How route and aircraft management lowers fuel use and emissions | Finnair United Kingdom
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How route and aircraft management lowers fuel use and emissions

Through optimizing fleet use Finnair is making a big impact on fuel efficiency. Several custom designed tools help keep costs and emissions down.

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Finnair is a front runner in utilizing flight data to create solutions for better fleet optimization. Route and aircraft management are always done with fuel efficiency as one of the factors, the objective now is to find even more efficient ways to lower fuel use and emissions.

Finnair has created a custom-made Delay Cost model, that enables users to see the financial impact of flight delays.

- Around this model we have been able to build a variety of optimization tools, says Pertti Määttänen, Finnair's Head of Operations Control Center Development and Flight Planning.

One of these tools used daily is Tail Swapper. It makes it possible to change the type of aircraft swiftly if there is a change in the number of passengers. It helps avoid flights being operated with an unnecessarily large aircraft and save fuel accordingly. 

- Especially during the corona pandemic there has been a constant need for small adjustments and this tool has given us great results. We will continue using the tool, however, in addition to downsizing aircrafts we are likely to be upsizing more and more, Määttänen explains.  

Another tool is used by technical operations to optimize wide-body traffic. The fuel use between different wide-body aircraft types varies greatly. The tool helps find best aircraft and route combinations, so that longer routes are operated with planes using the least fuel.

Better insight regarding cost

The latest tool has been introduced recently. It helps optimize long haul traffic in a way that balances flight speed and fuel use. The new optimization solution delivers data on delay related costs to the cockpit during the flight. The tool makes it possible to estimate whether a short time saving is worth the extra fuel it will require.

- From now on we will get information on delay cost, as well as fuel cost. Using the optimization tool, we can now consider what is the best arrival time, so that both costs are minimized, Tom Hakala, Fleet Technical Pilot explains.

Hakala sees the tool as a great step ahead and a genuine opportunity to use available information fully.

- Up until now the pilots have not had access to all available data and optimization has been done using flight schedules. Individual pilots have had to make very independent decisions, without visibility into true cost, he describes.


Environmental savings follow the optimization of fuel use

Optimization also creates impressive savings to carbon emissions. According to Finnair calculations the work done is going to create a saving of 3200 tons of CO2 in 2021 and for 2022 a saving of 10 000 tons is expected. This equals the yearly emissions of around a thousand average Finnish consumers.

- Our emission objectives are ambitious; we aim to be carbon neutral by 2045 and cut our emissions by 50% by 2025 from the 2019 level. We follow the situation carefully through our fuel index meter and can see the status of different aircraft types and routes. We can also see how various changes impact the results, Määttänen says.

Both Hakala and Määttänen see, that Finnair is at the vanguard in creating solutions for now – and for the future.

- I believe we are one of the first ones to use data in a way that helps us work together with our pilots in optimizing delay costs. We are now planning to expand this from long haul traffic to narrow-body traffic, Määttänen mentions.

The development on many of the solutions is on-going. For example, the application for technical operations will also be used for narrow-body fleet in the future. The tool is also now adjusted so that it can be used closer to the flight date. This gives the operative command centre even better options to fine-tune fleet use and ensure optimal management.

- We are also building a model based on machine learning to help us predict delays. Our goal is to combine this to our Delay Cost model. If we can see future problems earlier on, we can be even more efficient and produce better services for our customers, Määttänen finishes.  

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