Global fleet: slow steaming. File image/Pixabay.
Marine technology company Greensteam believes that its machine learning-based vessel optimisation software can add value to ships, saving operators money while making their vessels more efficient.
While the company began life in 2006, machine-learning is relatively new in the marine space.
Machine learning leaves no data out to get to its ultimate goal of greater operational efficiency. In contrast, a non-machine learning approach will take a simplified base case using less data and build efficiency strategies from that.
Chief operating officer Simon Whitford told Ship & Bunker that machine-learning "is a more sophisticated approach to vessel optimisation".
Slow steaming is being suggested as way for the global fleet to address its emissions output but Whitford regards such a strategy inefficient.
With its data-rich approach, Greensteam can develop a 'speed profile' for a ship which gives vessel owners and operators a better understanding of vessel performance.
The software must be paid for but with no capital expenditure required and no need to take a vessel out of service for installation, it pays for itself fairly quickly, according to Whitford.
One of the company's first clients was Danish ferry operator DFDS. Greensteam is headquartered in Lyngby, Denmark.