What Is Vessel Performance Intelligence and What Does It Require?

Vessel performance intelligence is the continuous analysis of a ship's fuel, speed, propulsion, engine, hull and weather data, compared across voyages and across the fleet, to show where fuel, time and efficiency are being lost. It needs consistent, validated inputs from noon reports or automated sensors, and on a mixed-OEM fleet it needs signals normalized to shared fields before any comparison means anything.

What vessel performance intelligence means

It goes beyond fuel tracking. CHRIS Marine describes vessel performance optimization as the continuous process of analyzing ship data to make informed decisions that enhance efficiency, and says it goes beyond simple fuel tracking. The "intelligence" part is the comparison: one voyage against another, one vessel against its sister, one route against the fleet's other routes.

Wärtsilä frames the same idea as combining navigational, operational and technical data to reveal performance differences between vessels and identify inefficiencies. A single number such as tonnes per day tells you little. The same number against speed, draft, weather and engine load tells you whether the ship is running well.

What it measures for a commercial fleet

The core measures are fuel consumption, speed loss, propulsion efficiency, hull and propeller condition, engine condition and charter party performance. Jotun lists voyage, hull and propeller, and engines and systems performance as the areas fleets monitor, and names fuel consumption, speed loss and trim optimization as the factors worth tracking regularly.

Hull condition matters because of how much resistance it carries. The MariFuture development paper puts frictional resistance at about 70-90% of a ship's total resistance for bulk carriers and tankers, and approximately 50% for cruise liners and container vessels, and notes that it is directly affected by hull roughness, which in turn is affected by fouling. Hull roughness from fouling feeds directly into it.

Data sources and signals you need

Start with engine, fuel flow, speed, position, draft and sea state, then enrich with weather and AIS. Wärtsilä's examples of the data needed are voyage information, fuel consumption, vessel speed, weather conditions, engine performance and operational reporting. Analysed together, these give a more complete view than any one source.

The MariFuture paper says a typical deck log holds position, speed, propeller revolutions, slip, draft and sea state, and a typical engine log holds power, rpm, temperatures and fuel oil consumption. Sensor data, noon report data, design data or any combination can feed a model; Toqua says only one of these is minimally required but recommends sharing as many as are available.

How accurate and frequent the data must be

Frequency depends on the question you are asking. ISO 19030 is a voluntary standard, published in late 2016, that sets two tiers. ISO 19030-3 uses noon reports, which give at least an indication of overall performance. ISO 19030-2 is based on high-frequency data and needs a datalogger, a torque meter and some cabling, or alternatively flowmeters.

Accuracy follows from the instrument. A flow meter or torque meter gives a measured value. A noon figure is a daily average that depends on crew practice; the MariFuture paper notes that the number of variables logged depends on company requirements, available instrumentation and the motivation and training of the crew.

How performance data is cleaned and validated

Merge the sources, check their quality, and join them on shared equipment identifiers before anyone reads a KPI. Toqua says it continuously processes, merges and analyzes uploaded data to decide whether quality is sufficient for modeling. That quality gate is the right order: validate first, then calculate.

In practice, validation means catching sensor dropouts, impossible values, unit mismatches and noon entries that disagree with the automated record. Shared equipment identifiers let maintenance compliance, uptime and failure trends sit beside performance data.

Noon reports versus continuous automated collection

Choose by the question. Noon data is enough for voyage reporting, fuel trends, charter party checks and regulatory totals on a ship with no automated collection. Signal data is needed when the question is about equipment condition, short-interval performance change or the effect of a maintenance job.

So a chartered vessel with noon reports can still answer "did she meet the contract speed and consumption?" It cannot tell you whether a turbocharger cleaning changed engine efficiency last week. Owned ships with automated collection can. The tradeoff is cost and effort: automated collection means sensors, cabling and software, as Jotun notes, while noon reports add manual workload and coarser detail. CHRIS Marine argues that logbook entries and manual calculations alone no longer suffice, though automated collection on its own can still leave the crew's workload unchanged.

Why OEM telemetry formats make mixed fleets hard to compare

Each manufacturer names, scales and identifies signals differently, so normalization to shared fields has to come first. Take an ops manager comparing two vessels with engines from different OEMs. One reports fuel rate under one signal name in litres per hour, the other under another name in kilograms per hour, and each ties it to its own equipment identifier. Put them on one chart and the gap you see may be units, not performance.

The fix is mapping each OEM's signal names, units and identifiers to shared fields. Until the mapping exists, any cross-fleet benchmark is unreliable.

Which regulations and reporting rules require performance and fuel data

IMO greenhouse gas rules and the ship energy efficiency management plan (SEEMP) are the main drivers of fuel data. ABB describes fleet performance monitoring as supporting SEEMP and IMO GHG compliance alongside fuel efficiency and technical condition. ISO 19030 stays voluntary, but it defines how to measure hull and propeller performance changes, which is useful evidence when you assess a coating or a cleaning.

If you want to see how your own fleet's data would map into one schema, request a demo with SailPlan.