How can airlines effectively forecast their 2021 MRO needs with reduced budgets and resource? Aerogility’s civil aviation business manager Phil Cole might have the answer
[This feature first appeared in the March 2021 issue of MRO Management, which you can read in full here.]
The 2021 outlook for MRO has changed dramatically since predictions were made last year, or even six months ago.
Today, MROs and airlines are having to operate with reduced team sizes due to forced redundancies and social distancing measures, while facing cuts to MRO budgets and spend for the foreseeable future. If this is the case, how can MRO planners reduce their fleet downtime and improve efficiency when forecasting maintenance needs alongside these restraints?
Plans now need to be even more adaptable to changing circumstances, yet less time and resource is available to prepare forecasts.
This is where technology comes in. There has been much discussion across the aviation industry about the ‘digital transformation’ taking place during the downtime experienced in recent months. Maintenance planning is no different. One technology that is now coming into its own and seeing more adoption in the face of such uncertainty is model-based AI.
What is model-based AI?
This type of AI involves software models utilising ‘intelligent agent’ technology. In an aviation setting, the agents represent assets, processes and decision-makers in a holistic model of an airline operation. The model realistically simulates a fleet of aircraft accruing life, and forecasts the whole commercial lifecycle for each aircraft, including the maintenance and engineering operation.
The benefit of model-based AI is that the simulation process is more transparent and the outputs are completely explicable compared to some data-driven AI. The simulation considers multiple factors; optimising when a scheduled maintenance or inspection should be carried out, introducing modification or upgrade programmes and optimising scheduled maintenance on key subsystems, such as landing gear and engines.
The unprecedented events of 2020 led to cancelled flights and the grounding of aircraft, border closures and restrictions, and significantly reduced capacity demand. These issues are likely to remain during the majority, if not all, of 2021.
In addition, re-entry into service for currently grounded fleets will mean more pressure on MROs, re-prioritisation of servicing and additional cost considerations this year. The use of an intelligent agent model, such as Aerogility, enables planners to quickly adjust operational constraints to assess the impact of these changes and ensure they always have the best possible plan in place.
So how will technology improve the effectiveness of maintenance planning amid so many unknowns? The past year has been a learning curve in terms of being able to predict and adapt to changing fleet needs. Quick decisions needed to be made following almost daily industry announcements and long-term plans evaluated in accordance with the new circumstances.
During this time, model-based AI has become relied upon as an effective solution to planning cost-effective maintenance schedules, maintain efficiency and juggle reduced resources. Not only does this technology save huge amounts of time on re-entering data and manually playing out a variety of different scenarios on a spreadsheet, it also allows airlines to make quick and qualified decisions based on new information or government announcements, something that is crucial to the survival of many carriers.
With government restrictions, travel behaviours and capacity levels expected to continuously adjust in 2021, short-term reactive MRO planning is here to stay and, in turn, changing the way airlines have traditionally forecast maintenance needs.
Technologies such as model-based AI that allow scenarios to be immediately played out and strategies decided upon and adapted promptly will put airlines in the best position possible when it comes to adjusting to flight schedules over the coming months, and being prepared for a return to travel.