoiding both understocking and overstocking.

Sigmoid built an ML-driven order recommendation engine using SAP and syndicated CEN data, combining whitespace detection, hybrid product recommendations, and sell-out forecasting to optimize weekly store-level orders. The solution, delivered through a Power BI dashboard, helped Leading CPG Company improve portfolio profitability by 1.5%, increase market share by 2 percentage points, and reduce manual recommendation time by 66%.