A multi-location retail chain partnered with us to implement an AI-powered demand forecasting solution aimed at improving inventory planning, reducing stock inefficiencies, and enabling data-driven decision-making. Existing forecasting methods relied heavily on historical averages and manual inputs, leading to frequent stockouts and excess inventory. Our goal was to build a scalable, intelligent forecasting system that could adapt to seasonality, promotions, and changing customer demand patterns.
Business Challenge:
1. Inaccurate demand forecasts causing overstock and lost sales 2. Limited ability to account for seasonality, promotions, and regional trends 3. Manual, time-consuming forecasting processes prone to error 4. Lack of real-time insights for inventory and replenishment planning
Our Approach:
We designed and delivered an end-to-end AI demand forecasting solution tailored for retail operations: 1. Data Engineering: Consolidated sales, promotions, pricing, and external data into a unified forecasting pipeline 2. Machine Learning Models: Developed and trained models to predict demand at SKU, store, and regional levels 3. Scenario Planning: Enabled forecasting for promotions, holidays, and demand spikes 4. System Integration: Embedded AI forecasts into existing inventory and replenishment systems 5. Monitoring & Optimization: Implemented continuous performance monitoring and automated model retraining
Results
1. Improved forecast accuracy reducing stockouts and excess inventory 2. Lower inventory holding costs through better demand alignment 3. Faster, automated forecasting cycles replacing manual processes 4. Improved product availability enhancing customer satisfaction