Case study

Flying high: How predictive maintenance can take the FAA to new heights

Making better sense and use of data analytics to drive decision-making, from the federal government, to the on-site technician.

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Can predictive maintenance take the FAA to new heights?

Organizations are able to better monitor the health of systems remotely and generate insights to improve maintenance.


The Federal Aviation Administration (FAA) oversees national airspace where upward of millions of people travel each day and more than 67 million customers move through the system every month. It is the most complex airspace in the world, yet also the safest. Like other agencies, the organization is trying to figure out how to make better use of data, modern analytics methods and technology. It is tapping into years of data logs and telematics data to apply artificial intelligence (AI), such as machine learning, to unlock data insights to better optimize its maintenance program and reduce the cost of operations.

The FAA is on a transformative journey to adopt new technologies that keep the national airspace the safest in the world, use data to drive better decisions, discover ways to test and implement commercial leading practices, and find the means to create cost efficiencies.

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Approach data analytics more holistically at an enterprise level

Build data and visualization tools, along with innovative models, based on predictive maintenance technology.


Most federal agencies face challenges when it comes to data and technology adoption. These are compounded for the FAA because of the sheer size and scope of its operations: radar centers span the country to track the planes in the air. The FAA wanted to quickly and efficiently identify the root cause of radar and system failures. In addition, there was need to increase the level of understanding of the cost of repairs or the drivers behind the increasing cost of repairs. Other concerns included the intense pressure on budgets, rising inflation, and equipment modernization and service life extension needs.


Repeatable predictive methodology

These challenges, coupled with the COVID-19 pandemic, forced the FAA to reassess the way maintenance is performed and to use data to help weigh different investment options and better define financial priorities. It was clear that the FAA needed to apply data to answer critical business questions, such as which preventive maintenance interventions are most critical for the safety of the National Airspace System (NAS).


As organizational leaders wrestled with these different issues and the challenge of weighing diverse investment options, the FAA turned to the EY organization. FAA had lots of data; in fact, leadership frequently remarked that “they are data rich and insight poor.” But, how do could FAA access data that is spread across vast systems? Our role was to help them source, process and integrate millions of records from multiple types of different data sets.


Unstructured data was translated into structured data that could be used for analysis by employing natural language processing (NLP) to find commonalities in data and identify parts in maintenance logs. A pipeline was created to push data through and allow the FAA to utilize sensors to monitor issues and know where to send technicians to address them. These efforts created a reduction in maintenance hours and an enhanced planned maintenance schedule through remote monitoring activity on a broader scale.


By observing radar data in remote locations, for example, the FAA is able to save maintenance time, as technicians don’t need to drive to these locations. And, the technician can monitor system performance without relying on manual logs. This early detection approach can reduce operational risk and make it possible to proactively respond to potential failure and/or alarms.


Data-driven asset monitoring and operational efficiency are only two of the realized benefits of a predictive maintenance approach. The FAA is able to control the cost of maintenance for both labor and parts and can source parts when needed. It also can develop a plan to build internal analytics capabilities that will lead to more accurate maintenance reporting.

We helped the FAA identify process and data quality improvement opportunities to enable prediction of equipment failures — resulting in 75% to 90% accuracy three to seven days in advance.

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The journey from a reactive to a predictive maintenance capability

Aligning the maintenance maturity model with complex and evolving business realities.


A predictive maintenance capability is achieved through a solid data foundation and a systematic strategy that leverages modern data analytics and technology. The majority of firms and federal agencies move through four levels of maturity: reactive, preventive, condition based and predictive. The FAA is no different but aspires to a more capable future state.

Like most government organizations today, the FAA was focused on varying levels of reactive, preventive and condition-based maintenance. Then, the COVID-19 pandemic hit and everything shut down. Work processes had to change as employees were directed to stay home. It was a wake-up call that demonstrated the need to change quickly and perform less planned maintenance. By understanding its data and monitoring it remotely, the FAA found that the overall system could be better optimized. Today, the agency has a more rounded perspective on what is driving the cost of operations and maintenance and which embedded technologies and learning models will provide insights to drive better business decisions and guide future investments.

The FAA is now on a path where maintenance personnel are able to leverage data analytics and new processes that incorporate sensors and integrate supply chain, maintenance, labor and financial data in a continual evolving process that transitions the organization through the maintenance maturity stages. Maintenance processes will incorporate more optimal levels of maturity to include reactive, preventive, condition-based, predictive and, eventually, prescriptive phases.

We are helping the FAA move up the maintenance maturity ladder, creating new data pipelines, moving data into the cloud to enable AI-driven maintenance capabilities and modernizing maintenance processes.

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