Lloyd’s Register’s (LR) Maritime Performance Services (MPS) business published a report on the state of digital transformation through artificial intelligence and machine learning in the maritime industry.
AI in maritime
he new report: ‘Artificial Intelligence in Maritime – a learning curve’, produced in collaboration with maritime innovation consultancy Thetius, highlights the potential of this critical technology for giving maritime companies a performance edge.
Artificial Intelligence is a transformational technology that will allow maritime companies across the maritime asset value chain to not only get ahead of the market but accelerate their digital transformation and meet the challenges of the upcoming energy transition
Andy McKeran, LR’s Maritime Performance Services Business Director, said.
This rapid growth is driven in part by investment into the sector. In the last 12 months, $331 million USD has been invested in startups and SMEs developing AI solutions for the maritime sector, with a further $43 million in grant funding being awarded to develop the technology for the maritime sector around the world.
In addition, the use cases for AI in the maritime industry are wide ranging. They include supporting the operation of vessels through systems that can support autonomous navigation or voyage optimisation.
They also include systems that can support the maintenance and monitoring of vessels including equipment health management, working alongside and supporting remote engineers, supporting safety data analysis, and the virtual commissioning of systems and equipment.
Moreover, the report notes that ship operators who want to adopt artificial intelligence systems need to understand that the core premise that this technology is built on top of is learning from failure.
AI has the potential to revolutionise maritime operations and create significant competitive advantages for those companies that embrace it. But the pathways to adoption are not straightforward
#1 Work with the right data
The first step to using AI effectively is to work with the right data. Some data sets can be bought, such as weather, maritime traffic, or trade volumes. But data that is unique to a particular fleet, such as fuel consumption, will need to be collected, stored and made accessible.
#2 Buy don’t build
A question that faces any team on the path to adopting artificial intelligence is whether to build systems from scratch, or whether to buy them from third party service providers. The reality is that the best approach will require a combination of both, but for the vast majority of ship operators, it will be far cheaper and
easier to buy access to the right algorithms, data sets, or functions to make the system genuinely valuable to the team that needs it.
#3 Leverage expertise
Though it is always preferable to build the right skillset in an in-house team, it is also possible to leverage the expertise of third parties.
#4 Create a safe testing environment
This could be by using a digital twin as a simulation environment for experimental systems, or it could be by having a designated ship to act as a Beta tester for systems that are being applied in the real world.
‘Readiness Assessment’ tool
In addition to the report, LR also launched its interactive ‘Readiness Assessment’ tool to help maritime stakeholders evaluate their ability to manage the significant sustainability challenges facing the maritime industry by scoring their activities around the energy transition, digital transformation, efficiency and performance, cost saving, risk mitigation as well as their ability to make smart business decisions.