DATA SCIENCE IN AVIATION INDUSTRY

DATA SCIENCE IN AVIATION INDUSTRY

Transcript

1. DATA SCIENCE IN AVIATION INDUSTRY Avinandan Dutta
2. AVIATION INDUSTRY ◦ The aviation industry includes practically all aspects of air travel as well as the activities that support it. This covers the entire airline sector, as well as aircraft manufacture, research firms, military aviation, and much more. You will discover everything you need to know about aviation and the industry that surrounds it in this essay. ◦ The term 'aviation' is most generally used to refer to mechanical air transportation conducted by aeroplanes. Aeroplanes and helicopters are the two most common types of aircraft, however most modern definitions of the term "aviation" include the usage of unmanned aircraft, such as drones. ◦ The terms 'aviation industry' and 'airline industry' are sometimes used interchangeably, yet they refer to two distinct industries. An airline is a company that provides air transportation services for passengers or freight, and the airline industry is the umbrella word for all of these enterprises. ◦ The airline business, on the other hand, is only a small component of the overall aviation sector. Aside from airlines, the aviation sector comprises aircraft manufacturers, researchers, air safety professionals, military aviation enterprises, and, increasingly, drone design, production, and/or use companies.
3. Why is the Aviation Industry Important? ◦ The importance of the modern aviation industry is difficult to overstate, but one of the main reasons for this importance is the globalised nature of the industry, helping to connect different continents, countries and cultures. As a result, global aviation has been key in facilitating efficient travel to distant places, enriching many lives in the process. ◦ The aviation industry has also been a key contributor to global economic prosperity, not only as a result of the tourism industry boosting local economies, but also because it has allowed for improvements to global trade. ◦ Meanwhile, the aviation industry also directly provides millions of jobs for people around the world, with examples including everything from pilots and cabin crew, through to air traffic controllers and aerospace engineers. On top of this, the aviation industry has helped to create many jobs in the wider travel and tourism industry too.
4. 3 Large Aircraft Manufacturing Companies Within the Aviation Industry 7 Largest Airline Companies in the World
5. Link Data-Science with Aviation Industry ◦ The data on the airline business is freely available on the internet. Each airport, for example, publishes quarterly information on its passenger and freight numbers. Once collected and consolidated, this data can be a great place to start modelling and understanding airport operations. If you supplement it with data from previous airline flights, which is also available online, you can create a model that shows how each airport affects airline revenue and provides information on flight delays for each airport and airline, allowing you to create an index of airline operational efficiency that could be useful to airline industry actors. ◦ The airline industry has enough of operational data to work with, but if you want to work in data science for the airline sector, you should read some books about airline operations and airport operations, as I did, and grasp the airline industry's economics for both passengers and freight. Knowing the ins and outs of an industry is something that all data scientists should be concerned about because data science is required in every company, and the more you understand about an industry, the better you will be able to see how your data science skills can help improve a part of the value chain, or the entire chain.
6. ◦ The airline industry has a lot of opportunities thanks to data science technology. The data generated by aeroplanes flying high in the sky includes engine systems, fuel efficiency, weather, passenger information, and so on. There will be more data collected when more advanced aircraft, equipped with sensors and other data collection technologies, are utilised in the sector. If appropriately utilised, this data can provide the industry with new prospects. ◦ Process optimization, people management, and disruptive innovation are all possibilities. Although still in its early stages, data science technologies are seeing increased usage in the aviation industry. Let's have a look at some of the data science applications in the airline business.
7. The prominence of Data Sciences in Airlines ◦ Ticket Pricing-Airline pricing is determined by supply and demand. Weekends, holidays, routes, and other factors can have an impact on cost. It also relies on airline schedules. Flights in the evening and early morning are priced differently from flights in the afternoon and late at night. However, in order to attract clients, price must always remain competitive. Airlines can use analytics-driven pricing to automate the pricing process and increase revenue by maximising capacity utilisation. ◦ Personalized Selling- Airlines also sell a variety of extras such as lounge access, additional baggage, seat upgrades, and food, among other things. A customer's past experience can be analysed by a data-driven recommendation engine, which can then promote additional services at the time of ticket purchase. It can also make customised recommendations based on a customer's financial situation. ◦ Customer Feedback -Customer feedback nowadays comes from a variety of places: tweets, photos, phone calls, videos, and so on. Data Science can handle both structured and unstructured data in real time, allowing customer service representatives to listen to customers and respond swiftly to their needs.
