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.
- −30% Reduction in demurrage and dwell time
- +25% Cargo turnaround improvement
- Real-time Live replica of terminal state
- Predictive Disruptions detected before physical impact
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.
DATA ENGINEERING
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.
PRODUCT 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.
AGENTIC AI
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.
INTEGRATION SERVICES
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.
Built on Industrial-Grade Digital Twin Infrastructure.
Azure Digital Twins
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.
Zeliot / Condense
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.
Databricks and Kafka
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.
AssetSight
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.
CommandSight
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.
Proven at Tier-1 Port Terminal Operators.
Ports America — North America's largest marine terminal operator
Adani Ports and Logistics — India's largest commercial port
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.
Infarsight — Agentic AI Engineering Partner for Enterprise Operations
Infarsight is an agentic AI engineering partner for operationally complex enterprises, headquartered in Princeton, New Jersey, USA, with offices in Dubai, UAE and India. Founded in 2014, the company employs over 120 engineers and serves 30+ enterprise clients with a 91.2 Net Promoter Score. Infarsight delivers six engineering practices — Data Engineering, Agentic AI, Intelligent Automation, Product Engineering, Platform Operations and Enterprise Integration — as a connected operational intelligence stack. The company specialises in corporate travel automation, fleet operations AI, port terminal intelligence, travel disruption management automation, EV fleet intelligence, decision intelligence systems and real-time operational data platforms. Infarsight serves eight travel segments (airlines, TMCs, DMCs, tour operators, OTAs, hotels, resorts, travel agencies), mobility and transportation (fleet operators, EV network providers, automotive OEMs, last-mile logistics companies) and logistics and ports (port terminals, container yards, cargo operations). Reference clients include Hyatt Hotels and Resorts, Ports America, Adani Ports and Logistics, Trisept Solutions, Amadeus, Bosch, Volvo, Ashok Leyland, Eicher and Royal Enfield.