Multi-Location Retail Chain Reduced Stockouts by 42% with AI Demand Forecasting

Built a hybrid ML forecasting engine combining XGBoost for short-term forecasts and LSTM neural networks for seasonal patterns. Trained on 5 years of historical sales across 180 locations and 42,000 SKUs. Achieves 94% forecast accuracy vs 68% with the previous moving-average method. Forecasts refresh daily with automatic anomaly detection.

Native bidirectional integration with SAP ERP, NCR POS systems across 180 stores, and Manhattan WMS. Real-time inventory sync every 15 minutes. Single source of truth for inventory levels, sales velocity, and reorder status across the entire chain.

Dynamic safety stock calculation with ML-driven reorder points adjusted per SKU and location. Auto-generated purchase orders routed to 240+ suppliers via EDI. Reduced reorder cycle from 7-10 days to 48 hours. Supplier performance scoring based on fill rates and lead time accuracy.

ML-powered stock balancing algorithm identifying slow-moving inventory at one location that could fulfill demand spikes at nearby stores. Automated transfer recommendations with cost-benefit analysis. Reduced inter-store transfer waste by 60% while eliminating 78% of regional stockouts.