8. ◦ Fleet Maintenance- Every cancellation damages income as well as the brand's image. Delays may sometimes caused by unplanned maintenance. Predictive maintenance can assist airlines in keeping their fleet up and running as they aim to enhance revenues through efficient fleet management. Collecting and analysing aeroplane data in real time can aid maintenance personnel in preventing technical issues and planning maintenance schedules. ◦ Demanded flight routes-While RM is concerned with determining the most effective method of selling a product or service, carriers employ AI to answer one of the most important questions: where to fly. "In order to create air routes, experts must examine data and make conclusions based on their findings." They can use data sources like search history and macroeconomic parameters (e.g. GDP) to explore demand for a destination across different client segments," adds Konstantin.Specialists must utilise industry-specific norms to define willingness to pay in RM. ◦ Willingness to pay- Airlines collect and analyse data on customers to better understand their preferences and behaviour, allowing them to offer transportation options that they prefer and, more importantly, are willing to pay for. As a result, revenue managers begin by determining willingness to pay (WTP). This metric is linked to dynamic pricing, which is the practise of pricing a product based on the willingness to pay of a certain client. WTP computation necessitates careful data selection. Revenue management can group similar markets together or distinguish between high and low seasons, holidays, and weekends.
9. ◦ Crew Management-There are a lot of factors to consider when it comes to crew management. Working hours, vacation days, member licences, language skills, and so on. Data science can not only aid in the automation of crew scheduling, but it can also provide valuable insights into personnel management, crew fitness, and regulatory compliance issues. ◦ Fuel Efficiency-In 2018, the global aviation industry's fuel bill was predicted to be $180 billion (about 23.5 percent of operational expenses). Airlines can use data science technologies like AI and machine learning to extract meaningful insights from fuel-burn, weather, navigation, and operations data in order to maximise fuel utilisation and lower operational costs. ◦ In-flight sales and food supply- Many of us anticipate flying while eating a sandwich and sipping coffee while admiring the passing clouds and bright blue sky.Meanwhile, supply management experts determine how many food and beverages they can bring onboard without going overboard. AI is also available to assist. ◦ In-flight experience improvement- Delta launched the AI-driven system in January 2020, which will help operational choices in crucial scenarios such as inclement weather. It created a full-scale simulation environment using digital twin technology that processes tonnes of data points, anticipates likely outcomes, and provides decision makers with the most effective options for dealing with disruptions and ensuring safety.
10. Big data in aviation: 5 case studies ◦ 1. Encourage loyalty: United Airlines Customized offerings will always appeal to the customer, resulting in increased loyalty. Airlines are in the enviable position of being able to learn a great deal about their customers through analysing data. Even a single reservation contains information that can tell an airline a lot about its consumers. For example, United Airlines analyses over 150 characteristics in each passenger profile using their "collect, detect, act" procedure. In order to provide a tailored offer, these studies track everything from prior purchases to client preferences. United's revenue has increased by over 15% year over year as a result of the collect, detect, act campaign.
11. ◦ 2. Get to know the customer: British Airways Customers may get customised search results from British Airways thanks to an intelligent 'Know Me' function. BA discovered that their customer base is mostly made up of busy, time- pressed professionals who want quick, concise results in this outstanding big data case study. As a result, 'Know Me' employs in-depth data analysis to present them with relevant and customised offers. BA received a lot of favourable comments from customers who appreciated how well the company understood their travel requirements. 3. Deploy artificial intelligence: EasyJet Many airlines go above and beyond standard data collection. Companies can now evaluate massive data gathered from purchasing behaviour to demand patterns thanks to modern technology. For example, if an airline notices that demand for a certain route is increasing, it can modify its costs accordingly. The airline can also use this data to establish which consumer groups are price sensitive, as well as a segment's price range for a specific route.
12. ◦ 4. In-flight intelligence: Southwest Airlines During the flight, massive volumes of data are created, including pilot reports, warning reports, control positions, and exchanges with air traffic control. When this data is closely monitored and evaluated, processes may be streamlined and safety can be improved. Southwest Airlines, for example, has partnered with NASA to continually improve aircraft safety. Southwest and NASA have developed an automated system that can analyse massive amounts of data to detect anomalies and avert mishaps using sophisticated algorithms.5. Making lost bags a thing of the past: Delta Delta, an American airline, has developed a smartphone app that allows customers to track their bags. The notion is simple: the app use the same technologies that Delta's ground crew employs. Delta consumers have downloaded the app over 11 million times worldwide so far.
13. The future of AI in the airline industry ◦ AI now empowers businesses to improve customer experience through automation and self- service solutions, employee workflow through workflow optimization, and air safety through predictive and prescriptive aircraft maintenance. It also allows airlines to use data to make informed pricing and market positioning decisions. ◦ Furthermore, AI can boost an airliner's fleet availability by up to 35 percent while lowering personnel expenses by up to 10%. Using data from in-service aircraft, it can also predict possible faults with airliners. AI will be able to predict flight delays and plane malfunctions using algorithms.