The past two years have been a steep learning curve for the aviation industry in its ability to predict and adapt to changing travel and fleet needs. Quick decisions have often been required following almost daily industry announcements, and long-term plans evaluated in accordance with the ever-changing circumstances.
As a result, digital solutions have increasingly been introduced in all areas of the industry, and MRO planning is no exception. Digitalizing MRO operations using powerful model-based AI technology enables a more holistic approach and allows planners to explore multiple real-world ‘what-if’ scenarios quickly and easily. It can model a whole MRO operation, regardless of its complexity or processes, in an intuitive, highly scalable, and secure system enabling a broad range of users to collaborate and make better decisions.
Creating these simulations enables planners to see the impact of their decisions on future fleet availability. This can identify and mitigate potential risks and also help understand the effect of changes to independent variables and future consequences or side effects. This results in the ability to adjust decisions and plan accordingly, optimizing key subsystems and fleet availability for optimal operational performance and confidence making in strategic goals.
How else can ‘what-if’ scenarios be used?
Although ‘what-if’ simulation modelling is routinely used for MRO planning, there are wider reaching benefits in other areas of the business. It’s possible to create a model of any key part of an operation or enterprise and ask ‘what-if’ questions to solve strategic problems.
For example, carriers may use it when deciding whether to open a new route from Europe to Asia, and explore what is the optimal way of doing this is. Decisions like these often have far reaching consequences across the organization. However, model-based AI technology allow scenarios to be immediately played out, strategies decided upon and adapted promptly, putting airlines in the best position possible to make a confident decision.
AI modelling can also be used by airlines for making decisions on how and when older aircraft are phased out. Should it be done simultaneously or in staged approaches? What are the costs, maintenance and repair implications of keeping an aircraft for one, five or 10 more years? Different case scenarios can all be modelled taking into account every single aspect of the organization in order to make an informed decision.
Sustainability planning
In the increasingly important area of sustainability planning, by using model-based AI, airlines can consider the implications of switching to Sustainable Aviation Fuel (SAF) across the entire fleet. They can assess key changes through ‘what-if’ planning, but also investigate how these are affected when individual parameters are changed within these scenarios, and therefore decide the optimal way to position themselves for this change.
An additional area that AI modelling can help airlines with sustainability is the reduction of carbon emissions. Using an intelligent model to simulate the operation of a fleet and its associated carbon emissions and formulizing the component parts of the system and their interactions, this technology allows for structured thinking about the environmental implications of fleet activity.
A model-based approach utilizes this simulation strategy and can be used to enhance decision making in two ways. Firstly, an analysis is carried out using a single framework to measure and examine a system or component’s carbon output. Secondly, this approach can produce a variety of ‘what if’ scenarios to explore potential future strategies relating to reducing carbon emissions. This enhanced decision making can be applied to model the carbon output of an entire fleet or sub-fleet of aircraft, maintenance facilities and individual engine, component and aircraft part utilization. This has a potentially huge impact on the ability for airlines to make decisions that will reduce carbon emissions in the most effective and efficient way whilst also analyzing cost benefits and wider implications.
Organizational benefits
Creating ‘what-if’ scenarios for different areas of an organization can result in numerous benefits. It increases productivity across teams as a result of reduced time spent planning and predicting outcomes, but also the ability to become more agile when dealing with unexpected events. It helps resolve complex strategic decisions when it’s possible to see the future consequences of a decision, and support teams to work collaboratively to create a shared, integrated plan that optimizes operational performance.
Model-based AI, unlike data driven AI, promotes safe and trusted decision making as it shows you how the system came to a specific decision, and the parameters that drove it, which gives organizations confidence in making high-level decisions.
Endless opportunities
With model-based AI, it is possible to gain valuable insights into the work that needs to be done and strategically plan for the future. Any operational part of the business can be modelled and used to help any key business decision across an organization or across the lifecycle of an asset – from design stages, to building an implementation to sustainment of that asset – and it’s important to make the most of this technology to realize the true potential of a business.
