The summer of 2018 revealed nearly every shortfall in the aviation industry. Endless queues at the security checkpoints, chaos in flight plans, understaffed air traffic towers, or overloaded baggage claim. The only thing running smoothly in the entire flying business was the compensation for delayed or canceled flights.
Today, in 2021, after living more than a year with the novel coronavirus, these shortfalls from 2018 seem unreal - on nearly every level. No ques, no flight plans, almost no movement in the skies. And especially the compensation for cancelled flights has caused desperation not only among the passengers, but various governments too. The demand has collapsed. Air passenger traffic is down 60 percent, and airline revenue loss reached nearly USD400 billion for 2020 only.
Comparing this numbers with those of the year 2018, the difference couldn't be more shocking. In 2018, airlines generated $821 billion in revenue, up nearly nine percent from the previous year – placing it in terms of GDP somewhere between Saudi Arabia and Turkey (according to IMF). And they carried a little more than half of Earth’s population – 4.3 billion passengers. A new record.
An airline earns – after paying all expenses and taxes – a little more than $7 per departing passenger – the cost of two medium cappuccinos. Or a pair of socks.
The industry has been always running on small margins. The operating profits had only a slight peak in 2015 and 2016. Despite this pessimistic situation, there is light at the end of the tunnel. Here are some of the use cases for big data and how it could be used - and already is, in some places- to generate more revenue.
1. Predictive maintenance – selling data, not machines
The wealth of data generated in the aviation industry is causing a wave of excitement across many Executive Boards and their well-paid advisors. In response to that, they plot strategies way beyond current revenue streams. A digital airline is what many aim for. And data creates opportunities on many fronts to become one.
With Big Data, IoT, and predictive analytics (or any other machine learning algorithm), aviation companies will be able to cut costs and generate new revenue streams.
When Boeing 787 hit the market a decade ago, it produced an average of 0.5 terabytes of data per flight (according to Virgin Atlantic). A few years later, when the Airbus A350 arrived, it generated with its 6,000 sensors three to five times more (1.5 to 2.5 terabytes; on a transatlantic flight). Newer planes will only uphold the trend.
Some aircraft manufacturers use this data for predictive maintenance or fixing the airplane before it breaks down. Maintenance accounts for ca. 10% of airline operating costs, and many delays, too. Predicting when a part needs overhauling is of great financial benefit for both, the manufacturer and the carrier.
Rolls-Royce, the jet engine manufacturer, was one of the first to use the data streamed by its engines in a commercial way. It used real-time information generated by its 12,000 engines to monitor their health. By using predictive analytics models, Rolls-Royce was able to make decisions about which engines need to be pulled off for maintenance or replacement before they broke down.
Boeing and Airbus, the two biggest OEM market players, have also an interest in data. They’re moving away from being pure aircraft manufacturers to become service providers and leveraging the data their airplanes produce. The past aircraft programs required heavy investments and the costs were often not fully recovered. By selling data or insights, the returns seem to be more solid.
2. Real-time data analytics - increasing trip profitability
Fuel costs account for nearly 24% of airline expenses. And it’s a factor even the smartest analysts cannot predict. Every year, airlines fear how the price will affect their profits.
Unlike cars or ships, there is no real alternative to kerosene. Not yet. Therefore, any – even the slightest – improvements in fuel efficiency are highly welcome. (for example, the introduction of “sharklets”, reduced the fuel burn by 3.5% for an A320).
But data can save fuel, too.
The increased throughput of satcom systems, or the ascent of 5G mobile communications, make it possible. And this is how it works: the aircraft sends all the data to flight operations on the ground, where an analytics engine runs it through its algorithms and spits out a recalculated, optimized route and sends it back to the aircraft. And the data flow happens constantly, so the flight plan can adjust to the most current flight situation.
One of the airlines that does it well is Southwest Airlines, the world’s largest low-cost carrier. The airline collects data from its aircraft sensors, including wind speeds, temperatures, weight, or thrust. Then, it combines it with data on fuel, passengers (number), cargo (load), and other weather factors and feeds it into an analytics engine to search for the most profitable route.
One potential constraint here is the air traffic control’s ability to adjust to real-time and frequent changes in the flight plans. But maybe we will see some adjustments here, too.
Airbus is already offering its customers a data analytics platform, Skywise (powered by Palantir), to tackle fuel challenges (and others, too). This platform attracts not only established players in the market but also start-ups, willing to disrupt the whole industry’s approach in handling data.
3. Augmented reality - indoor airport navigation with data beacons
Airports have their challenges, too. And more often than not, these challenges directly affect the airlines’ operations.
One of the most frustrating situations for the passengers is finding the way through the complex environment of an airport. Especially when you’re on a tight schedule, finding baggage drop-off, check-in terminals, or the gate – and all this in a crowded airport terminal – is loaded with stress. Passengers are annoyed even before they take a seat on the airplane.
Because GPS is very inaccurate when it comes to indoor navigation, airports are looking into other technologies. For example, Bluetooth-based data beacons located within the building or Augmented Reality (AR).
Gatwick, one of the busiest airports in the UK, is experimenting with beacons and AR for indoor positioning. The beacons are placed all over the terminal building, helping the passengers to find their way by displaying blue dots on their map. Together with an AR app for wayfinding, the passengers can be guided by an on-screen arrow (see picture below).
By using these technologies, the airports of the future may be less hectic, and thus the stress of traveling could drop significantly. And the airlines will benefit, too, as the passengers will arrive more relaxed at their departure gates.
Also, by having real-time insights into the airport capacity, they could react immediately to any bottlenecks (let say, at security checks) and streamline the passenger flow.
There are more use cases for AR in the industry. Let’s look at another example.
The staff at Singapore’s Changi airport uses Augmented Reality (AR) to handle the cargo operations. The employees are equipped with smart glasses that scan QR codes and display all the details about the currently handled cargo (see animation below).
This technology allows ground crew to handle cargo operations in less time. According to SATS, the ground handling firm at the Changi Airport, the loading times were cut from 60 minutes to 45.
There could be many other use cases for Augmented Reality at the airports. We will see more and more of it in the near future. Especially, because they do not require adjustments to the airport’s physical infrastructure, which tends to be costly.
Who will control the data?
The aviation industry was never short on data. It just didn’t know how to make commercial use of it. But having data is not enough. Another challenge will be to hire talents understanding how to analyze the vast amount of structured, semi-structured, and unstructured data and make sense of it.
And who will control the data? The manufacturers, the airlines, the airports, or other service providers? Or will the industry finally tear down the silos it has fenced so well over the past decades?
The whole concept of digital transformation, however, has shown that silos are bad for business and data-oriented business models require cooperation, even between competitors.
Cloudy skies after all, huh?