Overview
Retailer Achieves Major Cost Savings with ML-Based Demand Forecasting.
Location: India
Introduction: A large retailer with a massive product inventory across extensive distribution networks was seeking to optimize its supply chain and reduce costs.
Challenge
Accurately predicting demand for over 15,000 SKUs distributed through more than 200 distinct points, leading to significant stockouts and high inventory carrying costs.
Solution
We built a sophisticated Machine Learning-based demand forecasting system. This system integrates multiple critical data streams, including historical data, weather patterns, local and national events, and promotional data.
Impact: The system improved forecast accuracy by 35%, leading to a direct reduction in stockouts of 40% and a decrease in inventory carrying costs of $8M annually.