Digital twins transform crew transfer vessel operations
Our digital twin technology enables predictive maintenance and smarter asset management, cutting costs and improving efficiency for CTV operators.
Our digital twin technology enables predictive maintenance and smarter asset management, cutting costs and improving efficiency for CTV operators.
Crew Transfer Vessel (CTV) operators face mounting pressure to improve reliability, reduce operational expenditure, and ensure vessels are available when needed.
Traditional maintenance and planning approaches often react to issues after they occur, leading to downtime, inefficiencies, and higher costs.
A proactive, data-driven solution was needed to support both vessel maintenance and operational planning.
We are working with CTV operators to develop digital twins of their vessels, powered by the BMT Deep toolset.
These digital twins integrate real-world data from onboard sensors and systems, creating a virtual model that mirrors vessel performance.
Machine Learning algorithms process the data to uncover hidden patterns, enabling predictive maintenance and optimized asset management.
Continuous data feeds ensure operators have a real-time view of vessel health and performance, supporting proactive decisions.
With digital twins, CTV operators can predict maintenance needs before failures occur, minimising downtime and reducing operational costs.
Real-time monitoring improves efficiency, while advanced analytics support smarter operational planning and asset management.
By leveraging lessons learned from other industries, our approach accelerates insights, giving operators a competitive edge.
The result is safer, more reliable, and cost-effective vessel operations.