The IABPAD Conference Proceedings

Dallas, Texas, April 23-26, 2009

AIRBUS-BOEING DUOPOLY: STRATEGIC ANALYSIS BASED ON THE MODIFIED VOLTERRA-LOTKA COMPETITION MODEL Thanarerk Thanakijsombat Dinorah Frutos Southern New Hampshire University ABSTRACT The commercial large jet aircraft market is currently dominated by Airbus and Boeing. Such duopolistic condition provides the perfect scenario for applying a modified version of the natural growth S-curve law that involves only two competitors: the Volterra-Lotka competition model. Since the Volterra-Lotka competition model, accounts for three fundamental factors that shape growth: attractiveness of an offering, the size of its market niche, and the interaction with competition, in the present paper we study the type of competition and the “competitor” relationship between Airbus and Boeing over time. In addition, the analysis of the curves for each company allows us to determine the effectiveness of each strategy used in the past. The model of the competition for aircraft orders between Airbus and Boeing in the period of 1992-2004 proved to be robust. Two positive interaction parameters contradict the common belief of fierce competition between the competitors. The lag of the growth curves provides insight to the strategies they employed in responding to the internal and external changes.

INTRODUCTION The competition battle between Airbus and Boeing in the large jet airliner market has been growing since the 1980s. Airbus was established in 1970 as a consortium, whereas Boeing took over its former arch-rival, McDonnell Douglas in 1997. Since the 1980s aircraft manufacturers, such as Lockheed and Convair, have pulled out of the civil aviation market. Additionally, the collapse of the Eastern Bloc in the 1990s put the Soviet aircraft industry in a disadvantaged position. These circumstances left Boeing and Airbus in a duopoly in the global market for large commercial jets consisting of narrow-body aircraft, wide-body aircraft and jumbo jets. As of September 2008, Boeing’s commercial airplane segment had a backlog of $276 billion, encompassing more than 3,700 planes. Their estimated backlog is more than seven years at current production rates. As of June 2008, the commercial aircraft backlog of Airbus was also 3,700 aircraft, or about 6 years of production. (Standard & Poor’s Industry Survey, 2008). The competition for market dominance seems as fierce as ever. Analogous to the competition for resource between a predator and a prey which demonstrates S-shaped growth pattern, the competition between Airbus and Boeing can be modeled using the natural growth competition formulations developed in Ecology studies. One of the first models to incorporate interactions between predators and prey into the natural growth model was proposed in 1925 by the American biophysicist Alfred Lotka and the Italian mathematician Vito Volterra. Since the Volterra-Lotka competition model, accounts for 1) the attractiveness of an offering, 2) the size of its market niche, and 3) the interaction with competition, the aim of this paper is to apply the modified Volterra-Lotka formulation for two The International Academy of Business and Public Administration Disciplines

