Navigating the Future: Harnessing AI and ML in the Maritime Industry


United Kingdom


2021 – 2022


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About the Client

The company, to which Amazinum provided its data scientists, is engaged in the optimization of processes in shipping. It is the developer of certain pro-game solutions, services and platforms that have changed the policy in the maritime market.

Business Context

Data Scientists joined our client’s team to implement Data Science and Data Analytics solutions. The main task that was set before our professionals was to structure data on the movement of ships, and information on berths and ports. The solution was developed to make it easier and more automated for users to work with the shipping industry.

Among the tasks that were set before our data scientists were:

  • Predict the price of unloading and loading goods onto the ship;
  • Determine the reliability of ports;
  • Sorting varieties by similarity;
  • Predict patterns and popularity of ports.

Predict the Price of Unloading and Loading Goods Onto the Ship

The Amazinum Data Scientists team was already working with ready-made data provided by the client. The task set before us by the customer was to predict unloading or loading prices based on certain metrics. To fulfill the tasks, our specialists took data on port services for the previous time and trained models based on them. Among the selected models were the Prophet model, which predicts time data based on seasonality, and the Darts library for time-series forecasting and anomaly detection using state-of-the-art models.

Among the metrics that Amazinum specialists took into account were:

price graph in relation to certain metrics
A graph showing the price in relation to certain metrics.
price graph of port services
results graph

After training the model, the results were checked, and the ready data in the form of a graph was provided to the client.

Determining the Reliability of Ports

The next task that the client set before us was to determine the reliability of the ports. For this, Amazinum’s Data Scientists collected statistics on price fluctuations, such as mean value, standard deviation and variance.

Ports were ranked by these values and reliability was determined by using metrics such as deviations per port and standard deviation for each country, continent, etc. Based on them, the risk level of the ports was determined.

cost map 1
cost map 2
cost map 3

Forecasting Trends

Another of the tasks that the client set for our data scientists was to predict trends related to ports. For this, Amazinum specialists used the Prophet model, which forecasted time series data based on an additive model, where non-linear trends are consistent with annual, weekly, and daily seasonality, as well as the impact of holidays. In this way, our specialists determined whether there is any regularity or trend for ports in relation to the time metric.

Outcome of AI and ML in the Maritime Industry

The implementation of Artificial Intelligence (AI) and Machine Learning (ML) in the maritime industry has ushered in a transformative era of efficiency, safety, and sustainability. With AI-powered technologies becoming increasingly integrated into various maritime operations, the results have been nothing short of revolutionary.

Enhanced Safety and Predictive Maintenance

AI and ML algorithms analyze data from sensors, historical maintenance records, and real-time inputs to predict equipment failures and maintenance needs. This proactive approach prevents potential accidents and costly downtime, ensuring safer and more reliable maritime operations.

Optimized Routing and Navigation

AI-driven route planning algorithms take into account factors like weather, sea conditions, fuel consumption, and vessel traffic. This results in more efficient routes, reduced fuel consumption, and minimized emissions, contributing to both economic savings and environmental conservation.

Automation of Repetitive Tasks

Routine tasks such as data entry, documentation, and monitoring can be automated using AI. This not only reduces human error but also allows maritime professionals to focus on more strategic and complex tasks, ultimately improving overall operational efficiency.

Advanced Collision Avoidance

AI-powered collision avoidance systems utilize real-time data from sensors and radars to predict potential collisions and provide recommendations to the crew. This technology significantly reduces the risk of accidents and enhances vessel safety.

Cargo Handling and Logistics Optimization

ML algorithms optimize cargo loading, unloading, and storage, ensuring that vessels are loaded efficiently while maintaining stability. This reduces turnaround times in ports and maximizes cargo capacity utilization.

Environmental Monitoring and Compliance

AI can monitor vessel emissions, water quality, and adherence to environmental regulations. By doing so, it helps the maritime industry meet stricter environmental standards and fosters sustainable practices.

Improved Decision-Making

AI-driven analytics provide maritime operators with valuable insights from massive amounts of data. This assists in making informed decisions related to fleet management, route adjustments, fuel procurement, and resource allocation.

Crew Assistance and Training

AI-powered tools can provide on-board crew with real-time assistance, such as identifying equipment malfunctions and suggesting troubleshooting steps. Additionally, virtual simulations and training powered by ML enhance crew skills and readiness.

Economic Savings

The integration of AI and ML leads to cost savings through reduced fuel consumption, optimized operations, minimized downtime, and efficient resource allocation. This can positively impact a company’s bottom line.

Innovation and Competitive Edge

Companies embracing AI and ML gain a competitive advantage by staying at the forefront of technological innovation. They are better equipped to adapt to changing industry trends and customer demands.


Vitaliy Fedorovych

CEO, Data Scientist at Amazinum

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