Yes, to automate or speed up operations, the aviation industry employs AI, or rather, data science and machine learning. So, in this article, we'll look at how the aviation industry uses data science through real-world examples.

• Route Planning and Revenue Management

Data science is used in aviation for revenue management and route planning. Understanding traveler demand for specific city pairs and pricing flights are two of the most difficult problems that airlines must solve in order to survive. Carriers must consider thousands of factors when analyzing data to accomplish this. Traditional statistical approaches can still be used by analysts.

Data science enables more sophisticated approaches to demand analysis. Airlines, according to IATA, can use traveler behavioral data, abandoned searches on online travel agencies, metasearch sites, and social media chatter to help define leisure demand.

Data from professional networking sites' recruitment and procurement activities could point to new business travel destinations. In a 2017 showcase for airlines, Skyscanner used machine learning-based clustering to group about 50,000 origins and destinations by similarities. They took into account about 30 variables, including the month of travel, the time a reservation is made, how long people stay at the destination, and many others.

• Sales And Food Supply in Flight

Because some people do not order airplane meals, airline supply management specialists must estimate how many snacks and drinks to bring onboard to serve food eaters without being wasteful. EasyJet CEO John Lundgren tasked the data science team with analyzing food demand.

The team discovered that demand for items on a 6:00 a.m. flight to Edinburgh differed greatly from that on a Friday night flight to Ibiza. As a result, the jet was discarding three fresh food items after each flight, amounting to nearly 800,000 per year. According to John Lundgren, such an error cost the carrier millions of pounds. Eventually, data scientists developed a new algorithm for demand prediction, and the insights assisted the airline in saving a significant amount of money.

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• Using Facial Recognition to Board and Check Bags

This biometric technology is used by airlines as a boarding option. The technology scans travelers' faces and compares them to photos in border control agencies' databases. Photos of passports, visas, or other travel documents can be used.

The process is as follows: travelers first identify themselves and their passports at self-service, then check their luggage with a scanner. This technology enables the creation of a more seamless, faster, and safer travel experience. Delta Airlines has opened a biometric terminal at Atlanta International Airport.

Delta introduced facial recognition in another Atlanta airport terminal, as well as in Minneapolis, Detroit, and other airports that have 49 more gates equipped with facial recognition software, in the summer of 2019. Travelers appear to like the new boarding option. 

• Optimizing Fuel Consumption

Over the last five years, carbon emissions have increased by 32%. As a result, aircraft manufacturers and airlines are seeking ways to improve fuel efficiency. In 2018, jet fuel accounted for 23.5% of total airline expenses. An airline must accurately predict how much fuel it will require for each scheduled flight to become more fuel-efficient. The best scenario is to have a single analytical tool.

Southwest Airlines worked on a solution like this for its fuel consumption project. The team created eight predictive models, including time series of algorithms and neural networks, so that the system could generate 9600 fuel consumption forecasts per month. It generates 12-month forecasts and takes into account influencing factors such as fuel prices, number of trips, and time.

• Preparing the Plane for the Next Flight

Sometimes we have to wait at the gate to board a plane because an aircraft isn't ready for boarding and departure, such as when a catering truck arrives late, or the cleaning crew is working on another plane. Passenger airlines in the United States lost an average of $74.29 per minute due to delays in 2022. According to the US Department of Transportation, plane servicing delays accounted for 5.8 percent of all delayed flights.

Airlines can expect to start personalizing offers for individual travelers based on their preferences and willingness to pay. Data science and machine learning are used to evaluate passenger demand across different routes, optimize aircraft ground handling and fueling, or redefine passengers' airport experiences with biometric boarding with new distribution technologies. Also, the Data Science course in Delhi is a great place to get hands-on experience with data science tools and get insight into their practical applications.