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Dynamic Speed Optimisation for Vessels

Most shipping companies still rely on simplified assumptions to set out routes for voyages, leading to unoptimised fuel consumption.

Weather service providers and captains do not have access to granular speed and consumption data and, as a result, are not able to consider the true speed and consumption profile of the vessel when setting the route.

With our dynamic machine learning models, operations teams can draw upon a dataset that accurately depicts expected performance in any given draft and weather condition.

This unlocks new revenue and profit opportunities by generating a voyage speed or RPM instruction that maximises a vessel’s TCE profit.



Download to find out more.

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DOWNLOAD WHITEPAPER

Dynamic Speed Optimisation for Vessels

Most shipping companies still rely on simplified assumptions to set out routes for voyages, leading to unoptimised fuel consumption.

Weather service providers and captains do not have access to granular speed and consumption data and, as a result, are not able to consider the true speed and consumption profile of the vessel when setting the route.

With our dynamic machine learning models, operations teams can draw upon a dataset that accurately depicts expected performance in any given draft and weather condition.

This unlocks new revenue and profit opportunities by generating a voyage speed or RPM instruction that maximises a vessel’s TCE profit.



Download to find out more.

DOWNLOAD

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