Beyond the ‘sweet spot’ of speed versus time in vessel optimisation


Despite the noise around digitalisation, gathering accurate, actionable vessel performance data continues to be a huge issue for shipowners. There are so many optimisation solutions available to owners, each claiming they can save 10-15% of vessel carbon emissions, but there’s no way of judging whether these claims have any value because we can’t know what any of them is being compared to.

The key aspect remains to be the ability to optimize on the right data. Without valid data you are just another person with an opinion. This sounds logical but many software systems don't include a proper validation process but simply rely on whatever reporting already exists onboard a vessel which can be anything from free text e-mails to excel sheets.

Collection and verification of vessel operational data rely on the ability to connect software to operational functions. Software can be a very useful thing for shipping companies but there is still a requirement to help people understand how to use it for commercial benefit.

Sensors are a great thing but not a solution in themselves; they constantly have to be monitored and calibrated. We’re also a long way from unified sensor data that everybody can use. A company spot chartering a vessel is never going to get the sensor data they need on a short-term period charter but they still need validated data for environmental reporting and taking action.

Regulation and Compliance

Historically the optimisation factors around a voyage have focussed on finding the ‘sweet spot’ between the correct speed versus the value of time. A third dimension has been added to voyage optimization, namely the ideal speed for reduced CO2 emissions. This is the central motivation behind the introduction of CII.

Vessel operators have always been subject to regulation but the responsibilities that come with EEXI and the CII put them into different territory. This will matter more and more in future, not just for compliance with IMO regulations but once the EU Emissions Trading System comes into force.

To help owners prepare, Coach has recently entered an agreement to enable owners to submit vessel emissions data to DNV for verification using an open API. This partnership means that verification against MRV and IMO DCS reports is simplified.

With the data we collect, owners can interrogate their emissions reporting against measures including Annual Efficiency Rating and the Energy Efficiency Operating Index, under the conditions they choose. Integration into shipmanagement systems or Business Intelligence tools is a must for carbon-compliant voyage planning.

Coach is also working on including the impact of the EU ETS in all products which will be introduced later this year, providing tools users need in preparation of its introduction in January 2024.

Building Better Models

A myth has taken root in shipping that collecting thousands of vessel data points is in some way an advantage to the optimisation process. Instead, the most important factor is that the correct data is used for generating insights that the user can act upon.

Coach users report standard data using one piece of software (no hardware is required) which we use to validate daily vessel reports using both vessel-specific min/max values and previously reported data.

Consistency of reporting is highly problematic, what are the start and end points, what fuel remains on board, do consumption figures match. These are simple validations but the operator can’t make a report unless they know where and when to start.

Collecting and validating data is the first step in creating performance models. Our in-house process relies on naval architects to construct a digital twin for each client vessel, plugging ship-specific fuel consumption and emissions performance into a platform that can create a report on how ships are performing against benchmark values.

By comparing AIS position and hindcast weather data with other reported data it’s possible to build a ‘resistance model’ of the ship’s performance.

This means putting the digital twin into exactly the same sailing conditions as the actual vessel, converting fuel into power and measuring the discrepancy, including some resistance to represent hull fouling. From there it’s a matter of extrapolating into actual speed performance.

Being able to monitor the ship’s performance during the voyage also demonstrates whether the voyage is executed to the terms agreed in the charterparty or whether a claim is valid.

A Sustainable Business

Shipowners by and large want to comply with regulations but they are in shipping to make a profit. It doesn’t need to be either/or; profit and sustainability go hand in hand. What it really requires is a change management process.

We think that sustainability means more than just defending the environment, it’s about protecting your business. Of course, shipping has a really strong focus on carbon emissions reduction and that’s a good thing, but the truth is that take-up is always going to be slow unless there is a benefit to the bottom line.

Any software solution a vessel operator chooses needs to cater to their commercial needs too; simply presenting data does not equal insight that necessarily has any value. Understanding vessel performance also means looking beyond simplistic numbers for energy or fuel savings and having a better view of the big picture.

For example, a ship with performance at 85% of its design specification may have minimal savings potential if it primarily sails short routes at low load and speed; while a vessel with a better performance level could have a huge savings potential if sailing long hauls at full load and high speed.

There are really only two rules that matter to this discussion: be as efficient as you can be and be prepared for a global price for carbon. All vessels can benefit from taking the best decisions to optimise the voyage and it doesn’t matter if the ship is 20 years old or brand new; it always makes sense to optimise performance.

Ultimately, optimising a voyage requires taking the complexity out of collecting the data, especially if that complexity is there as a smokescreen for knowledge gaps. Small amounts of data can create actionable insights, they just need to be compared to something meaningful.