McKinsey underscores gen AI’s role in bridging the labour gap

by Jessica Brownlow | May 13, 2024 | Innovation, MRO IT

Extensive labour shortages the aviation industry is currently facing have been identified as a critical challenge with the potential to slow progress, and the MRO sector is no exception. In fact, McKinsey & Company research indicates that by 2033, one-fifth of aviation maintenance technician jobs will go unfilled.

However, generative AI (gen AI) is seen as a potential way to bridge this growing gap, and the MRO industry is particularly is well positioned to benefit from the innovative technology. Gathering research on this topic, a recent McKinsey article looked at the transformative potential of gen AI for the MRO sector, here are some of its key insights.

The major ways gen AI can assist the MRO industry are as:

• Virtual AI maintenance and repair experts (“co-pilots”)
• AI-augmented reliability engineering tools
• Assistants who take care of busywork
• Permanent quality control supervisors
• Supply chain managers
• Accelerators of onboarding hires through skills training

Key challenges with its integration include:

• Striking the right balance between careful and agile
• Preserving strict safety and regulatory compliance
• Finding the right talent
• Having the right data

Highlighting gen AI’s potential, the article references a mining company that is scaling its support tools. The company is projected to see “at least a 35 per cent reduction in the time it will take technicians to troubleshoot equipment problems and at least a 25 per cent reduction in the time needed to do unplanned repairs.”

“Given the acute labour shortages in the MRO industry, these capabilities could turn out to be substantial productivity levers. There is also reason to believe that gen AI platforms could boost the quality, consistency, and accuracy of maintenance work, ultimately keeping more aircraft in the sky and minimizing aircraft out-of-service periods.”

Gen AI has the potential to transform the MRO sector, providing a range of solutions to the industry’s workforce challenge. Although its integration comes with a set of obstacles, if successful, the technology could help establish a more efficient, accurate, and safe industry.

For more about MRO see:

• KLM demonstrates how Apple Vision Pro can improve technical maintenance
• AI takes to the skies: The four practical applications of AI in aviation maintenance
• British Airways leverages real-time data to predict aircraft maintenance issues

KLM demonstrates how Apple Vision Pro can improve technical maintenance

by Jessica Brownlow | Apr 29, 2024 | Innovation, MRO IT

Historically, augmented reality (AR) has opened up possibilities within the industry, especially around training and inspection. As the technology becomes more refined, its use cases evolve symbiotically. Recently, KLM Royal Dutch Airlines shared how they are using Apple’s Vision Pro to elevate technical maintenance and training.

In 2023, Apple debuted the Apple Vision Pro, “a revolutionary spatial computer that seamlessly blends digital content with the physical world.” With AR, users can overlay digital information and interactive elements onto their surroundings while virtual reality (VR) offers users a fully simulated and immersive digital experience.

Its debut restarted excitement around the potential use cases of extended reality (XR) across industries. This month, Apple showcased how KLM Royal Dutch Airlines is using the Vision Pro. The airline’s Engine Shop app allows technicians to see a step by step breakdown of the task ahead of them with detailed instructions overlayed on an accurate 3D model of the exact engine.

Bob Tulleken, Vice President of Operations Decision Support, KLM said:

“We see Apple Vision Pro as a tremendous value-add that will improve our fleet availability and operations […] Training our employees with spatial computing will lead to fewer costly errors because the most current information they need to do their job is there in front of them as they perform the task. This means we not only get vastly more efficient in our work, but also provide a better work environment for our employees to succeed.”

Apple shared this short video demonstrating how the airline is leveraging spatial computing for technical maintenance. Video credit: Apple.

The Apple Vision Pro may have re-ignited excitement around XR but the aviation industry has been leveraging immersive technologies that merge the physical and virtual worlds in a multitude of ways for some years. As the technology evolves, it will open doors to more applications across the industry giving way to transformative advancements.

AI takes to the skies: The four practical applications of AI in aviation maintenance

by Jessica Brownlow | Apr 24, 2024 | Innovation, MRO IT

AI is answering some of the most difficult questions in aviation.

