Understanding Business Cycles in the Airline Market
Martin Liehr, Andreas GréBler’, Martin Klein
Industrieseminar der Universitat Mannheim
D - 68131 Mannheim, Germany
Phone: (+49 621) 292-3140
Fax: (+49 621) 292-5259
E-mail: agroe @is.bwl.uni-mannheim.de
Abstract
Cyclical behavior in the airline industry is mostly endogenously generated. With the
help of a relatively simple system dynamics model, basic behavior modes can be
replicated. Furthermore, the model allows the identification of leverage points for
improving performance. Insights generated during the project work are now going to
influence order policies for new commercial aircraft jets.
The evolution of the airline market is characterized by long-term business cycles.
They are the major cause for the market’s poor profitability and for its low
shareholder returns. Since 1970 the airline market has seen two complete cycles.
These included severe crises in the early 80s and in the early 90s, affecting nearly all
carriers. In order to gain insights into the dynamics of the cyclical movements and to
derive strategies for long-term capacity and fleet planning, we developed a model of
the airline market.
The paper first describes the generic, cycle-generating structure of the problem—
a negative feedback loop with two delays. This relatively simple dynamic model
already provides a first explanation for the business cycles in the airline industry. In a
second step, this generic model serves as basis for the development of a general model
of the airline market. The general model helps
= to identify the cycle generating components of the industry and to understand
their interactions,
= to analyze different scenarios, and
= to identify key variables and leverages for cyclical management strategies.
The model reproduces historical behavior of the airline market and allows basic
estimations of future order trends for commercial aircraft jets.
The project reported herein is a system dynamics study realized for the corporate
planning department of Lufthansa German Airlines. It emphasizes the importance of
systems thinking and systems simulation in complex environments.
* Corresponding author
Business Cycles in the Airline Market: Planning Under Uncertainty
The evolution of the airline industry is heavily influenced by business cycles. Figure 1
shows the industry’s operating profits according to the IATA-member statistics.' The
figure shows, that the early 80s and the early 90s were periods of severe losses.
20,000
15,000
10,000
Mio. US$
5,000
-5,000 E a
1971 1975 1980 1985 1990 1997
Figure 1: Total profit over all airlines from 1970-1997 (source: IATA World Air Transport
Statistics)
Trying to explain these cycles, one has to look at the underlying critical factors of
success within the airline market. Doing this, it has to be seen, that the air traffic as a
product is basically a service, which is offered to the customer. From this point of
view, the air transport market suffers from the typical service industry’s problem
which is the missing ability to produce on stock. Thus, the cyclical behavior of
financial results corresponds with orders for new aircraft placed by airline companies.
Compare Figure 2 for a summary of ordered and shipped commercial aircraft jets.
a 1200 a
a pA 7s ordered
8 iN i
E it H
a 800 H }
3 ; shipped
B 400
5
eo 7
0
1971 1975 1980 1985 1990 1997
Figure 2: Orders and shipments of aircraft jets from 1970-1997 (source: Lufthansa Analytical
Report)
In addition, the air transport product is an indifferent product. This means, that
the service levels of different air transport companies are more or less the same. The
most important factors, which influence the customer’s decision for a specific airline
are the schedule and the price.”
For business travelers, the schedule is more important than the price of the flight.
Due to the higher yields in the business travel market, airlines mainly try to attract
business travelers. Knowing that business travelers mainly decide according to the
airlines schedule, the airline’s challenge is to develop and optimize a schedule, which
is characterized by a high number of destinations and frequent flights to each of these
destinations.
On the other hand, the cost structure of a single flight of an airline leads to a
contrary situation. Since the biggest part of the overall cost of a flight are induced by
the flight itself, the marginal costs of each additional passenger are low. From this
point of view the airline should try to fly with a low frequency to a specific
destination, trying to fill the plane with as many passengers as possible.
Taking these aspects together, airlines are facing the fact, that capacity planning
and schedule planning are mostly relevant for business success.
Business success itself became more and more important for the air transport
companies. Starting in the U.S., the international air traffic markets were deregulated
and according to international liberalization of the markets, most of the formerly state
owned companies are now traded at the public stock markets. Being listed at the
international stock markets, it becomes increasingly important for airlines to focus on
shareholder returns.
