Digital Twins
for Port and Terminal Operations
A continuously updated virtual replica of your port or terminal, every berth, every container position, every vessel ETA, every piece of equipment, enabling operations teams to simulate decisions, predict cascade disruptions and coordinate the physical operation through a single live model.
What is a Digital Twin for Port Operations?
A port digital twin is a continuously updated virtual model of the physical terminal, ingesting vessel AIS data, container sensor feeds, equipment telemetry, gate camera data and TOS records to maintain a live, accurate representation of operations. Unlike a static dashboard that shows historical data, a digital twin reflects the terminal as it exists right now, and simulates how it will look in 2, 4 and 8 hours based on current trajectories. Operations teams use it to make berth allocation, yard staging and equipment deployment decisions before committing to them in the physical world.
Port Operations Run on Coordination Across Systems That Were Never Designed to Connect.
Berth planning based on incomplete vessel data
Berth planners work from spreadsheets and VHF radio updates. Vessel ETAs change hourly based on weather, port priority and cargo loading delays. Without a live model that updates as ETAs shift, berth plans are stale before the ink dries.
Container yard congestion building invisibly
Dwell time accumulates in specific yard blocks while others sit empty. Without a live yard model, restaging decisions are made on experience rather than data, creating congestion, double-handling costs and missed departure windows.
Equipment failures cascading into operational downtime
Crane breakdowns, straddle carrier failures and gate system outages are discovered when they occur, not before. Terminal equipment has telemetry signals that predict failures 24 to 72 hours in advance when monitored continuously.
Demurrage accumulating from avoidable coordination gaps
Demurrage builds when berth planning, yard staging and gate release are not coordinated in real time. A vessel arrives on schedule but the yard has not been cleared. The cost is avoidable, but only if the coordination happens before the ship arrives.
Five Engineering Practices. One Live Terminal Model.
Every terminal signal connected and synchronised
Vessel AIS feeds, container RFID and sensor data, terminal equipment telemetry (cranes, straddle carriers, RTGs), gate CCTV and OCR, TOS records and weather feeds, all ingested and synchronised in real time via Kafka streaming pipelines using Zeliot's Condense platform for device connectivity. Data Engineering →
The twin model and simulation engine
The digital twin model itself, built using Azure Digital Twins or AWS IoT TwinMaker, maintains a live graph of berths, containers, vessels, equipment and relationships. The simulation layer evaluates proposed berth reallocations and yard restaging decisions against current terminal state before operations teams commit. Product Engineering →
Predictive disruption detection and response
AI agents monitor the digital twin continuously, detecting developing yard congestion, approaching demurrage thresholds, equipment health anomalies and gate throughput bottlenecks before they become operational incidents. Agentic AI →
TOS, VTS, SCADA and equipment protocol connectivity
Terminal Operating System (Navis N4, SPARCS), Vessel Traffic Service (VTS), SCADA protocols (Modbus, OPC-UA) and crane/RTG control systems, all connected through Infarsight's integration practice using the appropriate industrial protocol layer. Integration Services →
Built on Industrial-Grade Digital Twin Infrastructure.
Microsoft's enterprise digital twin platform, graph-based live models of port terminal assets, relationships and operational state with real-time update and simulation capability.
Microsoft Partnership →IoT device connectivity and real-time data streaming from terminal equipment, vessel tracking systems and port sensors, the live data layer that keeps the twin current.
Zeliot Partnership →Real-time event streaming via Kafka and predictive analytics via Databricks, processing the high-throughput telemetry that port operations generate and running the ML models that detect anomalies in terminal state.
AssetSight and CommandSight Reduce Build Time by 50%.
Pre-built asset telemetry connectivity and control tower interface layers, reducing time to a production digital twin programme.
Equipment telemetry and health intelligence
Pre-built connectors for terminal equipment protocols, cranes, RTGs, straddle carriers and gate systems. Provides the continuous telemetry layer that feeds the digital twin with real-time equipment state and predictive health signals.
Explore AssetSight →Operator interface and decision layer
The command-layer interface that sits above the digital twin model, presenting the live terminal state, surfacing AI recommendations and routing decisions to the right operations team member.
Explore CommandSight →Proven at Tier-1 Port Terminal Operators.
Ready to build a live model of your port operations?
We start with a data and systems assessment, mapping the terminal data sources, integration requirements and the specific operational decisions the digital twin will support.