How to use machine learning in maritime intelligence

Authors: Hans De Hondt , Qronoport , Stanislas Oriot , Opsealog , David Levy , OrbitMI Inc ?

Machine learning, integration and interoperability are key components of digital transformation?

The maritime industry is under pressure as it looks to reduce costs, meet environmental regulations and improve safety. One way it is looking to do this is through digital transformation – the use of new technologies to improve performance. Many maritime businesses are now using digital technologies such as sensors, big data and the Internet of Things to improve efficiency and reduce costs. For example, sensors can be used to monitor engine performance, while big data can be used to track trends in maritime traffic. The Internet of Things can be used to connect ships, ports and other maritime infrastructure, improving coordination and efficiency. By harnessing these technologies, the maritime industry can become more efficient, safe and sustainable.?

Machine learning is a powerful tool that can be used to glean valuable insights from data. By automated processing and analysis of data, machine learning can identify patterns and correlations that would be difficult or impossible for humans to discern. This can be extremely useful for businesses, who can use machine learning to improve their products and services. For example, by analyzing customer data, businesses can identify trends and target their marketing efforts accordingly. Additionally, machine learning can be used to improve the accuracy of predictions and forecasts. By understanding past patterns, businesses can make more informed decisions about the future.??

As businesses increasingly rely on data to make decisions, it's more important than ever to avoid information silos. When data is spread across multiple systems and data sources, it can be difficult to get a holistic view of what's going on. And even when you avoid information silos there is still the risk of using data that is neither granular enough nor provides the necessary level of detail. This can lead to decision-making that's based on incomplete or inaccurate information.?

?Integrating multiple systems and data sources can help to avoid this problem. By bringing all of the data together into one place, it's easier to get a complete picture of what's going on. This can help to improve decision-making and ensure that businesses are using the most up-to-date information possible.?

There are a number of different ways to integrate multiple systems and data sources. One approach is to use a data warehouse. This is a central repository where data from all of the different sources can be stored and queried. Another approach is to use a data integration platform, which can help to automate the process of integrating data from multiple sources.?

In the world of software development, interoperability is key. That's why developers often turn to specialized systems that are designed to work together seamlessly. By doing so, they can take advantage of powerful features and speed up the development process. However, interoperability is not always easy to achieve. Different systems often use different technologies, and often different data standards are used to communicate, which can make it difficult for them to communicate with each other. Fortunately, there are a number of ways to overcome this challenge.??

Whichever approach is used, the goal is the same: to avoid information silos and make sure that businesses are using the best data possible to make decisions.??

When it comes to vessel performance and port call optimization, using solutions based on machine learning require standardised, high quality and high frequency datasets. The maritime industry has started to collect these. Using software to input, normalise and process data is the first step of the ladder towards fully integrated and interoperable systems powered by machine learning.??

Another common solution is to use an application programming interface (API) that allows different systems to interact with each other. Another option is to use middleware, which is software that acts as a bridge between different systems.?

The analysis derived from these that will actually transform the data in actions to extract value from it remains a challenge for many in the industry. Ultimately these solutions exist to enable human decision making.? Let’s call it the “human side” of machine learning: humans are coding the algorithms that drive the analysis, and humans are still the ones actually making the decisions.?

By using these and other strategies, and by keeping these thoughts in mind, developers can ensure that their systems are interoperable and can take advantage of all the powerful features that specialized systems have to offer.??

About the Authors?

Hans De Hondt , Qronoport ?

Hans De Hondt is responsible for sales and marketing of Qronoport, a port call optimization platform aimed at optimizing the turnaround time of tankers in ports. Qronoport brings together port call stakeholders, with charterers and cargo owners leading the way for other port call stakeholders towards true port call innovation. Qronoport uses data sharing, machine learning, and AI, to generate port call visibility and insights, facilitate digital collaboration and automation, and support better decision making. You can find out more at www.qronoport.com ??

Stanislas Oriot , Opsealog ?

Stanislas Oriot is a Marine Consultant Manager, and former Merchant Marine Officer with a degree in energy management. He is passionate about energy transition and how data analysis coupled with powerful communication can bring about the change our economies need.?

Opsealog specializes in performance management and reduction of the environmental impact for the offshore energy and maritime sectors. They provide shipowners and charterers with value at several levels from digitization of the data collection, data integration & analysis, and consulting.

For more information, visit https://opsealog.com and email [email protected]?

David Levy , OrbitMI Inc ?

?David Levy is Chief Marketing Officer of OrbitMI, a maritime software company headquartered in New York City with offices in Sweden, Norway, and Serbia, whose purpose is to unlock the value in data to help maritime become more efficient, profitable, and sustainable. Its product Orbit is a suite of integrated SaaS business solutions that transforms raw data into actionable insights and predictive intelligence.?

For more information about OrbitMI, visit https://www.orbitmi.com and email [email protected]

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