Given the requirements of the global capital markets, it becomes increasingly
important for the airlines to be able to show substantial growth. Against this
background of increasing shareholder orientation of the airline companies, the
business cycles, which determinate the profitability of the industry, are subject of
growing interest of the companies’ management, since these cycles are watched by
professional investors, too. As long as these cycles cannot be explained or forecasted,
the industry suffers from a discount in their stock prices, compared to other industries.
This situation leads to the necessity to be able to explain and forecast the business
cycles. Through explanation and forecasting it should be possible to prevent cyclical
behavior (at least as a single company) and, thus, to be able to keep profit up and to
outperform the industry.
Given this background, the following SD-project at Lufthansa German Airlines
was set up to use modern systems theory to explain the dynamic behavior of the
complex system of the airline market.
A System Dynamics Model to Analyze the Cyclical Behavior
The purpose of the model we developed for Lufthansa German Airlines is threefold.
First, we intended to gain insights into the dynamics of the cyclical movements and to
identify the core structure of the problem; second, to develop a tool for the analysis of
different scenarios, for example, exogenous demand-shocks; and third, to test
alternative policies in order to derive strategies for long-term capacity and fleet
planning.
The cycles of the airline market are often considered to be a response to
fluctuations in the evolution of the GDP and to lie beyond the sphere of the industry’s
influence. As a consequence there is a lack of cyclical management strategies to
smooth the oscillations and to reduce their negative impact on the carriers‘
profitability. However, our research has shown that there is strong evidence to believe
that the cycles of the market are endogenously driven and that there exist several
strategies airlines can adopt throughout the cycle. In order to improve understanding
and to create a basis for a general model, the underlying structure of airline market
cycles will be illustrated in a first step. This generic, cycle-generating structure as
described in Figure 3 is a very simple representation of the problem, but it already
provides a first explanation for the cyclical phenomenon.
T growth
demand
oS
orders
<Time>
MANUFACTURING TIME v
Manufacturing
(Delay)
seats offered
Capacity
\ SERVICE LIFE
retirements
&
Figure 3: Generic model generating business cycles in the airline market
Figure 3 shows a negative feedback loop with two delays—a structure that can
lead to non linear behavior (Forrester 1971, p. 2-37). The first delay characterizes the
aircraft lead-time, the second the delayed recognition of the industry’s surplus.
The description of the generic loop in action is similar to the cause and effects
produced by commodity production systems (Meadows 1970) or by delayed inventory
systems, as simulated with the Beer-Game (Sterman 1989, pp. 326-331): Airlines
strive for high seat load factors (Desired Surplus) to maximize their revenue. Due to
aircraft lead-times and delayed recognition of overcapacities, the system starts to
oscillate around the desired seat load factor. The mechanisms underlying the
expansion and contraction movements are similar to those shown by the classical
theory for economic cycles (Mager 1987, pp. 3-5).
Simulations of the basic model reveal that the existence of fluctuations is
independent of the development of revenue passenger. Figure 4 illustrates the surplus
and seat-capacity development at a constant number (generic) and at linear growth of
revenue passenger (generic/). Notice that unit values and time bounds in Figure°4
have been chosen for illustrative reasons, that is, to elucidate the cyclical behavior of
the generic structure. For more realistic time bounds and unit values see simulation
results of the general model below.
Graph for Capacity
1,000
na)
*
PAs rst SJ
a
500 tN
ast TS
ates
a a a a
Cy)
0 300 600 900 1200 1500 1800
Time
Capacity : generic Seats
Capacity : generic] <§ =< ———=——---—— Seats
Graph for Surplus
400
‘
v
oN!
180 Ate ye eet
he Sigal Vit Vi
4 yay hi ‘
a at ht Say bvy
hy
-40 iit i [NEN Ed
0 300 600 900 1200 1500 1800
Time
Surplus : generic
Surplus : generic] —$ <= —_—_—-—=—-—-——- Seats
Figure 4: Dynamic behavior of capacity and surplus in the generic model
An enlargement of the generic structure—a price-loop that includes a price
setting mechanism and a price-demand function—shows that yield-management
strategies cannot dampen the long-term waves in the market. Different yield-
management strategies only affect the amplitude and period of the cycles but not their
existence.
