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How is AI used in inventory management and demand forecasting?

AI revolutionizes inventory management by predicting demand with unprecedented accuracy and automating replenishment decisions. Machine learning models analyze historical sales data, seasonality, promotions, weather patterns, local events, economic indicators, and social media trends to forecast demand at SKU and location levels. Key applications include: predictive ordering (automatically generating purchase orders when inventory hits optimal reorder points), safety stock optimization (balancing stockout risk against carrying costs), seasonal demand planning (preparing for peak periods with data-driven quantities), markdown optimization (dynamically pricing slow-moving inventory), and assortment planning (determining which products to stock in which locations). Results are substantial: 30-50% reduction in stockouts, 20-35% decrease in excess inventory, 15-25% improvement in inventory turnover, and significant working capital freed up. AI also enables drop-shipping decisions, allocates inventory across distribution centers optimally, and predicts product lifecycle stages to manage introductions and discontinuations effectively. The system continuously learns and improves forecast accuracy over time.