Future-Proofing MRO Operations: Using AI To Stabilize Costs and Predict Pinch Points in 2023 Planning

April 17, 2023
For all airlines, stable financial plans will be high on the agenda for 2023. But, with so many elements still up in the air, how can carriers stabilize costs and predict where the pinch points may lie in the coming months and years?

For all airlines, stable financial plans will be high on the agenda for 2023. But, with so many elements still up in the air, how can carriers stabilize costs and predict where the pinch points may lie in the coming months and years?

In today’s highly complex and dynamic aviation environment, digital transformation is fundamental to optimizing and future-proofing MRO operations. With civil aircraft costing many millions of dollars to buy and maintain over their lifespan, MRO planning must be as efficient as possible.

As a world-leader in AI-powered forecasting and planning, we understand that there are many financial aspects of an airline’s operation that are often overlooked. For example, needing to negotiate new MRO contracts, and being able to visualize multiple offerings to assess impact on cost and ground time. All of these elements should be factored into short and long-term planning.

Innovative airlines are increasingly leveraging digital and AI technologies to analyze financial uncertainty and mitigate the risks in achieving business plans.

Why adopt AI?

Airlines are adopting AI technologies to improve their decision-making, enhancing their capabilities, performance, and productivity. Using powerful model-based AI technology to digitize MRO operation forecasting allows planners to explore multiple real-world ‘what-if’ scenarios quickly and easily.

Model-based AI is a predictive technique that allows the user to simulate future scenarios and, unlike more opaque AI methods, interrogate the outputs to have confidence that its predictions are justified. A model represents processes and responsibilities present in the real world through digital analogs known as “agents”. In using this approach, companies are able to create a digital twin of their aircraft fleet, representing the entire operation, and run simulations of different scenarios to forecast potential outcomes.

In each simulation, the behavior of agents replicating different parts of the operation are driven by parameters set by the user. For example, the MRO agent in the model can be set up to operate at different capacity levels and analyzed on how it performs. Ultimately, these models produce predictions on key performance indicators such as cost, availability, capacity, or utilization.

Utilizing AI to stabilize costs and predict likely pinch points

Organizations can integrate model-based AI into their MRO planning to investigate the cost implications of different operational decisions. Users can configure their models in different ways to evaluate how operations will run if they encounter problems, which could impact efficiency or financial targets.

Variables such as the severity of maintenance needed on an aircraft, or an unexpected increase of ground time for a shop visit or overhaul, create cost instability for airlines. Use of model-based AI technology empowers users to plan for when large or small maintenance events are due, and the associated costs that will occur. Planners can add each individual MRO shop or office as an agent, isolating a specific area they want to examine further. You can adapt quickly to unexpected changes in your plan – they can be added into the model seamlessly, and the impact of those changes can be seen instantly.

With the uncertainty the aviation industry has faced over the last three years, running ‘what-if’ simulations for different areas of an organization allows airlines to be more agile when dealing with unexpected events. Potential pinch points this year include paying more for parts which are in short supply or having to wait longer for a maintenance slot due to staff shortages, as the industry adapts to pre-covid levels of demand.

Intelligent ‘what-if?’ analysis of different scenarios and detailed simulations help companies examine these issues, identify which scenarios are most likely to occur, and help make better business-critical decisions. For instance, if a forecast anticipates aircraft maintenance delays due to specific spares being unavailable, users can plan and simulate the best reaction to this by modelling the impact of an increasing or decreasing spares pool, and ensuring they can keep future operations working smoothly.

Aerogility enhances planning and forecasting by building on existing human knowledge and experience. It helps users better understand the likelihood and severity of variables in the business in terms of financial impact. This scenario modelling results in realistic, safe, and trusted planning that helps contain costs and reduce volatility.

Phil Cole is the business manager for civil aviation at model-based AI provider Aerogility. On any given day, Cole could be testing a new release, traveling abroad to demonstrate Aerogility’s software to a prospective customer, or helping a customer solve their complex scheduling and planning issues. Cole began his career as a project manager at Keane NTT, an IT services unit of NTT Data Corporation. After gaining eight years’ experience, he joined computer software company, Lost Wax Media, in 2006 and joined Aerogility just over 10 years later.