Avinor and Veovo have developed an integrated platform that will optimise operations across all areas of the Norwegian company’s airports.
The strategic collaboration aims to driver better passenger experiences across Aviner’s 40+ airports and allow more cost-effective and reliable operations. As part of this programme, Avinor and Veovo will build data that reflects the processes of passenger and flight movements across each airport. This will enable service providers to continually improve performance by making automated and accurate predictions and putting actionable insights directly into the hands of staff, where and when they need it.
The Veovo machine learning platform will mesh data from multiple third-party systems and IoT devices to accurately automate predictions for multiple operational services and use cases across all time horizons. The programme scope includes providing accurate forecasts and capacity planning for passenger and baggage flow, check-in, security, and border control resource planning. It can also be used to provide predictions for baggage handling, concession footfall, shuttle services and terminal services such as cleaning, passengers with reduced mobility (PRM) assignments and transfers. This will enable Avinor’s airports to match the availability of services to demand at any time and proactively address any potential issues, improving both the passenger and staff experience while lowering the overall cost to serve.
The technology may also be used by Avinor to improve airside decision-making such as turnarounds, de-icing, gate allocations and maintenance planning. The prediction capability will extend between airports – if Avinor understands the impact of events in one airport on others in the Norwegian network, the operator can take early action to minimise disruption.
Lars Vågsdal, CTO at Avinor, said, “The Veovo machine learning platform was chosen after a comprehensive selection procedure involving technology pilots. Veovo’s ability to accurately aggregate multiple data sources and automate the delivery of accurate forecasts with no human intervention required stood out as a clear point of difference. We look forward to rapidly expanding its use across our airside and terminal processes.”