Meteomatics Data Improves ABS Wavesight's Navigational Safety and Sustainable Shipping

ABS Wavesight is adding on-demand, high-resolution historical and forecast weather data on its ABS My Digital Fleet™ risk management platform together with Meteomatics.

This latest addition to the ABS My Digital Fleet Alliance Program unlocks weather data that enables actionable insights for users to help them understand and lower fuel consumption, improving bunker costs and carbon intensity levels. Additionally, the potential structural impacts from weather exposure during a ship’s voyage are also surfaced helping to better understand and reduce risk.

ABS My Digital Fleet is the only customizable risk management platform that seamlessly integrates data to provide real-time insights for driving sustainable operations and reducing operational risks. The ABS My Digital Fleet Alliance Program nurtures an ecosystem of industry trusted intelligence and technology providers enabling integrated insights for clients on one unified platform.

“When it comes to voyage performance and charter party compliance, up-to-date weather data plays a key role. We are now able to overlay the most up-to-date, weather data on the map and have on-demand forecasts for voyages. This allows for operations to react by adjusting timetables and routes, providing efficiencies in fuel consumption, more optimized ordered speed, increased reliability in arrival times and improved navigational safety” said Heather Zimmer, Global Director of Alliances at ABS Wavesight.

Meteomatics is the perfect match for ABS Wavesight due to powerful hyperlocal weather data with high accuracy forecasts, which when combined with ABS’ machine learning capabilities on ABS My Digital Fleet, will be a game changer for the maritime industry in its journey towards digitalization and decarbonization.

This case is an impressive illustration of what is possible with high quality weather data in the field of digital planning and artificial intelligence. Our data can immediately and easily be integrated into existing systems, allowing any AI or machine learning to be trained directly and efficiently. This quickly leads to noticeable improvements and efficiency gains in many kinds of operations. Additionally, the case also shows how relevant real-time weather data is during a journey to arrive at the desired destination as efficiently and safely as possible.