The Cyclical Nature of Airline Industry Profits

1. The Cyclical Nature of Airline Industry Profits Kawika Pierson MIT System Dynamics Group 3nd Year PhD Fall 2008 Albany-MIT PhD Colloquium

2. Outline • Relevant Literature • Reference Mode for Airline Profits • Digging Deeper • The Model • Demand • Price • Capacity • Costs • Profit • Results

3. Relevant Literature • “Cycles in the sky: understanding and managing business cycles in the airline market” M Liehr, A Groessler, M Klein, PM Milling - System Dynamics Review, 2001 • Made for Lufthansa as a guide for strategy • Very limited scope (only one feedback loop)

4. Relevant Literature • “System dynamics for market forecasting and structural analysis” James Lyneis - System Dynamics Review, 2000 • Commercial jet aircraft industry • Focused on use of SD models as forecasts for Jet Orders • Proprietary, but potentially similar to our work • "Analysis of Profit Cycles in the Airline Industry" 2004 Helen Jiang, R. John Hansman • Very simple model, two stocks one feedback loop • Control theory perspective

5. Reference Mode • The data for US airline industry profits shows some cyclicality since before deregulation Taken from a presentation by Prof. R. John Hansman and Helen Jiang Nov. 2004

6. Digging Deeper • Profit = Revenue – Costs • Revenue = Price * sales in units • Costs = unit cost * production • Sales is Revenue Passenger Miles • Price is the Price of Tickets • Production is Available Seat Miles • This gives us Profit • How does financial reporting effect our modeling?

7. Capacity – Causal Loop Diagram

8. Capacity - Model Structure

9. Third Order Stocks –Cancellation and Mothballing

10. Forecasting Demand

11. Correction For Growth

12. Fitting to Data • Get historical data on important stocks • Airlines are great for this • Airlines.org, MIT Airline Data Project, BTS • Set up summary statistics • John Sterman’s Book plus MAE, RMSE, %E, Thiel, SSE/M^2 • Drive each model sector with historical variables • Use Vensim’s model fitting functions • Lets walk through this

13. Summary Statistics

14. Example of Fitting the Model • 1. Open Simulation Control • 2. Create a Payoff

15. Example of Fitting the Model • 3. Run “Policy” Negative

16. Example of Fitting the Model • 4. Set Parameters

17. Example of Fitting the Model • 5.

18. Capacity Fit – Historical Inputs “R^2” MAE/Mean RMSE/Mean Um Uc Us 0.995 0.0224 0.0309 0.0043 0.7475 0.2480

19. Demand – Causal Loop Diagram

20. Demand – Model Structure

21. Demand Fit – Historical Inputs “R^2” MAE/Mean RMSE/Mean Um Uc Us 0.99 0.0273 0.0356 0.0033 0.9595 0.0371

22. Price – Model Structure

23. Price Fit – Historical Inputs “R^2” MAE/Mean RMSE/Mean Um Uc Us 0.98 0.0583 0.0710 0.0004 0.2980 0.7015

24. Costs – Causal Loop Diagram

25. Costs - Model Structure

26. Cost Fit – Historical Inputs “R^2” MAE/Mean RMSE/Mean Um Uc Us 0.99 0.055 0.0719 0.0603 0.6697 0.2698

27. Wages – Model Structure

28. Profits – Model Structure

29. Wages Fit – Historical Inputs “R^2” MAE/Mean RMSE/Mean Um Uc Us 0.99 0.0278 0.0398 0.0294 0.9426 0.0278

30. Real Wages Fit – Historical Inputs “R^2” MAE/Mean RMSE/Mean Um Uc Us 0.68 0.0262 0.0339 0.0290 0.9370 0.0339

31. Full model Optimization • Move from partial model tests to full model parameterization • Fits are slightly worse, parameters more believable MAE/Mean RMSE/Mean MAE/Mean RMSE/Mean 0.0459 0.0564 0.0508 0.0595

32. Full model Optimization MAE/Mean RMSE/Mean MAE/Mean RMSE/Mean 0.0345 0.0434 0.0372 0.0465

33. Parameters More Believable • In Partial Model Test SLAT = 0.05 TAC = 1 • Theoretically should be very similar • In Full Model Parameterization SLAT = 0.18 TAC = 0.19 • Time to Adjust Prices Partial = 0.05 Full =0.64 • Sensitivity of Price to Cost Partial = 3 Full = 0

34. Profits Still Questionable

35. Conclusions • Growth Correction • Partial Model Tests with Historical Inputs • Cyclical Nature not alleviated by Cancellations or Mothballing • Standard SD Structures fit the industry reasonably well • More dynamics exist in the real system • Comments? Questions?