I pose here four questions and show how AI is transforming aviation maintenance in four key areas:

1. Maintenance Scheduling & Supply Chain Optimization
2. Error Detection & Reclassification
3. Automated Failure, Troubleshooting, & Repair Identification
4. Predictive Maintenance & Anomaly Detection

I think maintenance can be a little more efficient, don’t you? Maintenance Scheduling & Supply Chain Optimization

An application of AI that has many use cases is optimization, which includes maintenance scheduling optimization.

An optimization engine that can schedule maintenance at the best possible time at the best possible location has the potential to greatly reduce maintenance costs and improve maintenance yield fleet wide. At the same time, optimizing the order that are performed in, and how personnel are assigned to tasks can result in more efficient maintenance—reducing costs, improving turnaround time, getting the aircraft back in the air sooner—thereby generating more revenue.

Are you sure that’s correct? Error Detection & Reclassification

Another use of AI is to identify errors made in entering data, or to reclassify data after the fact to ensure accuracy of data and improve the overall quality of the dataset. A common problem across the airline industry is the misclassification of the failed ATA system when raising a fault. These misclassifications can impact the data quality in the system.

IFS Customer Southwest Airlines has rolled out a solution to use AI to identify misclassified faults and improve the overall quality of their data. This is an excellent application of LLMs that learn to identify patterns in the text entered by the technicians to classify faults more accurately. Using AI to identify potential errors and surface those potential errors to a person helps to make the whole process vastly more efficient while maintaining authorized human oversight.

Let’s try this again—Automated Failure, Troubleshooting, and Repair Identification

When a fault is raised, a technician is often required to spend a considerable amount of time researching the correct source of a fault, what troubleshooting steps to take and what repairs to apply.

The logical extension of fault classification is to take the same kind of LLM model which will suggest potential sources of the failure, and recommend troubleshooting activities or even make repair suggestions. Suggestions would be based on previous success rates.

By providing first time fix rate percentages to the technician, they may choose options that save time by resolving the issue quicker, meaning the aircraft may be able to get back in the air sooner, or even prevent recurrences in the future. Avoiding or reducing has real value, according to Airlines for America, in 2023, delays have a direct cost of $101.18 for every minute a flight is delayed.

Now what will happen? Predictive Maintenance & Anomaly Detection

The concept of predictive maintenance is nothing new. However, what is new is the application of newer types of AI, namely Anomaly Detection and Pattern Recognition. Predictive maintenance uses time-series data—which is gathered over long periods of time recoded at fixed known points along the way, i.e., discrete data points in time.

Live feeds of sensor data changed that with IoT, allowing current data to be considered—but that huge amount of data is difficult to interpret, requiring highly trained data scientists. Then Machine Learning (ML) came into the picture. Using ML means that data scientists are now focussed on creating the current learning model for the AI, rather than developing the algorithm themselves.

Unsupervised learning models are lowering the barrier to entry for the use of AI in predictive maintenance applications. “Unsupervised” learning models for AI means that you can plug the AI into a set of data and it can figure out its own algorithm. This reduces the time and cost of implementing a solution, and also has the power to remove bias from the process, particularly when dealing with large amounts of unlabeled data like multiple terabytes of data points generated from a modern aircraft .

Anomaly Detection means that you can plug the AI into the sensor data to figure out what “normal” looks like, it then warns you whenever a deviation from “normal” occurs. Coupled with Pattern Recognition, the AI can learn to detect patterns in the sensor data that indicate certain events are about to occur—providing an early warning system that can warn what is about to happen with extremely accurate results.

AI is here to stay

By using AI to streamline tasks, to provide decision support to the human, to pare down the noise of information—while keeping the human in the loop, still requiring them to be the ultimate decision maker—cutting edge companies have the ability to make huge strides in terms of efficiency and accuracy.

These improvements can represent real value to airlines & air operators and ultimately, their customers.

Article by Rob Mather, Vice President, Aerospace and Defense Industries at IFS