1110

The IABPAD Conference Proceedings

Dallas, Texas, April 23-26, 2009

competitors to study the type of competition and the “competitor” relationship between Airbus and Boeing over time. The model developed will also invoke deeper understanding of the strategies and their effectiveness employed by Airbus and Boeing. LITERATURE REVIEW The Natural Growth Competition of Two Competitors Many phenomena go through a life cycle of birth, growth, maturity, decline and death. The phases of natural growth proceed along S-curves, which occasionally cascade from one to the next creating a nested pattern that leads to a higher level S-curve. The implicit expectation in a process of natural growth is that the growth cycle does not stop in the middle but it continues until its end. Therefore, the predictive power associated with these curves comes from their symmetry. The S-shaped curve also describes the growth in competition of a species population. Competition arises in the population when the members start pushing one another in a crowded niche. However, in the presence of more than once species, the S-curve law does not generally apply, because one species can interfere with the growth rate of another in many different ways. Therefore, more terms must be added to the mathematical formulation of the S-curve to account for the interaction between species. However, the additional terms distort the S-shape pattern. (Modis, 1998) Nevertheless, in the exceptional case when only two competitors are involved, the Sshaped patterns emerge and it is possible to follow the growth of the two competitors. The basic mechanism is how one competitor influences the growth rate of the other. The additional parameter in the growth equation that takes into account the coupling is related to the overlap between competitors. Within this context, Farrell (1993) defines an attacker’s advantage and a defender’s counterattack in terms of the coupling parameters in the growth equations. In the context of market competition, the attacker’s advantage quantifies the extent to which the attacker inhibits the ability of the defender to keep market share. The defender’s counter-attack quantifies the extent to which the defender can prevent the attacker from stealing market share. The business strategy and tactics of attack and counter-attack have been qualitatively described by Peter Drucker (1985) and especially by Richard Foster (1986). The nature of the attacker’s advantage has also been discussed by Cooper and Kleinschmidt (1990) who determined that the most significant parameter in gaining market share is a “superior product that delivers unique benefits to the user”. This and price considerations dictate the magnitude of the attacker’s advantage. Under attack, the defender redoubles its own efforts to maintain or improve its position. A high value for the defender’s counterattack implies a face-on counter-attack within the context of “we do better what they do”. An effective counter-attack, however, with long-lasting survivalsustaining consequences implies eventual adoption of the new technology and some sort of death of the old company (Modis, 1998). As mentioned previously, in the commercial jet aircraft market, a two-competitor struggle between Airbus and Boeing is ongoing. Although nowadays Airbus has aircraft families that rival most Boeing products, this wasn’t always the case. Boeing was the market leader in the 1980s, however, Airbus proved to be a serious competitor when it won 50% of the market in

The International Academy of Business and Public Administration Disciplines

1111

The IABPAD Conference Proceedings

Dallas, Texas, April 23-26, 2009

1997. Given this turn of events and according to the Volterra-Lotka model used in this study, Boeing assumes the role of the “defender” and Airbus then assumes the role of the attacker.

The Models of Boeing and Airbus Competition Industrial applications of the S-shaped growth pattern were documented by A.J. Lotka, as early as the turn of the century. More recently, R. Foster (1986) has described an S-curve method for business applications. Charles Handy (1995) in “The Age of Paradox” exploits the sigmoid curve to coach managers on how to avoid premature death. In “Conquering Uncertainty” Modis (1998) presented an example of nested S-curves found in the aviation industry for the wide-body aircraft, which constituted a family with about a dozen members, each having its own life cycle. Based on the overall S-curve describing the growth process of the product, Modis concluded that the wide-body family was approaching the ceiling and predicted little, if any, growth in the annual passenger-mile totals of wide-body aircraft (Modis, 1998). The majority of studies about the aircraft industry focus on strategic and marketing aspects of the industry. Competitive analysis articles that specifically focus on Airbus and Boeing use various economical models and attempt to measure the effects on price and market share (Borenstein & Rose, 1994; Benkard, 2004; Marin, 1995). Another set of articles focuses on policy effects on competitiveness as well as rivalry under various assumptions on firm conduct (Armanios, 2006; Irving & Pavcnik, 2009). Finally, Esty and Ghemawat (2002) looked at competitive strategic interactions between Airbus and Boeing in the very large aircraft category by developing a game-theory model. They concluded that Boeing attempted to preempt Airbus in introducing a new product but failed to do so because of self-cannibalization. We are not aware of any other recent articles that use the S-curve approach to study the strategies of Boeing and Airbus, thus allowing for the development of this primitive research. METHODOLOGY The Model of Natural Growth Competition for Orders Our approach is to apply a modified Volterra-Lotka system of coupled differential equations to aircraft orders for both Airbus and Boeing. The nature of the competition is given by the system shown in Equation 1. Thus, determining the system parameters utilizing the aircraft “orders” provides an insight about the nature of the competition between the two companies. dB a B B bB B 2 c BA BA dt dA a A A b A A 2 c AB AB dt

------------ (1)

The terms in Equation 1 system are defined as follows: The International Academy of Business and Public Administration Disciplines

