Decarbonisation journey

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This integrated maritime intelligence environment helps ports, terminals, vessel operators and logistics providers improve port-call predictability, berth utilisation, vessel turnaround and emissions performance.

Port calls require close coordination between shipowners, terminals, port authorities, pilots, tug operators, mooring teams, agents, cargo handlers and landside transport providers. Fragmented data and inconsistent arrival estimates can cause berth conflicts, resource underutilisation, congestion, vessel waiting and unnecessary fuel consumption.

The solution creates a shared operational picture by converting real-time maritime data into predictive, actionable intelligence. It enables users to:

• Produce continuously updated vessel arrival and departure estimates
• Optimise berth allocations and port-call schedules
• Identify resource constraints, conflicts and operational risks
• Coordinate activities through shared events, tasks and status updates
• Support Just-in-Time arrival and virtual-arrival processes
• Integrate predictions and alerts into existing operational systems
• Measure port performance, waiting times and associated emissions

More accurate predictions improve the deployment of pilots, tugs, labour, cranes, cargo-handling equipment and landside transport. Ship operators can reduce speed when berths are unavailable, avoiding fast sailing followed by anchorage.

The cloud-based architecture aggregates and processes data from multiple sources, including:

• Terrestrial and satellite AIS
• Historical vessel movements and port-call events
• Vessel characteristics and operating patterns
• Port Community Systems and terminal operating systems
• Weather, wind, waves, currents and visibility
• Pilotage, towage, berth and resource availability
• Port infrastructure, geofences and sensor data
• Operational tasks, events and stakeholder updates

Machine-learning models analyse historical and real-time data to predict vessel arrivals and future port-call milestones. Estimates can cover pilot boarding areas, port boundaries, anchorages and berths. Predictions are recalculated when vessel speed, route, traffic, weather or port conditions change.

A visual planning environment supports berth allocation, vessel movements and resource scheduling. Automated alerts identify conflicting bookings, operational delays, unsafe arrangements and changes requiring intervention.

Application programming interfaces and system connectors allow predicted timestamps, alerts and events to flow into existing port, terminal and fleet-management platforms. Role-based access controls protect commercially sensitive information while enabling controlled collaboration between stakeholders.

The principal emissions-reduction mechanism is Just-in-Time arrival. When berth availability is known earlier and with greater accuracy, vessels can reduce speed during the voyage instead of maintaining higher speed and waiting at anchor. Because propulsion power and fuel consumption increase disproportionately with speed, controlled speed reduction can deliver meaningful fuel and CO₂ savings.

Additional decarbonisation benefits include:

• Reduced anchorage and vessel waiting
• Lower auxiliary-engine consumption
• Fewer unnecessary accelerations and schedule-recovery manoeuvres
• Shorter, more predictable port stays
• Better coordination of pilots, tugs and mooring services
• Improved utilisation of cranes and cargo-handling equipment
• Reduced truck queuing and landside congestion
• Better emissions measurement and reporting

By comparing predicted and actual port-call timestamps, operators can identify delays, quantify avoidable emissions and measure improvement. The technology therefore provides both the intelligence needed to reduce emissions and the data required to demonstrate the results.

This AI-powered optimisation technology helps shipowners and fleet operators reduce fuel consumption, lower operating costs and improve vessel efficiency using vessel-specific performance intelligence.

Traditional voyage-planning tools frequently rely on generic vessel assumptions, static weather routing and infrequent performance updates. Actual consumption, however, is affected by draught, trim, speed, hull fouling, machinery condition, wind, waves and currents. Even vessels of the same class can perform differently.

The solution creates an accurate operational model for each vessel, enabling operators to:

• Predict fuel consumption under different operating conditions
• Compare alternative routes, speeds and arrival strategies
• Optimise voyages against fuel, cost, ETA and commercial objectives
• Detect performance deterioration and excessive consumption
• Improve voyage execution through continuous onboard guidance
• Support CII improvement and EU ETS cost management
• Measure savings against reliable performance baselines

This transforms vessel data into practical decisions for crews, fleet-performance teams and commercial operators. Better visibility also allows companies to understand the financial and emissions consequences of schedule changes before instructions are issued.

The technology combines onboard data collection, cloud-based analytics, machine learning and voyage optimisation.

High-frequency data can be captured from navigation, propulsion, engine and auxiliary systems. Where extensive sensor infrastructure is unavailable, models can also use AIS and noon-report data, supported by transfer-learning techniques developed from comparable vessels.

