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4 examples of how machine learning can be used in the maritime industry


Martin Dommerby Kristiansen

CEO, GateHouse Maritime


March 03 2021

Are all businesses already using technology available such as machine learning, artificial intelligence, IoT (The Internet of Things), and others? 

Given the rush of new technologies marrying business functions, you might be forgiven to think that usage is rampant. 

It’s not, and this is particularly true for the B2B space. Even more so for the maritime industry.

For the Maritime Industry, a bulk of the infrastructure is decades behind global advancements in data management. Supply chain organizations struggle to collect and make sense of an overwhelming amount of data, all of which is scattered across different processes, sources, and siloed systems.

As far as the maritime industry and supply chain management is concerned, with the right tools to aid data visibility, you can optimize maritime routes, increase on-time delivery, and be transparent about the status of a shipment.

Instead of scrambling to investigate issues and responding to tracking requests, your customers can get all the information they need, all on their own (in real-time).

There’s a lot of insight and value to be unlocked by using data. Using artificial intelligence, machine learning, and IoT, your business can improve data operations and accelerate outcomes.

Accurate, real-time data along with visibility and collaboration lead to better decision-making, intelligent action, and an inherent edge when it comes to maritime operations leading to better operational efficiency, innovation and growth.

Here are some concrete examples below to show how machine learning aids in better data management, value-added data processing, and to provide overall value:

Predicting Vessels Arriving

You don’t want to just “guess” and “wing it” in the Maritime Industry. You’ll have a better edge (not to mention superior customer service, reliability of operations, and more employee productivity) when you are able to predict with confidence.

What if you could predict your vessel arriving 5 days out into the future with more than 97% accuracy?

Armed with Machine learning, The Gatehouse Destination Predictor allows you to avoid having to look at map to compile AIS information manually, update spreadsheets, and use up precious man-hours with mundane tasks.

Using the Gatehouse Destination Predictor, for instance, identify opportunities by knowing which ship arrives at any given port, upto 5 days in advance.

Gain a competitive advantage with all the data at your hand (unlike your competitors who are still “winging it” or “flying by the wire”) and sell services or provide better service by getting a pulse on ETAs beforehand.

Want to read more? See my post on a concrete example of #5DaysOut

Predictive ETA To Reduce Costs

The team at Project 44 wrote about how Predictive ETAs can affect your business and makes some excellent points on how to use predictive ETAs effectively for your business. You could streamline your business by:

– Make proactive adjustments based on knowing exactly when your shipment will arrive at a port or destination. This allows you to make adjustments to your supply chain (upstream or downstream), make changes to help reduce (or prevent) late fees or detention fees, and more.

– Use “arrival geo-fencing” to help boost the productivity of your teams by sending them well-timed alerts to notify them about vessel arrival times, shipment arrivals, and more.

– Reduce the need for manually tracking shipments, vessel movements, port docking time, ETA delays, and much more.

Read more about how Predictive ETAs can affect your business by Project44.

Predict Groundings and Boost Operational Efficiency 

We already know that advanced Machine Learning algorithms can help improve voyage optimization, manage fuel efficiency, minimize crew non-performance, improve travel costs, calculating the optimal routes for vessels, and also give optimum recommendations on speed, course, and more.

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.

That’s just the beginning of what the right combination of maritime tech stack can do. You could do a lot more by getting access to the right tools which provide you with data, predictive analyses, optimized decision-making inputs, and more.

By using Predict by Kraken Tools, you can:

– Predict upto 75% of possible groundings even before they occur. (Groundings account for more than 20% of the mishaps reported in the maritime industry).

– Analyze years of vessel traffic to analyze positions, speed, and course of journeys to accurately predict ETA.

– Stay ahead of vessels’ journey by staying on top of any conditions that can abruptly change the course of the vessels or if the destination port is changed. Predict calculates route patterns to reliably predict deviations from historical routes in real time, thanks to sophisticated machine-learning algorithms.

Leading with Clever Machine Learning Algorithms 

If you are in the maritime industry, just how do you make decisions? If it’s based on intuition, “gut feeling”, experience, or number of years in the industry, you are missing out on all the value that you can unlock using data.

The Gatehouse Maritime Data Foundation has access to plenty of data with over 273 billion data points. Having master data from over 4,000 ports and terminals worldwide, 250,000+ vessels, and more than 5M containers, you have all the data you need for decision-making, for tracking performance, and to help optimize your processes. The flow of combined data from ports, vessels and containers is possible thanks to clever machine learning algorithms on top.

With this data, for instance, you can predict ETAs of shipments, figure out when your vessels arrive at the “next port”, and analyze transhipment routes based on historical patterns while staying on top of real-time data.

These are only a few examples of how technology such as Machine Learning and artificial intelligence along with the right use of real-time and accurate data is set to transform the maritime industry.

4 examples of how machine learning can be used in the maritime industry

Martin Dommerby Kristiansen

CEO, GateHouse Maritime


March 03 2021


Data foundation