The general model of the airline market, that builds up on the generic structure,
provides a more realistic and detailed view of the cycle generating elements. It
consists of three modules: (1) the airline market as a whole—including all carriers and
manufacturers, (2) the structure of Lufthansa German Airlines—integrated as a micro
module in the airline market and (3) the competition module, where passenger decide
whether or not to fly with Lufthansa German Airlines depending on its competitive
situation.
In the following we will focus on the ,.macro-module* of the airline market as
illustrated in Figure 5.
<LH capacity share> <M sko> a MPrice
J wna 4g —E COMPETITION
M capacity share J ae
Mticket price
expected increase
market,
growth
<LH reverie passongar
~/ “Sg get rte / rate
Revenue
—
Mrevenue passendiy
Capacity
na Trp growth
M SERVICE LIFE | <Mtticket price>
<Time>
‘surplus
<increase> Msko
IM retirements
M Coste per Se
pit, —— M Savings
NF pete
~T Mcostdevelopment
M Profittrend <time>
Figure 5: General model of the airline market
The flow diagram displays a demand section (RP = revenue passenger), a price
section (M Ticket Price), a cost section (M Costs per Seat) and a capacity section (M
Capacity), the latter comprising all variables of fleet planning. The order variable (M
Orders) is a key element in the general model. The decision to buy new aircraft
depends on seven variables including, among others, the passenger growth forecast
(Expected Market Growth) and legs (number of daily take-offs of one aircraft). Since
carriers tend to wait and see if their profitability is sustained before committing to new
orders (Skinner and Stock 1998, p. 54) the model considers a variable that describes
the mid-term development of operating profits (M Profittrend).
The general model is the result of various consultations of experts, who helped to
identify the relations between the key variables and to define the system’s boundaries.
Hence, it was possible to construct a model that reproduces historical behavior: The
characteristics of cyclical variables and the two crises of the airline market in the early
80s and 90s can be duplicated by model-simulations. Figure 6 illustrates the evolution
of the seat load factor (M SLF) from 1970 with troughs in 1983 and 1993.
0.9
08
07
0.6
1970 1978 1986 1994 2002 2010
Time (Month)
M SLF : Basic-Run in %
Figure 6: General model: simulation of the seat load factor-evolution since 1970
Leverage Points for Corporate Planning in the Airline Market
The model presented above satisfactorily reproduces historical behavior of the airline
market. Compare, for example, actual orders from 1970 until today and data generated
by the simulation model (Figure 7). Although no complete identity can be stated the
dynamic, cyclical behavior is obviously the same.
400
350 7;
300
simulated f 1
én 250
a8 |
gs 200 ++ actual
g
B= 150 N Ly
of
100 N
"4 V
50 india f
0 + -
1970 1975 1980 1985 1990 1995 2000
Figure 7: Comparison of historical and simulated data for orders of new aircraft jets (airline
market)
Note in particular the level of similarity to results of a simulation presented by
Lyneis (1998, p. 11). Our goals, however, are different. We are not interested in an
numerically precise prediction of the future airline market. We aim at identifying
endogenous factors that are responsible for cyclical behavior in the airline market.
Furthermore, our intention is to improve the system to achieve more stable results.
With these two goals, we follow Morecroft’s (1988, p. 312) approach and built a
model to “’prime’ policymakers for debate.” Nevertheless, the model presented here
allows basic estimations of future order trends for commercial aircraft jets. (See
Lyneis 1999, for a discussion about the use of models with different degree of detail.)
Furthermore, different scenarios, for instance, exogenous changes in demand, can
be analyzed. For an example, see Figure 8, which depicts results for the basis
simulation run in comparison to a simulation run where effects of the Gulf War are
not included. The cycles in the simulated markets only differ in amplitude, not in their
principal appearance. We interpret this results as another indication that the cycles in
the airline industry are mainly caused endogenously. Exogenous factors only
determine the amplitude of the cycles, but they are not responsible for the general
cyclical behavior of the system.