1112

The IABPAD Conference Proceedings

Dallas, Texas, April 23-26, 2009

A(t) is the cumulative aircraft orders for Airbus at time t. B(t) is the cumulative aircraft orders for Boeing at time t. aA and aB are the logistic parameters that relate to the attractiveness of Airbus and Boeing respectively, to customers and their abilities to compete for aircraft orders. bA and bB are the logistic parameters that relate to the size of the commercial jet aircraft market and order carrying capacities of Airbus and Boeing respectively. cAB measures the degree to which the presence of Boeing affects Airbus’s order growth. cBA measures the degree to which the presence of Airbus affects Boeing’s order growth. This system of equations is nonlinear and cannot be solved analytically. However, solutions can be found numerically. Using Pielou’s (1970) formulation, the system of differential equations is transformed into a system of different equations given in Equation 2. B(t 1) A(t 1)

B B(t )

1 B B(t ) M B A(t )

A A(t ) 1 A A(t ) N A B(t )

------------- (2)

In Pielou’s formulation, a number of new constants are introduced and they are defined below: and

for A and B

By compiling order data for both companies from 1989 to 2008 and utilizing these data to solve the system given in Equation (1), we obtain estimated values for the parameters a, b and c. These parameters are then used to calculate constants required in the system of equations given by Equation (2). Consequently, the model of the competition for orders between the two companies is obtained using the Pielou’s formulation. The Goodness of Fit of the Model The reliability of the model in terms of goodness of fit was examined using regression analysis based on the following equation:

Model C0 C1 Data

------------- (3)

Data of Airbus and Boeing’s Orders Annual order data for both companies were obtained from the respective companies’ websites. The aircraft order data ranges from 1989 to 2008. The accumulated orders for both The International Academy of Business and Public Administration Disciplines

1113

The IABPAD Conference Proceedings

Dallas, Texas, April 23-26, 2009

Airbus and Boeing are shown in Figure 1. The graph indicates an S-Shape growth pattern for the accumulated orders for both companies. It can also be observed, that both companies have experienced two cascaded S-Curves. Their first S-Curves are observed from 1992 to 2004, while the second S-Curves are observed from 2003 to the present. The second S-curves however are not well established to the level that the reliable estimated parameters and model can be produced. Therefore the focus of our study is the development of a prescriptive competition model for the first growth phase from 1992 to 2004. Figure 1 Cumulative Number of Aircraft Orders of Airbus and Boeing from 1989 to 2008 12000 Boeing Order Data Airbus Order Data

10000

8000

6000

4000

2000

0

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

RESULTS AND DISCUSSIONS The parameters a, b and c estimated from the system given in Equation (1) are shown in Table1. The logistic fits of aircraft order growth competition of Airbus and Boeing are presented in Figures 2 and 3. Table 1 Estimated Parameters in the Modified Volterra-Lotka Formulation Coefficients

