EasyJet, the second largest low-cost carrier in Europe by number of passengers , is having difficulties to deliver good operational performance. On-time performance has been worsening every year during the last 5 years, and in its fiscal year 2017 only 76% of its flights arrived within 15 minutes of scheduled time, down from 87% in 2013 . Increased disruption has had a very negative impact on customer satisfaction, which was 71% in FY2017, down from 80% in FY2013. Improving operational performance has become a key strategic priority for EasyJet in order to survive in an increasingly competitive market.
Why is machine learning important to improve delays?
Flight delays can be caused by factors that are not controllable by airlines, such as air traffic control, airport operations, or weather. However, the main cause of flight delays in Europe in 2017 was airline operations . In turn, one of the main causes for airline operations disruption is aircraft maintenance problems. Therefore, improving the aircraft maintenance process is a key lever to improve punctuality.
Airlines typically follow reactive maintenance programs, meaning that they fix aircraft faults after they occur. By analyzing aircraft sensor data and using machine learning algorithms, airlines could potentially move to predictive maintenance , fixing problems before they are likely to happen. Machine learning can discover meaningful fault patterns in aircraft data that people would otherwise be unable to find . Identifying potential faults before they become actual problems can improve aircraft reliability and limit delays.
How will EasyJet take advantage of machine learning to improve delays?
EasyJet has recently started to explore the use of predictive maintenance technology on its Airbus A320 aircraft family. EasyJet has launched strategic initiatives derived from its findings. For example, spare parts have been distributed around the network to support a faster response when machine learning algorithms predict that a fault could happen.
More broadly, EasyJet is also applying machine learning to improve other processes, such as optimizing in-flight food and drink choices .
With the ambition to become the most data driven airline in the world , EasyJet hired its first Chief Data Officer in August 2018. The new Chief Data Officer has broad experience in applied predictive technologies in other B2C organizations.
More importantly, EasyJet announced a five-year predictive maintenance partnership program with Airbus . EasyJet will use Airbus’ Skywise aviation data platform, which aims to become the platform of reference used by major aviation players. Skywise is bringing together data from multiple airlines, and is enriching this data with Airbus’ aerospace expertise . This platform is empowering airlines to develop machine learning algorithms to effectively implement predictive maintenance, with a data set that is much broader than the data set that airlines would own individually. Skywise is currently collecting data from more than 2,000 aircraft . Having this broad and diverse amount of data is critical to properly assess the quality of the machine learning model through meaningful cross-validation . This project has been launched by Airbus in collaboration with Palantir, a leading company in big data and machine learning. This tool will also help airlines to improve other processes, such as the decision-making process after unexpected events.
During EasyJet’s trial of the platform, Skywise successfully predicted 31 technical failures, which enabled EasyJet to remove components before a fault occurred. This meant that 31 flights would have been delayed, and this would have affected more than 4,400 passengers. EasyJet has now expanded the program to its entire A320 family fleet.
Moving forward, what else can EasyJet do?
First, EasyJet needs to develop personalized algorithms to adapt Skywise data to its own operations. Skywise incorporates data from dozens of airlines – and some of them have different operations than EasyJet (in terms of meteorology, length of routes, aircraft utilization, etc.). EasyJet needs to be able to adapt this data to its particular context. Therefore, EasyJet needs to continue building a strong data team and continue transitioning to become a data-driven organization.
On another note, EasyJet also needs to readapt its maintenance organization and processes. Way of working for predictive maintenance is very different than for reactive maintenance. EasyJet needs to adapt inventory management, culture, and procurement processes to benefit from Skywise data.
Lastly, there are some open questions that EasyJet’s leadership team need to address. One of them is to what extent should EasyJet share its data with Airbus. Being part of Skywise will allow EasyJet to access a vast pool of data that will enable enhanced predictive maintenance capabilities. However, this comes at a cost for EasyJet: it will need to share its valuable aircraft data with Airbus and with other competing airlines. Is participating in Skywise data benefiting more EasyJet or its competitors?
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