08
0.6
1970
MSLF:
MSLF:
60,000
30,000
oO
M Orders : regular
1978 1986 1994 2002 2010
year
regular Seat load factor
not St Se == == === © Seat load factor
1970 1978 1986 1994 2002 2010
year
Seats ordered per month
M Orders : nogolf == — — = = — — — — — — — Seats ordered per month
Figure 8: Comparison of seat load factor and orders with and without Gulf War (airline
market)
The model presented in this paper helped to identify key variables and leverages
for cyclical management strategies. Decision makers learnt that the cyclical behavior
of results in their industry are to a good amount caused by their decision rules and not
by exogenous factors. A fact that is no surprise for system dynamicists. As
possibilities to stabilize the system, the points shown in Figure 9 were identified.
o ~ Alternative order policy:
ad “ —_- strategic alliances
“x - - counter-cyclical behavior
orders
MANUFACTURING
TIME y
“NN Manufacturing
(Daley) urplus
. eats offered
a Capacity
Timea A
- retirement policy Ny
‘N
retirements Ny
Network planning:
Y - geographical transfer
of capacity
- counter-cyclical behavior
Figure 9: Leverage points to stabilize results in airline market
As an example for these leverages a leasing policy was further explored.
Figure 10 depicts the dynamic consequences of a more flexible fleet, which could be
achieved by leasing of a substantial part of the airplanes. Leasing of airplanes
stabilizes Lufthans:
results. It has to be considered, however, that this approach does
only work, if the leasing company is able to work with stable demand and order
policies. That means, it will not have a positive effect if the leasing companies just
reproduce behavior formerly shown by airlines.
0.8
06
1970 1978 1986 1994 2002 2010
year
LHSLF : Base run Seat load factor
LHSLF: Leasing = —-—-—-—-—— = = = & Seat load factor
8,000
4,000
0
1970 1978 1986 1994 2002 2010
year
LH Orders : Base run Seats ordered
LH Orders: Leasing = -§ - = —-——— ----- == Seats ordered
Figure 10: Consequences of leasing of airplanes on seat load factor and orders (Lufthansa)
Another possibility to stabilize the whole industry would be more cooperative
policies of aircraft ordering. If airlines would consider the total amount of orders,
overcapacity could be avoided. The growing importance of strategic alliances of
airlines could offer chances aiming in this direction. They allow to adjust order
policies within the alliance. In the competitive airline market between the different
alliances, however, such cooperative behavior is still not likely to happen.
Like for many other industries before, it was shown with the help of a simulation
model that cyclical behavior in the airline industry is endogenously generated. Already
a small system dynamics model can replicate historical data sufficiently. Leverage
points to stabilize system’s behavior can be easily identified using this model. Future
project work will be on the implementation of improved order and network policies.
Another area of interest is to extend the model in order to achieve more precise
financial statements.
References
Forrester, J. W. (1971). Principles of Systems, Cambridge, Massachusetts.
Lyneis, J. M. (1998). System Dynamics In Business Forecasting: A Case Study of the
Commercial Jet Aircraft Industry. In System Dynamics Society (ed.), CD-ROM
Proceedings of the 1998 System Dynamics Conference, Quebec City.
Lyneis, J. M. (1999). System Dynamics for Business Strategy: a Phased Approach.
System Dynamics Review 15(1), 37-70.
Mager, N. (1987). The Kontradieff Waves, New York.
Meadows, D. (1970). Dynamics of Commodity Production Cycles, Cambridge,
Massachusetts.
Morecroft, J. D. W. (1988). System Dynamics and Microworlds for Policymakers.
European Journal of Operational Research 35, 301-320.
Skinner, S. and Stock, E. (1989). Masters of the cycle. Airline Business 04/1998, 54—
59.
Sterman, J. D. (1989). Modeling Managerial Behavior: Misperceptions of Feedback in
a Dynamic Decision Modeling Environment. Management Science 35(3), 321-
339.
Notes
1. IATA = International Air Transport Association
2. Lufthansa market research results / conjoint analysis
CC BY-NC-SA 4.0