Data is validated, structured and used to create vessel-specific digital performance models. These models analyse relationships between:

• Speed over ground and speed through water
• Main-engine power and fuel consumption
• Draught, trim and displacement
• Wind, waves, currents and sea state
• Hull and propeller condition
• Machinery performance
• Voyage duration and required arrival time

The optimisation engine simulates thousands of operating scenarios to identify the most efficient route and speed profile within technical, navigational, safety and commercial constraints.

Unlike static voyage planning, recommendations are continuously recalculated as weather, vessel condition, voyage progress or commercial requirements change. Automated propulsion-control capability can also translate the optimised plan into precise speed adjustments onboard, reducing the gap between recommended and achieved performance.

The architecture can integrate with existing vessel-management, navigation, weather-routing and emissions-reporting systems through APIs and secure data connections. 

The technology reduces emissions primarily by lowering the energy required to complete each voyage. Vessel-specific modelling identifies the most efficient combination of route, speed and arrival time rather than relying on fleet averages or generic performance curves.

Decarbonisation benefits include:

• Lower main-engine fuel consumption
• Reduced CO₂ and other combustion-related emissions
• Avoidance of high-speed sailing followed by waiting
• Continuous optimisation as conditions change
• Earlier identification of hull or machinery inefficiency
• Improved CII performance
• Reduced EU ETS allowance exposure
• Verification of fuel and emissions savings

Because fuel consumption and CO₂ emissions are directly connected, efficiency improvements create immediate environmental and commercial value without requiring vessel replacement or major capital investment.

The result is a scalable route to fleet decarbonisation: using AI, operational data and automated execution to make every voyage more efficient.

This technology gives cargo owners, freight forwarders and logistics teams end-to-end visibility of containerised ocean shipments. It replaces fragmented carrier updates, spreadsheets and manual tracking with predictive, exception-based intelligence.

The solution enables users to:

• Track containers, vessels and shipment milestones across multiple carriers 
• Receive predicted arrival and departure times weeks in advance 
• Identify delays, rollovers, transhipment risks and port congestion 
• Prioritise shipments according to operational and commercial impact 
• Coordinate customs clearance, container collection and onward transport 
• Reduce demurrage, detention, storage and emergency-expediting costs 
• Compare carrier and trade-lane performance 
• Calculate shipment-level greenhouse-gas emissions 

Predictive intelligence allows supply-chain teams to intervene before disruption affects production, inventory or customer commitments. More reliable arrival forecasts also improve warehouse, labour, trucking and manufacturing planning.

The cloud-based architecture combines carrier, vessel, port, terminal and customer data within a unified analytical environment. Inputs can include:

• Carrier schedules and shipment milestones 
• Container and booking references 
• AIS vessel-position data 
• Vessel movements and historical voyage patterns 
• Transhipment and rollover events 
• Port and terminal congestion 
• Orders, contracts, invoices and free-time allowances 
• Customer-defined operational and service-level requirements 

Domain-specific machine-learning models analyse historical and live data to generate predicted ETAs and ETDs. These forecasts are continuously recalculated as vessel movements, schedules, port conditions and container events change.

The system uses exception-management logic to distinguish routine schedule changes from events requiring action. Automated alerts can identify containers at risk of missing connections, exceeding free time, disrupting production or incurring demurrage and detention charges.

Application programming interfaces allow tracking data, predicted milestones, alerts and carbon calculations to be embedded within transport-management, enterprise-resource-planning and customer-facing systems. Analytical tools can evaluate carrier reliability, trade-lane performance, dwell time, freight costs and emissions at shipment or portfolio level.

The technology supports decarbonisation by connecting shipment visibility with carbon measurement and operational decision-making.

Shipment-level CO₂e calculations can be accessed through the user interface or API, enabling organisations to analyse emissions by container, shipment, order, carrier or trade lane. This supports Scope 3 reporting, emissions baselining and lower-carbon freight procurement.

Additional benefits include:

• Selection of lower-emission carriers and transport routes 
• Reduced dependence on carbon-intensive emergency air freight 
• Earlier replanning when delays or route disruptions occur 
• Shorter container dwell times at ports and terminals 
• Better coordination of trucking, warehousing and customs activity 
• Reduced empty or unnecessary landside movements 
• Measurement of emissions changes caused by congestion and rerouting 
• Stronger evidence for sustainability reporting and supplier engagement 

The solution does not directly control vessel fuel consumption. Its decarbonisation value comes from enabling cargo owners and logistics providers to measure emissions consistently, identify inefficient movements and make earlier, lower-carbon transport decisions.

The result is a more predictable and transparent ocean supply chain in which cost, reliability and carbon performance can be managed together.

