#AMRG Presents: Summary of Estimating the Economic Viability of Advanced Air Mobility Use Cases: Towards the Slope of Enlightenment

What?

This research examined the operating costs of airlines and their economic viability using fare per km rates. It examines three use cases in Hamburg, analyzing direct operating costs in five categories: fee, crew, maintenance, fuel, and capital costs. The study also examined the impact of flight cycles and load factors on costs and airline business.

Who?

Jan Pertz, Malte Nikla?, & Majed Swaid, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Lufttransportsysteme, Hamburg, Germany

Volker Gollnick, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Lufttransportsysteme & Institut für Lufttransportsysteme, Technische Universit?t Hamburg (TUHH), Hamburg, Germany

Sven Kopera, Kolin Schunck, & Stephan Baur, Roland Berger, Hanseatic Trade Center, Hamburg, Germany

Where?

Drones, Volume 7, Issue 2, found HERE.

Summary

Following the premise of the Gartner Hype Cycle, the perception of advanced air mobility (AAM) is fading even before the service comes online. Competition between manufacturers is increasing, with manufacturers like Volocopter, Archer, and EHang demonstrating their capabilities in test flights. This study examined the economics of an AAM airline, considering cost and ticket fare deductions via three use cases in Hamburg and northern Germany, focusing on the influence of flight time on operating costs.

Operating profit in airlines is the difference between income and expenses, with income being the revenue from sold passenger seats and ticket fare, and expenses being the available seat kilometers multiplied by unit costs. This study uses the Direct Operating Cost (DOC) model, which is structured into five cost elements: fee charges, maintenance and overhaul, capital and depreciation, fuel, and crew. Flight cycles are predicted using a trajectory calculation based on vehicle capabilities and vehicle-specific flight cycles are determined based on average mission and block time of all routes. Flight times are investigated based on pre-defined use cases.

The study investigated the costs and fees associated with the AAM in urban areas, focusing on landing and terminal fees and navigation charges. The study separates an AAM airline from a vertiport operator, which acts as a further AAM stakeholder and imposes service fees. The vertiport service fee is split into a landing and terminal component, with a flat rate per landing. The study also considers maintenance and overhaul costs, as technical issues on aircraft can cause disruptions in flight schedules. Maintenance costs are divided into material and labor costs, with a wage of 66.5 euros/hour. The study also examines cabin designs and cleaning procedures for AAM vehicles, with a full-service car wash for a SUV or van costing around 200e. The study does not consider fees for noise or air navigation service providers.

This study examined the capital costs, fuel costs, and crew costs for aircraft operating autonomously (AAM) in Hamburg. Capital costs refer to the write-off of aircraft, while fuel costs depend on the flight trajectory and vehicle configuration. The study used fixed values for interest rate, depreciation period, and residual value factor to calculate the annuity. The capital cost per flight cycle is an expression of the unit cost as a function of the Operating Empty Weight (OEW), insurance rate, and annuity. The study assumes that an airline buys the aircraft by the OEM at any time and considers aircraft leasing as an established financing form in other transport modes. The study also considers the benefits of operating without a pilot, such as reducing human factors, increasing the available seat kilometer (ASK), and decreasing labor cost. The study applies the pre-defined use cases to the metropolitan area of Hamburg.

As different flight tracks are possible for each use case, assessed routes (gray squares) are presented relative to great circle connections. Squares and circles in green and blue represent the primary flight phase beginning sites for great circle trajectories. The analyzed intra-city aircraft route is 13.3% longer than the shortest possible connection. The airport shuttle has a detour factor of 7.1%, while regional air mobility has 16.7%. Nested circular charts show DOCs. For a cautious DOC estimate, apply a +30% detour factor to all charts.

Base scenario flight track and DOC for each use case. This flight shows no crew expense for use case 1 because there is no pilot. In the intra-city use scenario, maintenance and overhaul costs scale often due to a short flight period. Terminal and landing costs only scale with landing, not itinerary length. This use case has the lowest DOC, therefore landing and terminal fees exceed 25%.

In use case 2, the Archer Midnight can transport five people, including a pilot. For this, the airframe must stabilize the payload and battery pack. As long as the Archer Midnight's OEW cannot be reduced and the price per OEW remains high as mentioned, capital expenses, which scale linearly with OEW, dominate this use case.

Finally, in case 3, the Lilium Jet serves regional needs. Fuel expenses account for roughly 50% of the DOC because to distance and energy unit price. The total DOC is greatly affected by energy unit price changes. This use case depends on the energy market and its availability because to this itinerary's energy usage and energy price projection uncertainty. All three use cases have various DOCs due to vehicle performance and mission profile. All results have 100% load factors.

