In recent months, headlines about supply chain disruptions, parts shortages, staffing and training issues, and delays across the ecosystem have dominated – all with a backdrop of an appetite to improve sustainability measures.
Predicting how these many factors will impact each other and the overall fleet availability, and mitigating them early by adopting AI-based digital twin technology, is one way that many of the industry's leading organizations are addressing these challenges.
Understanding Digital Twin Technology in Aviation
A digital twin enables users to develop virtual replicas of their operations, providing unmatched insights, efficiencies, and AI-driven analytics to support decision-making. This can be constructed using software such as Aerogility's model-based multi-agent AI framework. In this context, 'agents' represent the different elements of an operation, including equipment, processes, and people.
From optimizing small and heavy base maintenance to simulating sustainability measures and modeling unpredictable challenges or disruption such as staff shortages or supply chain delays, digital twin technology is key to boosting operational efficiency and making informed strategic decisions.
Enhancing Maintenance Processes with Digital Twins
Base maintenance, whether small (line maintenance) or heavy (C and D checks), is critical in ensuring an aircraft’s longevity. However, it also represents a logistical challenge. Typically, maintenance tasks are scheduled based on predefined intervals, which can lead to inefficiencies including unnecessary downtime or premature part replacements.
One of the key advantages of digital twin technology in base maintenance is its ability to enable scenario planning and MRO forecasting. Through advanced modeling, companies can simulate a variety of operational conditions, providing detailed insights into how different maintenance strategies will impact fleet availability and costs.
Using this technology, airlines can determine when each aircraft should go in for maintenance. Here, various elements can be examined including flying hours, engine cycles, and environmental conditions, enabling more accurate predictions of an aircraft's condition to ensure that maintenance is conducted at the most optimal time.
Rather than using fixed scheduling for aircraft repairs and checks, digital twins allow for a more dynamic approach, identifying exactly when the check is necessary based on performance data. This helps avoid unplanned downtime and reduces the likelihood of mid-flight component failures.
Maximizing fleet availability is one of the most critical challenges airlines face, especially during peak travel periods. AI-driven maintenance systems optimize schedules to ensure that aircraft are available when they are needed the most. For example, Aerogility’s solution allows operators to strategically combine tasks based on real-time data and demand predictions, ensuring maintenance activities do not overlap with critical flight schedules.
Overcoming Operational Challenges: Logistics Modeling
As those within the MRO sector will know, the aviation industry is currently facing complex logistical and operational challenges, including labor shortages and global supply chain disruptions. Such factors can severely impact an airline’s ability to efficiently maintain its fleet.
With Aerogility, each key element of an operation is represented as an agent, such as an aircraft part, a staff member, or a facility. These agents can be manipulated to display ‘what-if’ scenarios, revealing how they may interact with one another in various situations.
For example, digital twins can simulate situations where staff shortages or parts delays might occur, helping anticipate bottlenecks. By integrating data on maintenance shop capacity and workforce availability, AI can suggest alternative schedules or resource allocations, allowing users to predict and mitigate potential disruptions.
The virtual models can forecast future parts demand based on algorithms, ensuring that the right parts are available when needed. They also allow maintenance teams to simulate the impact of delayed components on maintenance schedules. This capability helps operators stay ahead of supply chain issues, reducing the risk of unexpected maintenance events and enabling better-informed decisions that keep fleets operational with minimal delays.
Measuring and Evaluating Sustainability Initiatives
With digital twin technology, companies can assess the short- and long-term environmental impacts of their operations, exploring various scenarios to improve their processes, without disrupting day-to-day activities. This approach enables users to make informed, data-based decisions that balance operational efficiency with sustainability, which is essential as companies work to lower carbon emissions in alignment with global targets.
A key example of this is Aerogility’s work with Rolls-Royce to model the environmental impact of its products and various elements of the enterprise, such as energy consumption at MRO facilities, inventory stores, and even general office blocks. By adopting this technology, Rolls-Royce can evaluate the most efficient and effective solutions for integration into its operations before making substantial investments.
Andy Eady, Sustainability Executive at Rolls-Royce, told us: ““We are able to zoom into carbon, for instance, and assess how we might maintain our existing decarbonization efforts even at time of surge operations. Alternatively, we can take a look at energy and assess whether our synthetic fuel manufacturing output is sufficient to deal with a surge event in 2040. Or we could identify the optimal global location for our inventory, balancing costs, logistical considerations, and the carbon impact.”
Aerogility has announced a further five-year enterprise-wide contract with Rolls-Royce. This will build on the existing relationship across multiple divisions, which has already resulted in two awards, the Defence President’s Award and the Sir Frank Whittle Medal.
As the industry continues to shift towards more sustainable operations, digital twin technology allows companies to examine and mitigate their environmental impacts while maintaining operational efficiency. A tool which will become even more invaluable in optimizing fleet operations, improving safety, and supporting the industry’s sustainability initiatives as it develops.
Building Trust in AI
The term AI should not be used as a buzzword. There are real efficiency gains to be had from using advanced technologies to give hindsight in advance. What is important is that the results and decisions made by AI tools are safe and trusted.
Model-based AI, like that used by Aerogility for more than 15 years and the basis of our digital twin technology, is designed to be explainable. It allows users to understand why specific decisions are made by showing the reasoning behind outputs. This differs from "black box" AI systems, where users can see the inputs and outputs but have little understanding of how the AI arrived at its conclusions.
In the context of maintenance planning, predictive models must accurately forecast maintenance needs to avoid costly unscheduled downtime. By integrating multiple operational parameters and constraints into simulations that are aligned with real-world data and scenario, results are reliable and accurate.