Airbus

Coefficients

Boeing

aA bA

0.047790 0.000031

aB bB

0.050201 0.000031

cAB

0.000036 1.127803

cBA

0.000043 1.345423

N

M

The International Academy of Business and Public Administration Disciplines

1114

The IABPAD Conference Proceedings

Dallas, Texas, April 23-26, 2009

Figure 2 Logistic Fits of Airbus Order Data (1992-2004) 5,500 5,000

Airbus Order Logistic Fits Airbus Order Data

4,500 4,000 3,500 3,000 2,500 2,000 1,500 1,000

1994

1996

1998

2000

2002

2004

Figure 3 Logistic Fits of Boeing Order Data (1992-2004) 7000

6000

Boeing Order Logistic Fits Boeing Order Data

5000

4000

3000

2000

1000

1994

1996

1998

2000

The International Academy of Business and Public Administration Disciplines

2002

2004

1115

The IABPAD Conference Proceedings

Dallas, Texas, April 23-26, 2009

The goodness of fit of the model was measured using regression analysis. The results of the regression analysis for Airbus and Boeing are shown in Tables 2 and 3. The high R 2 values (0.987 for Airbus and 0.986 for Boeing) and the significant coefficients of both regression results confirm that the aircraft order growth competition model developed using the parameters provided in Table 1 stands scientific scrutiny. Both companies had a similar level of ability to handle new orders. However, according to the results, Boeing had a slightly higher ability to attract new orders than had Airbus from 1992 – 2004 (aB = 0.0502 > aA = 0.0478). A surprising finding is that the coupling parameters, cBA and cBA were both positive. Both coupling parameters were expected to be negative corresponding to the common belief that the competition between Airbus and Boeing for aircraft orders is fierce and both companies would suffer from each other’s existence. The value of the constant obtained for the attacker’s advantage was 1.3454, and the value of the constant for the defender’s counterattack was 1.1278. The figures imply that every time Airbus would obtain an order, Boeing would also obtain 1.3454 potential orders, and every time Boeing would obtain an order, Airbus would obtain 1.1278 potential orders. Table 2 The Result of Goodness of Fit between the Model and the Airbus’s Order Data Regression Statistics Airbus Multiple R R Square Adjusted R Square Standard Error Observations

0.993441106 0.986925231 0.986237086 355.0137705 21

ANOVA Regression Residual Total

df 1 19 20

SS 180756629.6 2394660.767 183151290.3

Intercept Airbus Data

Coefficients -227.1443308 1.070797838

Standard Error 130.0483149 0.028275192

MS F Significance F 1.81E+08 1434.181 2.32038E-19 126034.8

t Stat -1.74661 37.87058

The International Academy of Business and Public Administration Disciplines

P-value 0.096851123 2.32038E-19

1116

The IABPAD Conference Proceedings

Dallas, Texas, April 23-26, 2009

Table 3 The Result of Goodness of Fit between the Model and the Boeing’s Order Regression Statistics Boeing Multiple R R Square Adjusted R Square Standard Error Observations

0.992748599 0.985549781 0.984789243 401.1368237 21

ANOVA Regression Residual Total

Intercept Boeing Data

df 1 19 20

SS 208517638.1 3057304.276 211574942.3

Coefficients -409.6515189 1.041282963

Standard Error 169.2943062 0.028926101

MS F Significance F 2.09E+08 1295.859 6.00505E-19 160910.8

t Stat -2.41976 35.99804

P-value 0.025724293 6.00505E-19

In other words, the values found indicate that for the 1992-2004 period, a situation of mutualism or win-win competition was predominant. However, since the values of the coupling parameters were close to zero, there is a high possibility that a neutralism type of competition or no interaction between Airbus and Boeing was also prevalent. This argument is supported by the visualization of logistic fits of Airbus and Boeing’ order data presented in Figure 4. The figure shows no convergence of the two logistic fits. Figure 4 Comparison of Logistic Fits for Airbus and Boeing’s Order Data (1992-2004) 7000

6000

Boeing Order Logistic Fits Airbus Order Logistic Fits

5000

4000

3000

2000

1000

1994

1996

1998

2000

The International Academy of Business and Public Administration Disciplines

2002

2004

1117

The IABPAD Conference Proceedings

Dallas, Texas, April 23-26, 2009

The Rate of Growth of Airbus and Boeingâ€™s Aircraft Orders The first derivative of the S-curves for the cumulative number of orders for Airbus and Boeing are illustrated in Figures 5, 6 and 7. These bell-shaped curves represent the growth rates of aircraft orders of the two companies overtime. Using the timeline of the strategic actions performed by the two companies presented in Table 4, the relationship between the aircraft order growth rates and their strategies can be explained. Due to the downturn of commercial aircraft market resulted from the Persian Gulf War (1990-1991), Airbus and Boeing experienced stable or low aircraft order growth rate period up until 1993. 1994 however was the start of the exponential growth period for the two companies. Greatly contributed to the Boeingâ€™s order exponential growths was the launching of Boeing 777 which emerged as one of its manufacturer's best-selling models. Due to rising fuel costs, airlines have acquired the 777 as a comparatively fuel-efficient alternative to other wide-body jets, and have increasingly used the aircraft on long-haul, transoceanic routes. A direct market competitor to the Boeing 777 was the Airbus A330-321 which entered the service in the same year, starting the exponential growth period for Airbus.

