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The Cyclical Nature of Airline Industry Profits Kawika Pierson MIT System Dynamics Group 3nd Year PhD Fall 2008 Albany-MIT PhD Colloquium Outline Relevant Literature Reference Mode for Airline Profits Digging Deeper The Model Demand Price Capacity Costs Profit Results 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) 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 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 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? Capacity – Causal Loop Diagram Demand + Forecasted Demand Capacity + B Capacity Shortfall Capacity Control + Investment + + Desired Capacity Capacity - Model Structure Planned Replacement Orders <Airplane Retirements> Desired Capacity + Desired Capacity Acquisition Rate + + Desired Aircraft Supply Line Indicated Capacity Adjustment Time to Adjust Capacity Supply Line Adjustment Time + