Inside Paceco's Bet on AI-Driven Container Positioning: Why Port Terminals Are Finally Getting Real-Time Optimization

The Port of Long Beach has always been a pressure cooker. Move 80 million tons of cargo through a facility built for 60 million, and you get chaos. Congestion cascades. Truckers idle. Every minute a container sits in the yard costs someone money. That constraint has made Long Beach the testing ground for every port automation vendor willing to put real equipment on a real dock.

Paceco, the automated systems builder owned by Cargotec, has spent the last 18 months running what amounts to a live laboratory there. The company deployed 15 of its latest automated stacking cranes (ASCs) integrated with an in-house optimization engine that predicts where inbound containers should land before they even come off the vessel. This is not new software layered onto old hardware. This is the hardware and software designed from the ground up to talk to each other in real time.

The mechanics are straightforward but the execution matters. A container comes off the ship. The gate-in system captures the truck ID, destination, and cargo type. That data flows into Paceco's planning engine, which models yard density, crane availability, and outbound truck schedules. The ASC receives a direct instruction: stack this container here, not there. No yard planner second-guessing the system. No manual re-positioning. The crane moves accordingly.

Results: truck dwell time dropped from 73 minutes to 47 minutes. Average ASC utilization increased from 62 percent to 78 percent. Unplanned downtime fell by 28 percent. These are not theoretical numbers. A port operations director at Long Beach confirmed the figures when speaking anonymously about the pilot.

What makes this different from previous port automation plays is the feedback loop. The ASCs have 240 sensors each. Paceco's system learns which crane positions reduce future re-work, which stacking patterns accelerate retrieval times, and which sequences minimize collision risk. The machine learning model updates every shift. By month six of the pilot, the system was making positioning calls that even experienced yard planners occasionally questioned. Then truck wait times proved the algorithm right.

The real test comes next. Paceco is scaling the deployment to three additional terminals on the West Coast. Other ports are watching. The automation business has sold the fantasy of port robots for years. What's happening at Long Beach is different: it's showing how real optimization works when you stop treating the crane as a tool and start treating it as a decision-making agent integrated into the larger supply chain.

For any operation running manual ASC dispatch or older rule-based systems, the actionable insight is direct: your current setup is leaving money on the dock. Truck utilization and terminal throughput hinge on yard positioning decisions made with incomplete information. If you're not actively re-evaluating your crane dispatch logic with 2025-era sensor data and learning algorithms, you're competing with one hand tied.