In all three circumstances, airlines must present DOC each cycle. The cost per seat and kilometer is the DOC per ASK divided by flying distance. DOC per ASK is similar for use cases 1 and 2, however it lowers on longer flights like regional use cases. If an airline cannot function with an LF of one, the remaining passengers must pay the rest to make it profitable. Economics dictate that the airline reallocates costs by handling passenger ticket fares.

Every cost element in this study has its own effect factors, but every sensitivity analysis treats it as a locked subsystem. This sensitivity analysis handles the DOC as a whole rather than cost parts. The three usage cases have different flight cycles per day per aircraft mainly due to the duration of travel for mission fulfillment. It was assumed that there would be 300 operation days per year (???1) and daily (???1). The case using Joby estimated 12 h of intra-city operations every day. This suggests a new flight every 36 min by operating 300 days per year with 20 FC each day. In the thorough FC study, weather, airspace closures, and demand shortages are ignored. A ground handling time study is also excluded from this FC analysis. A 20% variation around the base scenario gives an optimistic and conservative outcome for analyzing the FC's influence.

The optimistic scenario shows that 20% more FC can be applied to each use case than in the standard scenario. Conservatives assume 80% of base FC. The FC affects DOC capital and crew expenses. In both circumstances, airlines pay a predetermined annual price for these cost items. Cost factors are carried on more flights as capacity increases. Flight time and FCs incur fees, fuel, and maintenance and overhaul costs. When FCs may be done less per year, the DOC grows for every use case.

No staff is envisaged for use case 1, therefore only capital costs rise. Using a pilot in use cases 2 and 3 increases crew and capital costs. An airline aims to maximize aircraft utilization to reduce DOC per cycle. Besides the airline, the vertiport operator contributes to AAM. When a vertiport operator requests authority to integrate into airspace operations, various considerations must be considered.

Aviation operations, together with ecological consequences and construction site integration, affect approval. If not enough FCs are proven, a vertiport location is needed, and no authority will allow AAM operation. This restriction affects AAM stakeholders, not airlines.

A comparison was made between general aviation using a Citation CJ2+ and AAM for one case route. The results can be seen in the table below.

The total operating cost (TOC) is airline-specific and depends on factors that are not the objective of this research. To achieve economic viability, the DOC can be set as a pre-defined percentage of the TOC. Two scenarios are presented with an optimistic and conservative TOC set up. In both scenarios, the profit margin is fixed to a healthy 10%.

The TOC can be evaluated in a loop simulation by considering passenger behavior and a ticket fare system. When revenue leads to a defined profit margin by a given ticket fare and DOC, an airline can operate economically. Previous analyses of FC and LF are not influenced qualitatively by introducing TOC to the analysis. Existing effects do not change their character when scaling them with a factor, in this case adding IOC to achieve the TOC.

After setting up a healthy 10% revenue margin to the TOC, an airline can determine a required fare system to operate economically. The assumed profit margin of 10% is set to be more conservative than the annual net profit margin of 7.8% for conventional aviation in 2019.

This fee structure does not consider a distance-independent base fare; rather, the fare per kilometer must encompass the total expenditures. Assuming that the OEMs independently manage the AAM, Archer disclosed a cost-per-mile (CPM) of €1.77–€2.36/km. In Hamburg, a passenger incurs a fee of €1.70 to €2.60 per kilometer for a ground-based taxi transport mode.

The development of AAM as a passenger transport service in Europe is still under development, with no business plans available to evaluate the DOC model in terms of vehicle utilization or fleet size. The DOC inputs do not consider time-related changes in input parameters, and detailed vehicle specifications are unknown. An AAM airline can operate either with a fixed schedule or a demand service, depending on the demand and the availability of vehicles. Understanding passenger behavior is necessary to estimate profit margins.

The ticket fare system for AAM airlines is not discussed in detail, but a passenger preference model is needed to include this investigation. The DOC model has uncertainties due to the lack of information, and detailed maintenance and overhaul aspects can be investigated when a vehicle configuration is open for research. Capital cost and depreciation benefit from price labels for AAM vehicles, as unit prices have to be deduced from press releases only.

A third unknown influence on the DOCs is energy consumption, as manufacturers do not publish technical performance in terms of energy. Revenue aspects are investigated in passenger choice models. To better understand the general economics of AAM, it is planned to integrate different transport modes into the business evaluation, enabling the derivation of key performance indicators for specific AAM markets.

Comments

While this study claims that AAM would potentially be viable in the cases described, there are many caveats buried in the research. Here are a few:

• Assumes 100% load factors (!!!)
• Assumes flights every 36 min, 300 days per year, with 20 FC each day
• Weather, airspace closures, and demand shortages were ignored
• Ground handling time was excluded
• DOC model has uncertainties due to the lack of information
• Unit prices have to be deduced from press releases only

The fact is, to some extent, AAM will have to prove itself through actual operations. There is no shortage of models and simulations. But there are still too many moving parts and question marks to make anything more than (pseudo)educated guesses.

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