But it does not stop there. With further data maturity, visualisations can become descriptive – telling operators how their processes are running at check in, transfer, onloading, arrival and so forth so they can see the precise status of their operations at any given time.
And then there’s the digital twin – a digital layout of the entire system. This type of visualisation can be used as a storyteller, in which operators can view the baggage flow, or bags destined for specific flights, or those that are in danger of approaching gate closures or those that are within security level, or any other determined parameter.
And the data can be visualised from different perspectives:
- Operational: Where do we have the highest capacity? Which part of the system is consuming the highest power? Where do I have the most bottlenecks?
- Maintenance: Where are the most errors occurring and why? What needs attention now?
- Management: How can we plan ahead to cope with the summer peak this year? How should we schedule our resources?
And then there’s decision science – where data can make recommendations for operational decisions and even let the system carry out decisions automatically. Sørgjerd hopes Oslo’s digital infrastructure will help the decision making. He believes:
“It won’t depend on one person who has special knowledge. With machine learning and AI, the system will operate by itself so that the maintenance and operation people can have more of an observatory role, and they won’t be so critical in the hourly operations.”
And while many airports are struggling with operational challenges in the new post-pandemic airport world, Oslo is so far coping well. Sørgjerd says:
“By having more data, we can use the system differently, more proactively. We can actually change things before they become a problem. For example, we can address certain pain points in the system in different ways, such as bypassing bottlenecks.”