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AirTran Airways Productivity and Cost Analysis 2006-2011

AirTran Airways Productivity and Cost Analysis 2006-2011. Michael J. McCormick Air Transportation System Engineering April 22, 2013. AirTran Airways - Background. AirTran Airways is generally considered a low-cost carrier: Non-traditional distribution channels No frills but reliable service

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AirTran Airways Productivity and Cost Analysis 2006-2011

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  1. AirTran Airways Productivityand Cost Analysis2006-2011 Michael J. McCormick Air Transportation System Engineering April 22, 2013

  2. AirTran Airways - Background • AirTran Airways is generally considered a low-cost carrier: • Non-traditional distribution channels • No frills but reliable service • But has many traits of network legacy carriers including: • Two-class cabin service • Hub network - Atlanta, Baltimore, Orlando & Milwaukee • Frequent Flyer Program – A+ Rewards • Wi-Fi on All Aircraft • Unionized Labor • Heterogeneous Fleet Mix – B717 & B737 • International Service (Mexico & Caribbean) • Assigned Seating

  3. AirTran Airways – History1 Founded in 1993 and incorporated into ValuJet Airlines in 1997 as AirTran Airways. The original AirTran Airways was part of Mesaba Airlines and based in Orlando. AirTran Airways become a wholly-owned subsidiary of Southwest Airlines in 2011. AirTran and Southwest joined on a Single Operating Certificate in 2012. AirTran Airways is a Fortune 1000 company with many customer service awards. 1. http://www.airtranairways.com/about-us/history.aspx

  4. Definitions • RPM:Revenue Passenger Miles ∑ i = 1 to All Flights (Number of Passengers (Flight i) * Distance Flown (Flight i) • ASM:Available Seat Miles ∑ i = 1 to All Flights (Number of Seats (Flight i) * Distance Flown (Flight i) • RASM:Revenue per ASM – Operating Revenue/ASM • CASM:Cost per ASM - Operating Expenses/ASM • Yield:Average Airfare Paid by Passengers per Mile Flown – Total Revenue/RPM • PRASM:Passenger Revenue per ASM – Total Revenue/ASM • Fuel Consumed: Quantity of fuel used in a specified time period or specified flight exclusive of reserves • Fuel Costs per ASM: Fuel Costs/ASM • Non-Fuel Costs per ASM: (Operating Expenses – Fuel Costs)/ASM • All metrics are utilized to compare airline performance over time, against industry standards or against individual competitors.

  5. Data All data is self-reported by the AirTran Airways to the Department of Transportation via Form 41. All data was downloaded from Bureau of Transportation Statistics public website, www.transtats.bts.gov. Data was imported into Microsoft Excel 2010 and Stata 12 for analysis. AirTran Airways stopped Form 41 reporting after 1st Quarter 2012. To mitigate seasonal fluctuations, the analysis utilized 3rd Quarter data from 2006 through 2011.

  6. Increased LF helps narrow the gap between ASM & RPM. • ASM increased in 2007 & 2011 due to growth in flight segments. • Essentially flat ASM/RPM/LF (2008-2010) is due to downward • economic pressure.

  7. In spite of steady operating revenue growth, operating costs • outpaced revenue resulting in a pre-tax loss in 2008. • From 2009 through 2011, operating expenses outpaced revenue • narrowing profit.

  8. From Chart 1, RPM & ASM were flat 2008-2010. • CASM increased to peak above RASM in 2008. • Yield per RPM is generally downward sloping, most likely due to • lower ticket prices to stimulate demand.

  9. In 2008, dramatic increase in fuel operating costs contributes to • increased CASM observed in Chart 3. • A significant increase in fuel consumed in 2007 is due to system growth • seen in Chart 1 and contributes to increases in both fuel and non-fuel • operating costs.

  10. Increasing fuel price in 2008, 2010 & 2011, explain the increases in • fuel operating expenses in Chart 4. • The decrease in non-fuel expense per ASM in 2011 is due to dramatic • decrease in General and Administrative Costs.

  11. Statistical Analysis • Due to the low number of observations (n=6), the analysis could develop a robust model due to limited degrees of freedom. • Due to low n and likely serial autocorrelation, the analysis does not use time series regressions. • There is significant multicollinearity between many of explanatory variables, therefore the model utilizes one cost variable and one revenue variable. • Income Before Taxes = B1(Operating Revenue) + B2(Fuel Price) + Constant + e

  12. Statistical Results • Ceteris Paribus, a $ 1 increase in fuel prices decreases quarterly income before taxes by $ 89,921,000. • Ceteris Paribus, a $ 1,000 increase in operating revenue increases income before taxes by $ 369.10.

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