Europe’s largest ultra-low-cost carrier, Ryanair, can predict the onboard food and drink available on a fight, what will sell and how much of it.
The carrier can also identify and predict maintenance and cut carbon emissions during a flight. How? By simply being an AWS (Amazon Web Services) customer.
How Ryanair uses AWS to optimise the flight experience
AWS allows Ryanair to use the cloud across its operations to lower costs, reduce food waste, and cut carbon emissions.
When food is presented on the trollies for passengers to choose from every flight, there’s more occurring behind the scenes than you may think. The trollies provide vital information about what food and drink are being sold and requested. This information is then fed into a machine learning tool (nicknamed the “panini predictor” by Ryanair) built using AWS which helps the airline predict the exact products to put on each plane.
This helps to cut down food waste and increase the satisfaction of customers by catering to their general desires.
Aoife Greene is Ryanair’s deputy director of ancillary and head of retail. She decides exactly what and how much food, drinks, and other items each aircraft offers with help from her team. She explains why the “panini predictor” is so necessary:
“Your holiday starts on the aircraft. People want their gin and tonic. They want their ham and cheese panini. They want to sit back and relax. They don’t want to hear, ‘no, that’s not available.’ It’s our job to make sure no one is disappointed.”
The machine learning tool is crucial as it would be impossible to calculate what kind of food and drink passengers want on each flight and the desired quantities otherwise.
The Irish carrier has more than 500 aircraft in its fleet and operates approximately 2,900 flights a day. Each plane carries five trollies’ worth of items and can only be restocked once every 24 hours.
Before AWS, Greene’s team relied on written logs and their own observations to predict and forecast demand on the various flights and destinations Ryanair serves. This was a time-consuming task. While the new panini predictor is in its relatively early stages, the custom algorithm analyses crucial information that helps Greene and her teams such as demand, consumption, flight duration, time of day, departure location, season, destination, passenger nationalities, and the number of children on board.
John Hurley, Ryanair’s Chief Technology Officer, said that the tool is particularly useful when deciding on product lines for a new route or adding a new loading base. It’s clear that the tool is accurate. He continued, adding that it improved customer satisfaction, cut waste in half, and boosted sales.
Hurley explained that Ryanair has been working with AWS technology for a while and are working on other initiatives elsewhere. The other projects include a new predictive maintenance tool that will forecast when different parts of a plane may be due for maintenance. The machine learning model will be able to generate reports for engineers that will recommend areas for them to investigate.
Another would be a system that would allocate the most fuel-efficient aircraft for each route, a customer service bot that will be built by Cation Consulting (an AWS Partner) that can converse in seven languages and handle more than half a million conversations a month. A digitalised document initiative, and a COVID-19 wallet for customers to upload necessary documents for travel quicker round up the incoming projects.
On the whole, AWS allows Ryanair to provide an enhanced positive experience for its many customers in all aspects of its operations. Working with AWS and cloud technology is a positive step for the major carrier in allowing them to offer positive and efficient experiences that rivals the competition.
John Hurley summarises it well:
“When AWS came on board, it sort of lit the torch paper to get us going. We’re testing these projects, analyzing all this data, getting the results back, and, for the most part, just saying, ‘Wow.’ It’s a major opportunity to be even more future-focused and efficient.”
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