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Ship fueling costs and CO2 emissions are major issues in the shipping industry. Stena Line, one of Europe’s leading ferry operators carrying out 29,000 voyages a year, aims to reduce fuel consumption by 2.5% per nautical mile annually. As part of varied initiatives to achieve this goal, a team of experts in data science from European branches of the Hitachi Group worked with Stena Line’s data scientists, Senior Captain and crew to develop a solution to improve fuel efficiency.
“Navigating a ship is an art form.” This was the expression used by the R&D Project Leader, Anthony Emeka Ohazulike (Hitachi Europe, European Research & Development Centre). The wind, sea current and waves never stop along the ever-changing sea routes, which means the exact same conditions rarely occur twice. Handling a ship, which constantly requires optimum navigation while reading the wind and direction of the sea current, relies on instinct honed by experience more than anything else. As a result, there is a large difference in the amount of fuel consumed by a ship navigated by a novice ship captain versus an experienced ship captain. In addition, captaining a ship as an occupation has been identified as a profession that may eventually suffer a labor shortage in the future. In Europe, where the working-age population is declining, it is becoming increasingly difficult to train successive generations of sailors.
To improve the situation, the Hitachi Team, proposed an AI-based navigation as a solution that would make efficient ocean voyages possible regardless of the captain’s experience. This solution uses Machine Learning (ML) to teach to the Artificial Intelligence system the navigating skills of an expert captain, and the system provides the captain with recommendations for the optimal navigation plan taking into consideration the weather, environment, and size of the ship.
Navigating the ocean is a battle with nature that surpasses the human imagination. Anthony’s team with Chief Data Scientist, Aatos Heikkinen (Hitachi Europe, Social Innovation Business) and Data Scientist, Lisa Knoll (Hitachi Consulting UK) began by converting the various natural conditions that affect navigation into data. The previous four years’ worth of Stena Line operation records were extracted, and operating conditions were tracked on daily ferries to build a model for sea current, and water depth “Data communication with the ferries at sea was difficult. Transmission and reception only take an instant on land, but on the water where the communication environment is poor, it took a long time. It was not rare for the process to take all night” says Aatos with a wry smile.
From the time of this data analysis, every two weeks Hitachi reported all of the project progress to Stena Line’s data scientist team, as well as working closely with Senior Captain, Jan Sj?str?m and his crew, to seek cooperation in AI development. “We could not have made this project a success without the trust of those actually navigating the seas” says Project Manager, Christopher Attard (Hitachi Consulting UK), who acted as liaison between Hitachi and Stena Line. Our sincere efforts to build a relationship proved successful when one day there was a request from Captain Sj?str?m. “Can you install the AI navigation system on the large ferry I operate?” The AI that Anthony’s team had nurtured like their own child was going to set sail for the first time.
A prototype of the AI navigation system began operating on the Stena Scandinavica. This was the beginning of its navigation training under the tutelage of Captain Sj?str?m. It was tested out on the open seas, issues that were found were addressed, and those modifications were reflected on the next voyage. Hitachi and Stena Line repeated this cycle time and time again.
The parameters of ocean current, waves, water depth and wind are all interrelated, and the absence of even one of them will throw off navigation functions. However, one of these elements was not included in Hitachi’s initial development plan: wind. On one particularly windy day, the prototype AI navigation system, which did not account for wind conditions, was rendered completely dysfunctional due to strong winds exposing its weakness. The next morning, Hitachi received a stern message from Captain Sj?str?m: “If you have no plans to incorporate wind in your model, I will never turn on the switch on the AI navigation system again”. This was because Captain Sj?str?m believed in the potential of AI and truly wanted to use it for navigating that he chose such strong language. The development team was spurred on by this and began working on adding a wind model in all sincerity to live up to expectations.
Converting wind into a model proved extremely difficult. In addition to affecting ocean currents and waves, the direct impact on the ship varies depending on the size and shape of the vessel. The team engaged in repeated discussions to build this complex wind model and incorporate it in the AI navigation system. “As the team was from different countries and had different areas of expertise, it is no surprise that we sometimes disagreed on the best approach. However, with the rich proposals available from the team, we finally delivered the best possible solution,” Lisa reflects. In this way, Anthony’s team overcame the issues they faced using the power of the Harmony.
Even on land, Hitachi knew if a captain is using the AI navigation system to navigate the open seas. If the switch is on, that alone is proof AI is working well. “One day, on a voyage, Captain Sj?str?m continued to use the system 99% of the time he was navigating. His trust in Hitachi came across in the most tangible way that day, giving us the greatest sense of satisfaction possible,” says Anthony exuberantly. As the project entered its final stages and the performance of the prototype stabilized, a delightful e-mail arrived. “The AI model did well tonight, even in a tricky voyage with strong sea current and shifting wind.” Reading these words over and over again, Anthony was filled with the joy of a parent whose child’s accomplishments have been acknowledged.
Using AI to optimize maritime navigation was the challenge that drove the team members’ Pioneering Spirit. Conversely though, it gave captains and crews the impression this technology would replace their jobs. It was the relationship of trust that grew between Hitachi and Stena Line that eventually dispelled their wariness. Rather than thrusting the AI technology upon them all at once, Hitachi was honest in their approach sharing the development process with them at each step of the project in order to gain their understanding of the true advantages AI could bring them. “When the captains and crews finally understood that AI could be the key to unlocking their own potential making their work even more efficient, that’s when our development speed really accelerated,” says Anthony. Thus, the AI-based navigation system steadily improved and evolved into a system that makes it possible to navigate efficiently through any type of weather.
So, has this AI-based navigation system made strides in nearing human capabilities? Captain Sj?str?m thinks for a moment before answering, “It hasn’t surpassed me yet, but it does feel like it is rapidly approaching that level”. When asked the same question, Anthony replied, “Compared to a new captain, it can maneuver more efficiently, but it is still far from the art form of an experienced captain”. Hitachi wants to create AI that can cooperate with humans to work better. In other words, AI-based solutions that contribute to the next generation alongside humans. He adds: “I will continue to seek new AI endeavors that will help leave our children with a better environment. If I only do what is certain, then not only will I fail to produce innovation, I won’t even be able to experience failure”. As Anthony says this with a smile, a whistle can be heard announcing a ferry’s departure from port. It was almost as if it was also signaling the new journey awaiting Anthony and AI.
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