Case studies

Vooban identified three key processes to focus on in order to help CSL achieve its objectives: improving the accuracy of vessels' estimated time of arrival (ETA), reducing average fuel consumption, and lowering CO2 emissions.

Following the implementation of an AI model, the ETA can now be calculated with high accuracy, increasing its reliability by 30%. We have also created a second model that predicts fuel consumption with 96% accuracy. Additionally, the AI can plan the optimal timing to carry out antifouling maintenance on ships, as the build-up algae on the hull can affect up to 10% of a vessel's speed.

By accurately establishing these key parameters, it becomes possible to simulate the ship’s efficiency. By managing speed and optimizing routes, we can reduce fuel consumption, and therefore, GHG emissions. The near-exact prediction of the ETA also helps optimize operational timing and enables more efficient planning of activities.

Over the next year, this AI project is expected to significantly the CO2 emissions of CSL’s Canadian fleet and lower its average fuel consumption. 

By accurately estimating ETA, the company's overall efficiency is increased. When operations like crew changeover planning, navigation synchronization with dock and lock availability, and material delivery coordination are optimized, the overall activities become more efficient and seamless. These improvements allow CSL to further strengthen its supply chain reliability and cost management, truly enabling the company to showcase maritime ingenuity around the world!