George Zervos will be attending the Digital Ship Athens Conference on the 13th & 14th November. If you would like to arrange a meeting with George at the event to discuss any of our solutions please let me us know.
Looking forward to seeing you there.
Digital Ship Athens – now in its 17th year – reviews the most important topics of maritime digitalisation, in the world’s largest centre of shipping and technology. We are at early stages with the agenda but here are some of the topics we might be discussing this year.
Satellites – A discussion about the current changing situation with L band, VSAT and LEOs, including with the new LEO systems like Leosat and SpaceWeb. Are the new LEOs a threat to L band and VSAT? Or is this competition which the traditional providers are comfortable facing head-on?
Cybersecurity – what are the precise concerns for shipping companies? Are the risks misplaced or exaggerated? Are we properly separating the concerns of hacking navigation equipment, corporate software, shipboard systems and shipboard software?
Onboard systems – are our digital systems fit for purpose for what we want to do? Are we thinking more about controlling seafarers rather than supporting them? Do our technologies make it as straightforward as possible to safely navigate a vessel.
Digitalisation project management – companies are increasingly putting people in the role of ‘project managers’ for new digitalisation projects – which is quite a different role to the traditional ‘IT manager’. We’ll discuss the new role of project managers and what they do.
Fuel efficiency data – companies are increasingly under pressure to reduce fuel consumption, but gathering and analysing data from vessels, to a point where it can be used to make decisions, in a world where nearly every vessel is different, is far from easy. Are there ways it can be easier to gather and share data and use it to drive decisions?
Predictive maintenance – companies have been talking about predicting which parts will fail before they fail since Digital Ship Athens started. But are they any closer to this goal? Are there machine learning techniques which are proving to work, in spotting patterns in the sensor data which indicate specific problems emerging with equipment?