Retail Demand Forecasting

A multi-channel retailer with 200+ physical stores and an e-commerce platform was experiencing significant inventory management issues. Their legacy forecasting system resulted in 24% overstocking of slow-moving items and 31% stockouts of high-demand products. Seasonal demand variations were poorly predicted, and the system couldn't effectively account for external factors like weather, local events, or market trends. This led to approximately $4.2M in annual losses from markdowns and missed sales opportunities.

We developed and implemented a comprehensive AI-driven demand forecasting system that integrates data from multiple sources. The solution utilizes ensemble machine learning models to predict demand at the SKU/location level with daily, weekly, and monthly granularity. The system incorporates point-of-sale data, inventory levels, promotional calendars, seasonal patterns, weather forecasts, local events, social media sentiment, and competitive pricing. Automated workflows provide inventory recommendations and highlight anomalies requiring attention.

We began with a comprehensive data assessment and cleansing process, establishing automated data pipelines from all relevant sources. The models were initially trained on three years of historical data and validated against known outcomes. We implemented a phased rollout by merchandise category and trained the inventory management team on the new system. Regular retraining cycles incorporate new data, and a dedicated exception management process handles unusual situations that require human judgment. The system continues to evolve with additional data sources and algorithm refinements.