Artificial Intelligence (AI) and Machine Learning (ML) in maritime logistics

Original article was published by Alexey Fitiskin on Artificial Intelligence on Medium


Artificial Intelligence (AI) and Machine Learning (ML) in maritime logistics

Every person who is somehow involved in the IT industry knows (or heard) that Artificial Intelligence (AI) and Machine Learning (ML) are technologies to keep an eye on. Machine learning is a branch of artificial intelligence and is one of the ways we expect to achieve AI. Machine learning relies on working with small to large datasets by examining and comparing the data to find common patterns and explore nuances. Artificial intelligence, on the other hand, is vast in scope. Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence.

Shipping companies have now found that investing in faster communication for their ships offers many benefits not only to captains, but also to the shipping company itself. Most vessels have evolved into remote offices at sea that can offer reliable Internet access, virtual networks, email, route planners and many other systems and applications to the captain and crew. However, now is the time for shipping companies to think about long-term growth. It’s time to invest in new technologies that can improve standard vessel operations, reduce company’s costs and optimize business processes.

That’s where AI and ML come on the stage. Computers can process huge amounts of data much faster than humans can ever achieve. The high intelligence of ML algorithms and industry experience will create a great advantage for shipowners, who first implement them in their business. The higher the investment in AI/ML, the greater the benefit of its big data analysis capabilities. ML algorithms are able to handle data throughout the history of vessel operation.

Articles and news about companies using AI/ML technology on a daily basis appear on the internet like mushrooms after the rain. Recently, Stena Lines (one of the world’s largest ferry operators) used AI/ML to reduce(single use) the amount of plastic on board, to reduce crew and passenger accidents, for fuel economy and battery-powered power plants, and etc. The port of Rotterdam uses an ML-based system to determine vessels’ time of arrival.

Due to the fact that data is a major element for eliminating uncertainty, adapting ML algorithms can help to increase atypical data that can be crucial for shipowners. So far, data mining in the maritime industry is very limited. Consequently, the application of ML techniques in maritime transport is limited compared to other industries. Machine Learning allows users to apply intelligent algorithms and to evaluate data that helps to guide the logic of possible problems in maritime transport. These methods can be used in marine network planning, voyage planning, cargo optimization, maintenance process and etc.

Advanced Machine Learning algorithms will be capable of improving voyage optimization, such as fuel efficiency, minimizing crew performance, improving voyage costs estimates, calculating the optimal route in a minute, give recommendations on speed, course and etc.

For example, ML algorithms can be used for estimating fuel consumption using engine data and vessel characteristics. These algorithms allow to transform huge datasets of noisy sensor data and other data from onshore sources to structured information that can be used to predict fuel consumption and plot optimal routes for vessels.

We in Marine Digital have created a tool that collects data from vessel’s sensors, as well as from external sources such as weather stations, satellites, etc. We process the entire data array through machine learning algorithms in order to provide the shipowner and the management of the shipping company with the necessary information for making decisions in the process of Vessel Performance Monitoring in our Fuel Optimization System — Marine Digital FOS.

Although machine learning is already used in many areas of the digital world, its adaptation to the maritime industry remains limited so far. Since maritime transport requires smart tools, the application of machine learning offers maximum benefits for sustainable transport. In terms of comprehensive analysis, marine professionals and researchers should pay particular attention to appropriate algorithms to address specific shipping problems in voyage optimization, stability of transportation, forecasting maintenance and repair, control of freight rates, digitalization on the bridge and control engine, energy efficiency management and enhancement maritime security.