Impact of AI-Driven Demand Forecasting on Retail Inventory Efficiency

Accurate demand forecasting is a critical component of retail inventory management; however, traditional statistical forecasting methods often struggle to capture complex and volatile demand patterns. This study examines the impact of AI-driven demand forecasting on retail inventory efficiency using SKU–store–time level data. A quasi-experimental research design is employed to compare forecast accuracy and inventory performance before and after the adoption of AI-based forecasting models. The analysis indicates statistically significant improvements in forecast accuracy, accompanied by reductions in stockout rates, increases in inventory turnover, and lower inventory holding costs. These effects are particularly pronounced in product categories characterized by high demand variability. The findings provide empirical evidence that AI-enabled demand forecasting can generate meaningful operational benefits when effectively integrated into retail inventory decision-making processes. The study also underscores the importance of responsible model governance, continuous performance monitoring, and bias mitigation to ensure reliable and ethically sound forecasting outcomes in operational inventory systems.