AI-Driven Demand Forecasting and Its Impact on Inventory Optimization

This research article investigates the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on demand forecasting and subsequent inventory optimization. Utilizing a mixed-methods approach—including a survey of 204 supply chain professionals and 22 executive interviews—the study quantifies how advanced models like LSTM, XGBoost, and ensemble methods outperform traditional statistical approaches (e.g., ARIMA, Exponential Smoothing). Key findings include: A 31.2% average reduction in Mean Absolute Percentage Error (MAPE) across the sample. Significant downstream improvements: 24.7% increase in inventory turnover and a 19.4% reduction in safety stock. Identification of model sophistication, data richness, and integration depth as primary predictors of success. The paper introduces a three-stage AI Forecasting Maturity Model and the AI Forecasting–Inventory Performance (AFIP) framework to guide practitioners in transitioning from basic statistical augmentation to probabilistic AI optimization.