This integrated measurement and analytics technology gives shipowners and fleet operators accurate, real-time insight into fuel consumption, engine efficiency and propulsion performance.

Unlike noon reports or model-based estimates, the solution continuously measures what is occurring across the vessel’s fuel and propulsion systems. It enables operators to:

• Measure fuel consumption by engine, consumer and fuel type 
• Monitor shaft torque, rotational speed, power and propeller thrust 
• Calculate specific fuel oil consumption and fuel consumed per nautical mile 
• Identify hull fouling, propeller degradation and inefficient engine loading 
• Compare vessel and voyage performance against reference baselines 
• Validate the results of maintenance, retrofits and energy-saving technologies 
• Automate emissions and regulatory reporting 
• Detect abnormal performance before it causes excessive consumption or failure 

The resulting data helps crews optimise vessel operation while giving shore-based teams reliable evidence for technical, commercial and investment decisions.

The modular architecture combines precision sensors, onboard signal processing, operational dashboards and cloud-based fleet analytics.

Positive-displacement or Coriolis flow meters measure volumetric or mass fuel flow. Temperature, density and viscosity measurements enable accurate conversion to mass consumption across conventional fuels, LNG, methanol and other fuel types.

Non-contact optical sensors installed around the propeller shaft measure:

• Torque 
• Shaft speed 
• Transmitted power 
• Axial thrust 
• Propeller efficiency 

The measurement of thrust as well as torque distinguishes useful propulsive output from mechanical power input. This helps identify losses associated with propeller condition, hull resistance, trim, cavitation or drivetrain performance.

A ruggedised onboard processing unit combines these measurements with GPS, speed log, draught, inclinometer and engine data. It calculates operational indicators including fuel consumption per hour and nautical mile, specific fuel oil consumption, propulsion efficiency and carbon intensity.

High-frequency data is presented onboard and transmitted securely to a cloud environment for voyage comparison, trend analysis, alerts and fleet-level reporting. Standard maritime communication protocols and APIs support integration with automation, fleet-management and emissions-reporting systems.

The technology supports decarbonisation by replacing estimated performance with measured, verifiable data. Accurate fuel and propulsion monitoring reveals where energy is being lost and confirms whether corrective actions deliver genuine savings.

Applications include:

• Optimising engine load, propeller pitch, RPM and vessel trim 
• Detecting hull fouling and propeller degradation 
• Scheduling condition-based cleaning and maintenance 
• Verifying retrofit performance before and after installation 
• Measuring the contribution of wind-assisted propulsion 
• Supporting alternative-fuel consumption monitoring 
• Improving CII, EEOI and voyage-efficiency performance 
• Producing data for IMO DCS, EU MRV and EU ETS reporting 

Because fuel consumption is directly related to greenhouse-gas emissions, verified efficiency improvements deliver immediate reductions in CO₂ and regulatory exposure. Continuous measurement also prevents savings from degrading unnoticed over time.

The technology provides the measurement, verification and reporting foundation required to convert vessel-efficiency initiatives into demonstrable decarbonisation results.

This technology enables cargo owners, freight forwarders, carriers and logistics providers to measure, compare and reduce greenhouse-gas emissions across maritime and multimodal transport.

It replaces generic distance-based estimates and inconsistent carrier calculations with standardised, shipment-specific carbon intelligence. Users can:

• Calculate CO₂e emissions for historical and planned shipments 
• Compare carriers, services, vessels, routes and transport modes 
• Integrate carbon performance into freight procurement 
• Identify lower-emission shipping options before booking 
• Calculate Scope 3 logistics emissions 
• Verify carbon surcharges and regulatory exposure 
• Model alternative fuels and transport scenarios 
• Produce auditable data for sustainability reporting 

This allows commercial teams to evaluate price, transit time, reliability and emissions together rather than treating carbon as a separate reporting exercise.

The cloud-based calculation engine combines geospatial routing, AIS vessel data, carrier schedules, vessel specifications and emissions modelling.

For maritime transport, the technology estimates fuel consumption using:

• Actual or modelled sailing distance 
• Vessel identity, type, capacity and dimensions 
• Main-engine power and fuel characteristics 
• Auxiliary-engine and boiler consumption 
• Vessel speed and operating profile 
• Cargo weight and container utilisation 
• Historical port calls and vessel trajectories 
• Transhipments and connecting vessels 
• Emission Control Areas and applicable fuel types 

Historical shipments can be reconstructed from AIS signals to identify the vessels used, actual sailing paths, intermediate port calls and connections. For planned shipments, carrier schedules and deployed fleets are analysed to estimate emissions for available services.

