Understanding how the vessel performs in real operation is a cornerstone for optimizing the fuel efficiency and technical maintenance of the vessel. Traditional engineering methods can predict the vessel performance but they are developed and thus are only reliable for ideal conditions. Today, the knowledge of how the vessel really performs in actual operating conditions is not very well known


Methods which combine engineering methods with machine learning models were developed, in order to make it possible to get more accurate and self-learning models for real-life performance of ships. In the framework of the project, the research was focused on developing suitable models which are applied on measured operational data from the vessel. Using such models requires a lot of computation power and thus a framework has been developed to allow the automatic analysis of the data in cloud environments. The results of the analysis are presented to the end user through a web-based business intelligence tool.

Part of the work was to establish automatic and reliable data communication infrastructure to gather data on board and to transfer it to the shore based system, providing sufficient access for globally distributed user base.

Validation of the results was carried out in cooperation with major organizations in the international maritime field (ship owners, operators, classification societies and shipyards).

Example ship performance reports from the shore based system


With the rise of environmental awareness and economical pressure from the ever increasing cost of fuel, the ship designers and ship operators are in pursuit of optimizing the ship design and ship maintenance program to minimize energy consumption of the ship. Traditionally there has been no means to accurately know the ships performance in real operation. In ship design a fixed, pre-defined “sea margin” has been used to prepare for the increased power need in real operation, without good knowledge of what is the real power need in actual operations. In ship operations, the maintenance program of the vessel has a significant effect in the energy consumption. However, without accurate information about ship’s performance, it is impossible to know if, e.g. hull cleaning should be performed once a month or once a year. With accurate information the optimum maintenance program can be determined.


The tool / system has been taken into full operational use with selected customers of NAPA. The customers have an online access to see the performance data of the ships in almost real time. Any deviations in the performance can be seen and possible reasons discovered with flexible data analyzing tools as part of the system.

For NAPA, the tool brings new business opportunities, and the cloud based architecture allows system to be easily scalable for any number of customers. ”We have utilized the tool to evaluate the overall model from fuel to operations. This tool gives us insight and simulation possibilities to enhance the NAPA Voyage Optimization model and NAPA for Design tools. This gives us a more throughout view to serve integrated maritime market from design to operations in our commercial applications“ Jouni Salo Product Manager, Shipping Solutions NAPA for Operations.


Esa Henttinen, NAPA




Author missing

Tommi Vihavainen