Fifteen-second speed read:
Airport aeronautical revenue could be increased through service improvements
Bots may be best-suited for this challenge
AI initiatives face three main limitations
- Becoming data-driven will have side effects
The term “airport” first appeared in a newspaper in 1919, referring to Bader Field in New Jersey, a pioneering parking spot for both sea and land-based craft. Since inception, this infrastructure point has been associated with dynamism and development both in popular culture and in business studies. Now it may continue its leadership by providing a perfect base for developing artificial intelligence systems close to resembling magic and earning more money from it.
Understanding this claim requires a bit of background. A well-run airport extracts as much as possible from its two revenue sources: aeronautical and non-flight operations fees. Charges in the latter have been argumented by among other reasons improvements in service quality (eliminated flow bottlenecks, easier shopping, needing finance for better connectivity to the port, and others). Pricing for the former has been growing for some time in search for market equilibrium and since there is a lack of substitutes for places to land or refuel an airplane unlike, e.g. leaving a passenger’s car at home instead of at a long-term parking closer to a runway or city centre versus duty-free shopping.
The two direct users of an airport’s services, airlines and passengers would of course like to get value for being charged more. Service improvements constitute one reason to raise fees. But while focus on serving the consumer qualitatively may justify markup and serve marketing goals well, the business-to-business segment does not usually make the news. Could it be because not much changes in it?
Probably, since elements of service demand from one business to the other have not changed much since Bader’s Field opened. Here’s a proposed list of Service Level Agreement items required from airports by airlines:
0. Infrastructure at quality (no potholes, clear NOTAMs, reasonably reliable weather predictions)
1. Efficiency – speed of ATC handling, quick provision of ground power, higher electrical supply on demand,
2. Punctuality – a delay minimisation approach,
3. Space to grow passenger volume (including baggage belts, check in desks, security checkpoints),
4. Security of its assets while on premises.
Demand details may vary, depending on the full suite of services on tender. Charging for their quality of provision has been studied already but without detailed consideration of IT multiplier effects. Here is a short attempt: Information Technology cannot increase the amount of space offered (some are trying to circumvent the need for extending it altogether by outsourcing services to people’s homes). It can certainly assist in analysing what to focus on and how to effectively manage infrastructure quality (0), punctuality (2) and asset security (4).
Another potential means of raising charges could be efficiency increase in aircraft handling (1) to improve customer satisfaction. This would include optimal parking and ground handling vehicle organisation with statistical proof that the airline operation gains revenue from the process.
To illustrate, let’s take the example of an aircraft arriving late with a large number of connecting passenger: the planes customers have to run and connect to could be automatically clustered at adjacent gates for shorter sprints. Meanwhile, ground handling units approaching from behind and right of the parked plane could be automatically queued, so the PRM service goes first, reaching the front right airplane door where the special passenger is before others; the catering one could be last as it has to reach the rear left door; and all their movements could be tracked via image recognition on existing apron-side cameras and in-vehicle GPS. QED with enough digital data sources combined with an analysis engine.
All this has already been possible but at the profit margin diminishing expense of hiring additional staff to supervise operations. Now, the Big Data challenge could be automated with the coming of age of self-learning bots, so more one-time technology purchases and not a continued highest cost element (direct personnel) increase.
A disclaimer must be considered when evaluating this idea since this feat does not appear to serve every operation equally well. Scale, Skills, and Data are essential:
The problem will need to be well-defined and narrowed in Scale. The one suggested here is an algorithm directing aircraft and ground handling vehicles optimally around an airport to increase passenger service quality,especially in irregular situations.
In combination, there should be about one hundred thousand aircraft movements per year for learning Scale (an arbitrary number for illustration; each provider must seek their own data threshold). If the airport serves ten airplanes per month, the investment into a bot to learn past aircraft and ground vehicle movements and direct new ones based on estimated quality increase will likely not repay itself.
Sufficient internal or external Skills talent – the AI field has not blossomed as quickly and diversely as one would expect because of a 50% lack of talent supply.
There should be a large enough input stream of Data for the bot to process – automated tracking of movements through camera images, recording of action start and end times, passengers reporting satisfaction levels continuously and so on. And once it gets going, the software agent will need supervision.
A concluding consideration and the key takeaway may not be the artificial intelligence application itself but its effect of driving digitisation – once process inputs become binary (i.e. the airport is data-drivennew use cases will spring up. And if a bot is faced with a well-defined problem to apply data to, answers about better customer service from both airlines and airports as well as means of increasing revenue from them might just emerge.