Figure 5 Airbus Order Growth Rate (1992-2004) 5500 5000

Airbus Order Growth Rate Airbus Order Logistic Fits

4500 4000 3500 3000 2500 2000 1500 1000

1994

1996

1998

2000

The International Academy of Business and Public Administration Disciplines

2002

2004

1118

The IABPAD Conference Proceedings

Dallas, Texas, April 23-26, 2009

Figure 6 Boeing Order Growth Rate (1992-2004)

7000

6000

5000

4000 Boeing Order Growth Rate (Logistic) Boeing Order Logistic Fits

3000

2000

1000

1994

1996

1998

2000

2002

2004

Figure 7 Comparison of Aircraft Order Growth Rates for Airbus and Boeing (1992-2004) 900 800 700 600 500 400 300 Airbus Order Growth Rate Boeing Order Growth Rate

200 100 0

1994

1996

1998

2000

The International Academy of Business and Public Administration Disciplines

2002

2004

1119

The IABPAD Conference Proceedings

Dallas, Texas, April 23-26, 2009

According to Figure 7, Boeing’s aircraft order number reached its maximum growth rate or the inflection point in 1997 while Airbus’s aircraft order number reached the same level of 800 orders per year in 1999. The two-year lag of maximum aircraft order growth rate demonstrated that Boeing had faster but shorter increasing growth period than had Airbus. Figure 7 also shows that Boeing had higher order growth rates in the increasing growth rate period, while it however had lower order growth rates in the decreasing growth rate period than had Airbus. The result implied that Boeing did a better job in term of advancing higher and faster growth rates. Airbus however, performed better in term of sustaining increasing growth rate and smoothing decreasing growth rate overtime. Surprisingly, in their decreasing growth rate periods, the strategic actions related to the organizational merger and integration (i.e. the merger of Boeing and Mcdonnell Douglas in 1997 and the transition of Airbus to fully integrated EADS in 2000) showed no positive effect on the growth rates. Compared to these organizational actions, the launch of new aircrafts had more influence on the aircraft growth rates of the two companies (i.e. the launch of Boeing 737-900 in 2000 and the delivery of A340-600 in 2002.) This is most likely due to other factors that influence growth, such as channels, distribution, market share, frequency of innovations, productivity in the ranks, and organizational and human resource issues. Many factors can be expressed as combinations of the three fundamental ones. Alternatively, the model could be elaborated by adding more parameters, to take more phenomena into account. Also note that the growth rates after the September 11 tragedy for both companies were higher than those after the Persian Gulf War. Table 4 Timeline of Relevant Events for Boeing and Airbus from 1989-present BOEING

Year

AIRBUS

Cancellation of Boeing 707 production line.

1991

777 twinjet rolled out

1994

Ultra long-range family rolled out (A340-300) First operating profit A330-A321 entered service

Boeing merges with Rockwell (Boeing North America)

1996

The Boeing Company merges with McDonnell Douglas Corp Rollout of the Boeing Business Jet

1997

“Large aircraft division” setup 1000th delivery milestone Airbus wins 50% of the market

1999

Airbus Military setup

The first 737-900 rolled out. Net total orders for commercial jetliners above the 15,000 mark Boeing merges its business units into one called Integrated Defense Systems. Boeing ceased production of the 757 jetliner

2000

Fully Integrated Company (EADS)

2002

First A340-600 delivered

2003

Airbus overtook rival in deliveries for the first time

The International Academy of Business and Public Administration Disciplines

1120

The IABPAD Conference Proceedings

Boeing launched the 787 Dreamliner program New Boeing record for total orders in a single year (1,002). Boeing sets record for total orders in a single year (1,413)

Dallas, Texas, April 23-26, 2009

2004

2005

2007

“Centres of Excellence” initiative launched Unveiling of A380 New engineering center in China Second US engineering center established

