Data analytics in maritime/shipping

Big data is used to control sensors on a ship and to perform predictive analysis. Enhanced decision making through big data analytics is being actively implemented to avoid and predict additional costs and can be used throughout the life of a ship
In today's digital age, competition is fierce in a variety of industries, including the maritime industry, and companies are constantly investing in solutions that can help them increase productivity while lowering overall costs. Consequently, the demand for advanced solutions such as marine data analysis is growing at an impressive rate among commercial shippers and other end users. In the shipping industry, big data is used to control sensors on a ship and to perform predictive analysis to avoid delays and improve efficiency. Enhanced decision making through big data analytics is being actively implemented to avoid and predict additional costs and can be used throughout the life of a ship. The Port of Hamburg (Germany), the Port of Cartagena (Colombia), the Port of Rotterdam (the Netherlands) and several ports in Southeast Asia are actively using big data analytics solutions for their port and terminal operations.

Predictive analytics solutions have the potential to transform the shipping industry by improving overall shipping operations, enhancing ship safety and protecting the environment. In addition, the high level of customization offered by these solutions, depending on the specific needs of any port or shipping company, is expected to fuel demand over the forecast period. With the growth of globalization, the demand for freight transport will increase significantly in the coming years. Consequently, the demand for advanced data processing and predictive analytics will also grow among maritime companies to maximize time efficiency and cost savings. These factors are driving the demand for marine analytics around the world.

The shipping industry is a complex network of people, countries, agencies and authorities. These include shipowners, port authorities, maritime authorities, classification societies, cargo traders, oil companies and trade organizations to name but a few. This makes the industry a truly global enterprise. For example, a ship built in South Korea, owned by a Greek tycoon registered in Panama, manned by a crew from the Philippines, Singapore and Norway, could carry cargo owned by a US multinational company from a port in China to a port in Europe. passing through the waters of a dozen other countries. The requirement to track economic flows in this global supply chain while eliminating any legal nightmares has led to extensive industry record keeping. Some of these include:

  • Each ship has a cargo manifest and a crew manifest

  • Each ship also maintains a captain's log, a ship's log and other logs, which record the internal and external condition of the ship, including equipment and environmental conditions.

  • Ports, canals and waterways have many forms that you need to fill out to collect information about the vessel, voyage and cargo transported.

  • Additional records are maintained by shipping agents, companies, traders, marine insurers, certification agencies, etc.

Finally, ships generate huge amounts of electronic data such as AIS, LRIT, radar, etc. Electronic data is also generated by separate equipment on board as IoT sensors become more prevalent.
Given the variety and volume of data generated, Big Data in maritime and marine data analytics can be roughly divided into three groups:

  1. Vessel management using data available in various logs, manifests, system parameters, bunker statistics, etc. This will include efficient bunkering, better vehicle maintenance using digital twins, crew management, etc.

  2. Port and cargo management using data held by port authorities, freight forwarders, trading houses, etc. This will include efficient cargo handling, tracking goods, optimizing port facilities, etc.

  3. Analysis of spatial imagery using data from position tracking systems such as AIS and LRIT, images from ships, coastal and space radars, optical sensors, etc. This will include efficient routing, fleet tracking, traffic pattern analysis, anomaly detection etc. soon.

Until recently, records were mostly kept for short-term transaction history or for autopsy in the event of any incident. Modern analysis methods now allow us to use this data to predict and provide information to improve the system and prevent future disruptions.

Much needs to be done to adapt to the changing landscape of data, software. Like the Internet more than 20 years ago, data analytics and the Internet of Things will change the world around us. No company can do it alone anymore. The right investments and smart technology choices are the keys to digital transformation. Collaborative innovation will support the development of the industry today and prepare it for what unfolds in the future.

The COVID-19 crisis is evolving rapidly, posing major challenges to logistics, supply chain, shipping and maritime traffic. In this scenario, data analytics and technology adoption are expected to gain traction in the post-COVID phase, which is expected to stabilize the maritime industry and push it towards growth.

In Marine Digital we during the development process have formed 4 solutions that are suitable for charters, shipowners and fleet managers of different types of vessels for:

Read also Data Mining on ships. What does it take to leverage Big Data in maritime logistics?
TOP 5 factors contributing to lower fuel costs for Shipping companies
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Marine Digital FOS can be integrated with other system and third-party's solutions through the API. To implement vessel performance monitoring for any vessel, we are using mathematical algorithms, machine learning and the same equipment as in FOS. The more data we collect from vessels, the more precise reports and recommendations our system will perform according to your individual requirements in fleet management.

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