Machine learning the Airplane Mechanic of the Future?

Machine learning the Airplane Mechanic of the Future?

Machine learning the Airplane Mechanic of the Future? : Though it will take decades of certification and testing before air travel becomes fully automated, the aviation industry is looking towards the future. 

Prototypes of self-piloted passenger air vehicles by Boeing and Airbus have already made their first test flights, and airlines have been incorporating the use of data science and machine learning to automate and speed up operations.

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Revenue management

Nowadays, airline tickets vary significantly for the same flight on the same airlines, even for adjacent seats within the same cabin. Consumers pursue the lowest cost when purchasing a ticket, while airlines seek to increase ticket prices. 

These two contradictory requirements are reconciled in practice. Airlines tend to optimize their operating revenue by using techniques like price discrimination. Airlines have models that can predict the minimum cost that customers will pay for an airline ticket on a specific future date. 

Deep learning is a popular machine learning tool used to send out targeted ads on social media platforms such as Facebook and Instagram after gaining information from a person’s web searches.

These models rely on some standard features such as past ticket prices, ticket purchase date, and flight departure date. Futuristic versions of this model are looking to integrate social media data to improve the accuracy of ticket price and demand predictions.

In-flight food supply

Imagine you are boarding an early morning flight. Your first thought after going through airport security and finally taking your seat may be to order a cup of coffee and some snacks. However, people seldom order airplane meals, so one thing the supply management researchers must do is estimate the amount and types of food and beverages they must prepare onboard to avoid waste and minimize unnecessary cargo weight. 

According to data in 2018 from IATA, 6.7 million tons of cabin waste is generated by airlines every year – each passenger generates more than 3 pounds of cabin waste. In addition, plastic is a significant component of this cabin waste, and causes irreversible environmental problems around the world. Machine learning algorithms have been applied to help solve this problem. 

Fuel consumption optimization

To reduce the environmental effects of airplanes and cut flight costs, airlines apply AI systems to collect and analyze flight data on flight distance, altitudes, actual passenger count, aircraft weight, weather, etc. For example, neural network models can be used to predict an airplane’s fuel usage.

After preprocessing the data and training the model, systems can apply it and estimate the amount of fuel that is necessary for one flight. This helps minimize fuel waste and decrease unnecessary aircraft weight and fuel consumption. 

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Real-time passenger feedback analysis

Flights are boring. But, if one customer is having a uniquely-poor experience. Then with machine learning models, airlines will be more equipped to deal with customer needs. The machine learning modes can quickly react. Then the model can determine whether there is a chance to positively influence the passengers’ journey and turn their unpleasant experience into a satisfactory one.

Nowadays, models have been developed that include data processing, classification, visualization, and sentiment analysis. With a platform for passengers to leave feedback, the data can be linked to both internal and external operational metrics.

The systems can then apply natural language process techniques to process and better understand customer experience data. Utilizing the power of the AI system, a lot of time-consuming work can be accomplished on existing information so that people can free up their hands to do more complex tasks. This technique can be applied to improve the flight quality of passengers.

Conclusion

Machine learning makes it possible to improve customer experience, optimize their employees’ workflow, and ensure aviation safety by predicting fuel consumption and prescribed aircraft maintenance. It also allows airlines to use data intelligently to make informed and quick decisions about prices and market positioning.

With all these automated and self-serviced solutions, we are fully utilizing our existing data and can foresee a bright future for the airline industry.

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Reference

Chen, Y., Cao, J., Feng, S., & Tan, Y. (2015, October). An ensemble learning based approach for building airfare forecast service. In 2015 IEEE International Conference on Big Data (Big Data) (pp. 964-969). IEEE.

Machine learning the Airplane Mechanic of the Future?