Digitalising the BHS: How data is helping drive Oslo Airport’s business strategies

Avinor, the owner of Oslo Gardermoen Airport, has taken a data-driven approach to implementing a new baggage handling system. What does this mean and how is it helping the airport achieve its strategic goals?


We talked with Idar Sørgjerd, Head of Baggage Handling at Avinor about data-driven asset management and what it means for Oslo Airport.

Oslo Airport’s strategic ambitions

Oslo Airport is the largest of 43 airports that Avinor operates in Norway.

As the gateway to Norway, Avinor has big ambitions for Oslo Airport. Sørgjerd explains:

“We have visions of Oslo being a central Nordic hub airport in the future with traffic volumes passing 30 million passengers per year. We want to be in the top three airports in Europe for punctuality. And in keeping with what Norway has to offer in terms of its waterfalls, mountains and fjords, it’s very important for us to be a green airport.” 

But, continues Sørgjerd, Norway is a country with a high cost of living, and matching high labour costs. So, when it came to investing in a new BHS as a key element of Oslo Airport’s development, Sørgjerd explains:

“We were looking for new technology that would enable the airport to use its resources more efficiently, within its existing building footprint, with lower O&M and spare parts costs and more predictable processes, in a way we could develop our existing and future revenue streams and meet our sustainability goals.” 

Investing in digital infrastructure

It was for these reasons that Avinor decided to invest in data and digital infrastructure when deciding on the advanced ICS BHS. It believes that the integration of the latest technology would help Oslo Airport in future-proofing its operations and enable it to deliver forward-thinking operations based on data-driven decisions.

By integrating the latest technology with its BHS, this is what Oslo is hoping to achieve, says Sørgjerd:

  • No more system stops or bottlenecks.
  • Moving from calendar-based  maintenance to condition-based maintenance. The system can direct operators when elements need changing.
  • Leaner and more flexible operation of the maintenance team. We have been able to reduce our maintenance team to just four workers per shift.
  • Higher situational awareness so operators can understand what’s going on at any time.
  • Higher system utilisation.
  • Data-driven dialogue.

But how is digital infrastructure in baggage handling able to achieve this?

Avoiding four performance traps

Baggage handling systems, Sørgjerd observes, can fall victim to four common performance traps:

  1. Unwanted human behaviour – typically, bad habits and lack of training.
  2. Inadequate processes – a tendency to do today what we did yesterday when it comes to allocating equipment and staff, resulting in unused available capacity.
  3. Failing infrastructure – typically alerted through the airport’s SCALA system.
  4. Undetected deterioration – failures occurring in small, incremental and undetected steps that slowly eat away at capacity.

Sørgjerd shared his experience of a data-driven approach in a co-presentation he gave with BEUMER Group’s Per Engelbrechtsen at the 2022 Passenger Terminal Expo.

By using data-driven asset management, Sørgjerd says, Oslo hopes to avoid these conventional performance pitfalls that impact its capacity and efficiency.

What is data-driven asset management?

Data-driven management is about maximising the value of data and treating it as a strategic asset and then using it for innovation and critical business decisions.

Sørgjerd says:

“Becoming data-driven involves linking a data strategy to clear outcomes and prioritising all the data we collect as a strategic asset. By embedding data analytics, AI and machine learning at our baggage handling core, we can enhance the value of data to help drive our goals such as reducing power consumption and operating with a leaner staff.”  

This is achievable through the collection and visualisation of data.

Data-driven asset management through different levels of visualisation

The visualisation of data occurs on a number of levels, says Sørgjerd, by assigning different representations to exactly the same data.

At the start of the data visualisation journey, data is displayed in dashboards that provide a picture of what’s happening in the baggage handling assets. But dashboards can also be confusing, presenting too much data to be useful.

Adding another layer of analytics can, however, organise data to be visualised in more helpful ways. This extra layer can translate data into strategic KPIs in different areas of baggage handling processes, for example, or the overall equipment efficiency.

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.”

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