The International Civil Aviation Organization (ICAO) stated that passenger numbers in 2021 were down by 49% from 2019, resulting in an expected loss of gross airline passenger operating revenues of $324 billion. But the market is expected to rebound in 2022, with an anticipated rise in passengers by up to 47%.
The previous two years demonstrated that neither airlines nor MRO (maintenance, repair, and overhaul) companies may depend on their previous triumphs. As a result, they face an unstated mandate to proactively future-proof their companies from top to bottom.
There are several ways AI, data, and technology can help the aviation industry recover.
Predictive Maintenance
The rapid adoption of predictive maintenance in the aviation industry remains a key driver of digital transformation for 2022.
Prior to the pandemic, only the top 10% of airlines implemented predictive maintenance. Yet in a recent opinion polls conducted by IFS, predictive maintenance revealed itself as the most frequently cited benefit of digital transformation.
Source: https://www.prometheusgroup.com/posts/reactive-vs-preventive-vs-predictive-maintenance
Predictive maintenance forecasting algorithms–powered by AI (artificial intelligence)—are used by original equipment manufacturers (OEMs), airlines, or MROs to gather real-time and accurate data about every onboard system and sensor-connected component in their fleet.
For engines, which are relatively self-contained units, this practice is well established and produces a 30% performance efficiency gain over traditional techniques. It allows for a maintenance team to efficiently service and maintain aircraft components while maximizing their life spans.
As more flights arrive on time and without incident, this will benefit an airline’s passengers and its bottom line. There will likely be a significant increase in the number of airlines utilizing predictive maintenance soon.
Data and Analytics
The theme is to use data in new ways and enable organizations to better understand how well they are serving their customers. Data and analytics help determine the allocations of funds and budgets.
Given Covid-19, one of the most vital topics remains aeroplane air quality even as the virus abates in parts of the world. Managers consider new data as necessary. Data freshness will continue to be crucial for competing after the virus.
Platforms such as Google Cloud’s AI and ML technologies interpret data in timeframes that enable real-time decision-making. Data alone is useful, but sometimes insufficient. With sufficient context, data becomes powerful tools for strategic planning.
For example, AirAsia uses Google Cloud to optimize pricing, improve revenue, and enhance the customer experience.
AirAsia started using an AI Platform in March 2018 to sort and predict demand for ancillary services such as baggage, seats, and meals, laying the groundwork for using machine learning to optimize pricing across a range of services. Other features include a digital health pass powered by AI.
Source: https://www.businesstraveller.com/business-travel/2020/11/25/airasia-develops-digital-health-pass/
Operational Performance
Passenger processing now bears a consistently larger impact on departure times due to the pandemic.
Travel restrictions, screening procedures, and available spaces frequently change, sometimes resulting in chaos. Passenger flow patterns can be modelled using machine learning to predict gate arrivals, passenger crowding, and the varying times it takes to leave different airports. Automated systems can reduce late gate arrivals and improve turnaround times.
Recently, the University of Cincinnati and Cincinnati/Northern Kentucky International Airport (CVG) announced that they are teaming up to predict (and reduce) crowding and improve the passenger experience.
Generative Design
Aerospace engineers and designers create components using generative design principles and AI. These principles give AI a set of parameters and enables it to generate a few possible designs. Then the results are manually improved upon for a final product.
These more efficient components are then quickly created using artificial intelligence and machine learning techniques that learn from the guidelines laid out by the designers.
When it comes to creating new designs, generative design uses machine learning logic. Parametric modelling design in CAD software lags AI. Using generative design software yields a variety of possible solution combinations made possible by simply entering the relevant design parameters. The result is often thousands of variations of the same design, each achieving iteratively better outcomes.
Fuel Efficiency
Even a tiny reduction in aircraft fuel consumptions significantly impacts a company’s bottom line and emissions through the power of volume. Aerospace companies place great importance upon fuel quality.
240 litres of fuel are used every second, and 14,400 pounds of fuel are used per hour on a typical commercial flight. We can reduce fuel consumption from 5% to 7% with the help of AI.
Practices powered by AI reduce fuel consumption. For example, machine learning aids pilots in optimizing their climb profiles before each flight. Safety Line data shows each flight can save 5% to 6% of its climb fuel without compromising passenger safety or comfort.
When applied to an airline fleet, this could reduce CO2 emissions by several thousand tonnes per year and operational costs by several million dollars. Optimizing the ascent process alone saves a staggering amount of fuel.
Customer Experience
Commercial aviation places a high value on customer satisfaction and service quality. Artificial intelligence in the airline industry improves customer service and engagement. Automated platforms powered by AI converse with customers in real-time and with human-like manners. Online chatbots save customers both time and effort by automating customer service processes such as:
- Automating flight searches and bookings
- Updating flights
- Assisting with check-ins
- Refunding flights and processing claims
Examples abound in the airline industry. Gol is the leading Brazilian airline and one of South America’s most important airlines. Gal, the company’s chatbot, assists passengers in booking and modifying flights, as well as with checking flight statuses.
Source: https://www.inbenta.com/en/blog/8-airline-chatbot-use-cases-youll-want-to-implement/
Around 35% of Gol customers finalize their check-in using WhatsApp.
Keeping customers happy remains one of the airline industry’s biggest challenges. One bad experience causes a passenger to switch to a rival airline.
Customers who remain loyal to one airline may do so because of cost or convenience, or for miles they’ve accrued. All airlines prefer to see an increase in revenue per passenger and increased customer loyalty.
When it comes to increasing airline revenue per customer, airlines have traditionally relied on direct marketing and promotions. However, thanks to advances in AI, customer service and sales functions are mergeable.
In the event of a customer service call, the AI can recommend future trips or flights based upon the passenger’s past travel patterns (once the issue is resolved). After the service interaction, the AI may wait for days or weeks for the best time to contact the passenger, based on its analysis.
Summary
Artificial intelligence is beginning to take off in the aviation industry. Compared to other technologies, this one is still in its infancy. As its adoption spreads however, we’ll undoubtedly see more uses of it. Right now, we’re only seeing the beginning, and we’re eager to watch how it all plays out.
Disclaimer: The author of this text, Jean Chalopin, is a global business leader with a background encompassing banking, biotech, and entertainment. Mr. Chalopin is Chairman of Deltec International Group, www.deltecbank.com.
The co-author of this text, Robin Trehan, has a bachelor’s degree in economics, a master’s in international business and finance, and an MBA in electronic business. Mr. Trehan is a Senior VP at Deltec International Group, www.deltecbank.com
The views, thoughts, and opinions expressed in this text are solely the views of the authors, and do not necessarily reflect those of Deltec International Group, its subsidiaries, and/or its employees.