A large-scale logistics operator processing 50,000+ daily shipments deployed AI route optimization, demand-driven inventory placement, and smart warehouse automation — achieving ₩12B in annual savings.
A large logistics operator was facing rising fuel costs, driver shortages, and inefficient inventory positioning across its network. Experience-based route assignment left an average 23% of each vehicle's run as unnecessary mileage. Operational cost pressure was intensifying with no clear path to improvement through traditional methods.
① AI Delivery Route Optimization — real-time traffic, weather, and priority signals feed into an AI engine that calculates optimal routes automatically, cutting average daily mileage per vehicle by 18%. ② Demand-Driven Inventory Placement — regional and seasonal demand forecasts pre-position stock across fulfillment centers, reducing last-mile distance and delivery time. ③ Smart Warehouse Automation — AI-powered inbound/outbound automation and worker routing improved warehouse throughput by 58%.
Twenty months after deployment, annual delivery costs fell 42% — approximately ₩12B in savings. Customer satisfaction scores improved from 3.8 to 4.7. A 28% reduction in carbon emissions also produced measurable ESG impact, cutting the fleet's annual CO₂ output by an amount equivalent to removing 1,200 cars from the road.