The engine calculates Well-to-Wheel greenhouse-gas emissions, covering both fuel production and operational combustion. Its methodology is aligned with the GLEC Framework and ISO 14083:2023 and supports multiple modes, including sea, road, rail, air and inland waterways. 

Application programming interfaces allow emissions, routing and vessel data to be integrated into transport-management, procurement, booking, reporting and customer-facing platforms.

The technology supports decarbonisation by making emissions visible at the point where transport decisions are made.

Its principal contribution is the consistent comparison of carriers, services and routes. Freight buyers can allocate cargo to lower-emission options, include carbon performance in tenders and engage suppliers using independently calculated evidence.

Additional applications include:

• Establishing Scope 3 emissions baselines 
• Tracking progress against reduction targets 
• Comparing direct and transhipment services 
• Modelling alternative fuels and modal shifts 
• Identifying inefficient routes and excess distance 
• Improving carbon-related procurement negotiations 
• Supporting ISO 14083 and GLEC-aligned reporting 
• Assessing EU ETS and other carbon-related costs 

Vessel-specific calculations use reconstructed routes, engine characteristics, speeds and auxiliary consumption to improve accuracy beyond simple distance-based averages. 

The technology does not directly control vessel operations. Its decarbonisation value comes from providing the trusted data needed to select lower-carbon transport, influence carrier behaviour, optimise routing and verify whether emissions-reduction strategies are delivering measurable results.

This technology provides shipowners and fleet operators with real-time, whole-vessel intelligence for improving reliability, fuel efficiency, maintenance planning and operational performance.

Traditional monitoring systems often analyse individual engines or equipment packages independently. This can conceal interactions between propulsion, auxiliary machinery, electrical loads, environmental conditions and vessel operations.

The solution consolidates data from multiple onboard systems and converts it into actionable recommendations. It enables operators to:

• Predict machinery failures before operational disruption occurs 
• Detect abnormal equipment behaviour and performance degradation 
• Reduce unplanned downtime and costly emergency repairs 
• Optimise maintenance intervals and spare-parts planning 
• Monitor fuel consumption and identify inefficient operating practices 
• Compare performance across vessels, voyages and equipment 
• Extend machinery life and reduce unnecessary overhauls 
• Improve regulatory, emissions and operational reporting 

The technology is equipment-, manufacturer- and vessel-agnostic, making it suitable for commercial ships, ferries, offshore vessels, workboats and tug fleets.

The architecture combines onboard data acquisition, edge computing, cloud analytics, artificial intelligence and machine learning.

Data can be collected from existing vessel sensors, control systems and machinery interfaces, including:

• Main and auxiliary engines 
• Propulsion and power-generation systems 
• Fuel-flow meters and tank instrumentation 
• Temperatures, pressures, vibration and rotational speed 
• Electrical loads and generator performance 
• Navigation, speed, position and voyage data 
• Weather and environmental conditions 
• Maintenance records and equipment operating hours 

Initial analytics are performed onboard, allowing high-frequency machinery data to be processed close to its source. Selected data packages are then transmitted to a cloud environment for advanced modelling, fleet comparison and shoreside analysis.

Machine-learning models establish expected operating behaviour for the vessel and its subsystems. Live measurements are continuously compared with these dynamic baselines to detect anomalies, developing faults and inefficient operating patterns.

Predictive models can create multiple virtual operating scenarios to estimate how equipment condition may develop and whether continued operation could result in failure. Automated alerts provide crews and technical teams with supporting data, likely causes and recommended actions.

The platform can connect to existing vessel systems without being restricted to a particular equipment manufacturer. This enables integrated analysis of relationships across the entire vessel rather than isolated monitoring of individual components.

The technology contributes to decarbonisation by reducing fuel consumption, preventing performance deterioration and improving the efficiency of machinery operation.

Applications include:

• Identifying inefficient engine loading and operating practices 
• Optimising generator use and electrical-load distribution 
• Detecting machinery degradation that increases fuel consumption 
• Reducing unnecessary running hours and maintenance activity 
• Preventing failures that require inefficient backup operation 
• Extending equipment life and reducing replacement requirements 
• Supporting fuel-use and emissions reporting 
• Verifying operational efficiency improvements 

Earlier fault detection enables maintenance to be performed before equipment efficiency declines significantly. Condition-based maintenance also reduces unnecessary component replacement, technician travel, spare-parts consumption and vessel downtime.

By linking machinery condition, vessel operations and fuel consumption, the technology provides a continuous feedback loop between reliability and environmental performance. The result is lower operating cost, reduced emissions and more efficient use of existing fleet assets.