Automated Demand Forecasting in Retail Supply Chains Using Deep Reinforcement Learning

Accurate demand forecasting is pivotal for effective retail supply chain management, influencing inventory allocation, operational costs, and customer satisfaction. Traditional timeseries models often struggle to adapt to the nonstationary nature of modern retail—where promotions, seasonal trends, and external disruptions can spark abrupt spikes or dips in demand. This paper proposes a Deep Reinforcement Learning (DRL) framework that continuously refines forecasts via a multi-objective reward mechanism encompassing both accuracy and cost factors, such as stockout incidents and overstock inefficiencies. We validate our approach on a synthetic dataset of 30,000 historical sales records across 12 product categories and 8 regional warehouses, enriched with promotional and seasonal effects. Experimental comparisons with established baselines (ARIMA, LSTM, XGBoost) reveal that our DRL agent not only improves prediction accuracy (mean absolute percentage error) but also significantly reduces stockout rates and inventory overages. We detail an end-to-end system architecture—from data ingestion and feature engineering to DRL policy training and real-time deployment—and discuss key limitations, ethical considerations, and avenues for future enhancement. Our findings underscore DRL’s potential to transform retail demand forecasting into a dynamic, costaware, and continually adaptive process.