Summary
Applied machine learning to predict inbound order lead times at purchase order creation and during transit. Unified enterprise and external data to provide real-time visibility into shipment delays and port congestion. Replaced static averages with dynamic, order-level forecasts to improve planning accuracy. Reduced inventory uncertainty, transportation disruptions, and excess operational costs while enabling scalable supply chain analytics.