AI-powered demand forecasting improves order planning efficiency by 30%

Sigmoid helped a global FMCG company improve demand forecasting accuracy to reduce inventory imbalances, costly air-freight shipments, and supply chain costs across LATAM.

Business Scenario

A leading global FMCG company faced inefficiencies in concentrate flow planning in LATAM due to discrepancies between Frozen Forecasts and actual bottler orders. These variations led to inventory imbalances, forcing bottlers to hold excess stock at higher costs or rely on costly air-freight shipments. The demand planning process depended on traditional forecasting and manual interventions, with frequent revisions due to forecast deviations. Lack of centralized visibility led to siloed operations, making real-time adjustments difficult.

Sigmoid Solution

Sigmoid developed an ML-based forecasting model to enhance demand planning accuracy, reduce inventory imbalances, and minimize air-freight costs. The solution leveraged AI-driven forecasting techniques to generate data-backed purchase order recommendations, replacing static frozen forecasts with a dynamic, real-time planning approach. Built on Microsoft Azure, the solution included a React-based web portal for forecasting and order recommendations.