Statistics in Aviation

~ Kshitij Srivastava

“You can’t improve what you can’t measure”

-Peter Drucker

 In the fast-flying world of aviation, airlines have to be at the top of their game to remain competitive, and statistics are a useful tool in this pursuit. Airlines use statistics to provide them with vital information regarding viability of routes, future profitability, and to make flight plans. Moreover, statistics are also used in meteorology for weather forecasting, which is crucial for air traffic movement.

The Sky in Numbers

At any point of time, 8,000 to 20,000 planes are flying in the skies connecting a total of 41,820 airports in the world in a $400 billion industry. The meticulous and complicated planning required to keep this industry up and running is fueled by business insights derived by carefully analyzing all sorts of data. Thus, the fields of statistics, data science and big data have valuable contributions to aviation.

Airlines have access to huge amounts of data on a regular basis. Half a terabyte of data is created on every flight of a Boeing 787 aircraft. Data to the likes of customer service data, ticket information, weather forecasts and much more creates an ocean of data for airlines to derive business insights from. The real challenge lies in analyzing the data and actually acting on these results.

There are numerous use cases of big data and statistics in optimizing airlines’ operations. Let’s go through a few of them-

1.      Optimizing maintenance costs

Big data statistics help airlines optimize the maintenance of their aircraft. Fuel costs account for 17% of airline operating costs, thus making it crucial for airlines to be fuel efficient. With big data, airlines have been able to analyze huge volumes of data to examine fuel efficiency for individual flights. This has allowed them to further reduce fuel consumption on their flights by successfully forecasting the amount of fuel needed on each flight.

Another interesting application is in the regularization of repair schedules. The aircraft manufacturer Boeing observes and analyses 2 million conditions across 4,000 of its aircraft on a regular basis in order to predict faults in the systems. Accordingly, repairs are planned and parts are distributed. This helps the company save approximately $300,000 annually which was previously lost due to problems in predicting and scheduling repairs.

2.      Flight Safety

Big data helps aviation regulators and airlines improve safety in the skies. Huge amounts of flight data, air traffic control information, and weather data are generated during flights. This can be analyzed to identify safety risks and recommend possible solutions.

USA-based Southwest Airlines and NASA have partnered to improve airline safety by creating an automated system to identify possible faults and avert accidents. The system has been developed by creating algorithms on the basis of the aforementioned data generated during flights.

3.      Improving customer service and loyalty

Big data has facilitated airlines to improve their customer relations, thus improving retention and allowing up-selling and cross-selling. By analyzing customers’ buying habits and cross-referencing them with historic behavior and other variables, airlines have been able to generate personalized offers for their customers. This increases ticket sales and purchases of add-ons such as class upgrades, onboard snacks and refreshments, and additional baggage.

United Airlines has reported considerable increase in revenue after deploying these methods, witnessing a 15% increase in their year-on-year revenue.

4.      Efficient pricing

Analyzing big data collected through ticket sales can identify routes which have increasing or decreasing demand activity. Airlines can thus set prices according to changing demand patterns across different routes. Examining demand patterns can also lead to identification of price sensitive routes which might need aggressive pricing to remain competitive.

EasyJet has implemented this strategy to develop an artificial intelligence algorithm which determines individual seat pricing by taking into account demand and other factors. The algorithm can also predict demand patterns in the future, facilitating faster decision-making about future projects such as new routes and partnerships.

5.      Tracking passenger belongings

Most of us have had an unpleasant travel experience where our bags got lost on the way. Sometimes it’s not even our fault – the airline may have misplaced it and now we’re left in an unknown place with a portion of our stuff damaged or missing. Well, Delta airlines has implemented a technology which can help customers track their bags on their smartphones. The system works by analyzing the big data generated by passenger’s baggage.

 Alas, the coronavirus pandemic could never have been predicted by any of the prediction algorithms discussed. The aviation industry suffered huge losses because of closure of travel worldwide, and only those with strong and efficient planning could survive through the crisis. Many airlines, such as Flybe and Avianca, have either gone under or filed for bankruptcy.

The industry has just started to pick up due to resurgence of demand, and it is the need of the hour to make airline operations as smooth and efficient as possible. Analyzing big data statistics and acting on the insights derived from them is crucial to minimize costs and maximize revenue. Furthermore, prediction models need to be developed and improved to forecast further events that might hamper business operations.

References –

1) https://www.statista.com/statistics/278372/revenue-of-commercial-airlines-worldwide/

2) https://blog.datumize.com/5-relevant-examples-of-a-big-data-case-study-from-the-airline-industry

3) https://www.altexsoft.com/blog/datascience/7-ways-how-airlines-use-artificial-intelligence-and-data-science-to-improve-their-operations/