CONCLUSIONS The Volterra-Lotka model applied to the cumulative commercial aircraft orders of Airbus and Boeing has provided an insight about the type of competition between these two companies in the past. The calculated coupling parameters were positive for both companies for the 19922004 period. This finding challenges the common belief that both companies were locked in a fierce competition. Rather, we see evidence that indicates a situation of mutualism or win-win competition between the companies in that period. Yet another possibility is that that there was a neutralism type of competition or no interaction between Airbus and Boeing in that period and each company focused on a strategy of new product development and new technology application. The bell-shaped pattern of growth rates overtime reveals that Boeing performed better and faster, enhancing growth rates during the increasing growth rate period. Airbus however performed better in term of its ability to sustain increasing growth and to ease decreasing growth rates. The links of the growth rate curves and the strategic actions of both companies reveal that the launch of new aircrafts and, economic and political environment exerted more influence on the aircraft order growth rates than the organization merger and integration did. In terms of competitive strategies, naturally, other factors influence growth, such as channels, distribution, market share, frequency of innovations, productivity in the ranks, and organizational and human resource issues. Many factors can be expressed as combinations of the three fundamental ones. Alternatively, the model could be elaborated by adding more parameters, to take more phenomena into account. As it stands the model provides the baseline, the trend on top of which other, higher-order effects will be superimposed. Further research involves building a model to explain the second S-curve (2002 – present) to examine the potential changes in the type and nature of competition between Airbus and Boeing. If new order data reveals the reliable S-curve pattern, the prediction of the decreasing growth rate period would be possible. REFERENCES Aerospace & Defense. Industry Surveys. (2008, July). Standard & Poor’s. Armanios, D. (2006). Parochialism in EU economic policy: Case study between the Boeing Company and the Airbus company. International Journal of Technology, Policy and Management, 6(1), 66-85. Retrieved from http://www.pitt.edu/~dea5/ URSPresentation.pdf Benkard, L. C. (2004). A dynamic analysis of the market for wide-bodied commercial aircraft. Review of Economic Studies, 71(3), 581-611. Retrieved from http://www.jstor.org/stable/ 3700737

The International Academy of Business and Public Administration Disciplines

1121

The IABPAD Conference Proceedings

Dallas, Texas, April 23-26, 2009

Borenstein, S., & Rose, N. L. (1994). Competition and price dispersion in the U.S. airline industry. The Journal of Political Economy, 102(4), 653-683. Retrieved from http://www.jstor.org/stable/2138760 Cooper, R. G., & Kleinschmidt, E. J. (1990). New products: The key factors in success. In American Marketing Association. Chicago. Drucker, P. F. (1985, May/June). The discipline of innovation. Harvard Business Review, 67. Esty, B. C., & Ghemawat, P. (2002). Airbus vs. Boeing in superjumbos: A case of failed preemption (Strategy Working Paper Series No. 02-061). Retrieved from Harvard Business School Web site: http://ssrn.com/abstract_id=302452 Farrel, C. (1993). Survival of the fittest technologies. New Scientist, 137, 35. Foster, R. (1986). Innovation: The attackerâ€™s advantage. London: MacMillan. Handy, C. (1995). The age of paradox. Boston: Harvard Business School Press. Irwin, D. A., & Pavcnik, N. (2004). Airbus versus Boeing revisited: International competition in the aircraft market. Journal of International Economics, 64, 223-245. Retrieved from http://www.dartmouth.edu/~dirwin/airbus3.pdf Marin, P. L. (1995). Competition in European aviation: pricing policy and market structure. The Journal of Industrial Economics, 43(2), 141-159. Retrieved from http://www.jstor.org/ stable/2950478 Modis, T. (1998). Conquering uncertainty: Understanding corporate cycles and positioning your company to survive the changing environment. New York: McGraw-Hill. Modis, T. (2005). The end of the internet rush. Technological Forecasting & Social Change, 72, 938-943. Retrieved from http://www.sciencedirect.com Pielou, E. C. (1970). Introduction to mathematical ecology. New York: John Wiley & Sons Inc.

The International Academy of Business and Public Administration Disciplines

1122