New AI Framework Boosts Retail Demand Forecasting Accuracy

Retailers often struggle with demand forecasting when market conditions change rapidly, making traditional models quickly outdated, especially for new products with limited sales data. This research introduces a "predict-then-correct" (PtC) system designed to enhance adaptive retail forecasting. It works by taking an initial forecast from a standard machine learning model and then applying real-time adjustments using a few-shot continuous contextual bandit approach. This correction mechanism can learn from limited new data, such as sales of similar products, to refine predictions without needing to retrain the entire base model. The PtC framework was tested on real-world data from Walmart and a specialized beverage dataset. It demonstrated significant improvements in accuracy metrics like MAPE, MAE, and RMSE across different demand scenarios, including stable, high-volume, and erratic patterns. Furthermore, the system led to lower inventory costs compared to other common inventory management strategies. These findings suggest that integrating online forecast correction can effectively bridge the gap between offline demand learning and dynamic retail decision-making, offering a more agile approach to inventory management.