Apples and oranges: Making sense of the economics of advanced air mobility

Flying taxis, passenger drones, electric regional aircraft, and other forms of advanced air mobility (AAM) are a hot topic in aviation. Funding is pouring into the space, and more than 250 companies are working on solutions. And as the industry advances toward the first commercial operations—expected around the middle of the decade—leaders are carefully reviewing and adjusting business plans to understand where they can make profits and see a return on their investments.

An important building block of business plans is unit metrics for cost and revenue. Unit metrics can offer easy benchmarks across business models and time, help AAM leaders evaluate their competitiveness with other modes (such as personal car, public transit, or ride-hailing), and model scaling and growth. But while unit metrics such as “price per mile” seem intuitive and easy to use, they also hold significant risk of misinterpretation. Used incorrectly, they can easily lead to false conclusions—such as making the market seem larger than it is or making an option seem better than alternatives when in reality it is worse—and this in turn can lead to investment in the wrong businesses, development of the wrong aircraft and mobility models, and, ultimately, value destruction.

With unit metrics, the devil is in the details. While it might seem obvious to some, a surprising number of people compare apples and oranges when talking about this industry. To fully understand the economics, we need to clearly define unit metrics and make sure we’re comparing apples with apples. The following discussion aims to provide some clarity about how to properly adjust unit metrics. It does not mean to endorse any absolute price points—that requires a longer and deeper discussion.

Defining unit cost and revenue

Unit cost for transportation is usually seen as cost per unit of distance (for example, dollars per passenger mile), but in the context of AAM, two things need to be clearly defined: the scope for which the cost is assessed, and how the distance is measured (exhibit).

Understanding the economics of advanced air mobility relies on clear definitions of the scope of the cost and the distance of the trip.

On the scope side, we can look at cost from several different perspectives: cost per vehicle, cost per seat, and cost per passenger. There are also three ways to think about distance: the direct (great circle) distance, which is the most direct path between two points; the road distance, which reflects the indirect nature of road travel; and the air distance, which is the aircraft’s flight path. Air distance tends to be shorter than road distance but longer than direct distance because the aircraft needs to maneuver for takeoff and landing and around other traffic and geography.

To demonstrate the importance of these distinctions, the exhibit shows a unit revenue, or price, comparison between a hypothetical AAM provider and a ride-hailing service. As shown, there are nine different ways to define unit price. If the typical ride-hailing service costs $3 per vehicle road mile and the AAM cost is $2.50 per passenger flight mile, at face value the AAM player appears to have a lower cost. But that conclusion assumes that there’s only one passenger and that both vehicles follow the same route. Because the car will likely take a less direct (and thus longer) route, it is not a fair comparison. A more insightful comparison is to adjust toward a common definition of distance.

In this example, we adjust to a common definition by assuming that the car’s route will be about 33 percent longer than the direct distance because the car has to use roads. Similarly, we assume the aircraft adds 10 percent in distance to allow for takeoff and landing paths and constraints along the route. With that adjustment, the cost becomes $4.00 per vehicle direct mile for the car and $2.75 per passenger direct mile for the aircraft—making the AAM costs look even better. But this is still not an apples-to-apples comparison, because it compares price per vehicle with price per passenger. When the car is carrying two passengers, for example, the price per passenger direct mile drops to $2.00—well below that of the AAM at $2.75 (exhibit).

Length of trip matters

We also need to consider the length of the trip. Every transportation mode has both fixed costs per departure (such as landing infrastructure or booking fees) and variable costs (such as energy). Fixed costs are spread across the entirety of the trip, so if all else is equal, shorter trips tend to have higher unit costs and revenues. When comparing business plans and financial reports, it is important to acknowledge this effect and adjust the unit metrics accordingly.

In traditional air transportation, the distance square-root adjustment formula provides a good approximation of the impact of stage length on unit metrics. To adjust, one multiplies the unit metric by the square root of the actual stage length of the metric divided by the stage length one would like to adjust to. The ratios of fixed to variable costs published by a number of AAM companies suggest that the distance square-root adjustment formula will also be a good approximation for this new industry, at least to a point. As the distances grow farther apart, the different design points of each aircraft will start to play an important role and break the validity of the adjustment formula.

For example, a hypothetical AAM has a cost of $1.75 per seat flight mile at a 25-mile reference stage length, while another hypothetical AAM has a cost of $1.50 at a 35-mile stage length. At first glance, the second AAM appears to have lower costs. But when its path is adjusted to the first AAM’s stage length—$1.50 * sqrt (35/25) = $1.77—the two costs turn out to be nearly equivalent and thus quite competitive.

These adjustments, both for the proper definition of unit metrics and for trip length, are necessary to get a proper view of the AAM business. Players that fail to use unit metrics correctly could easily make poor decisions leading to value destruction.

Andrea Cornell is a consultant in McKinsey’s Denver office, Axel Esqueis a partner in the Paris office and Robin Riedelis a partner in the San Francisco office.

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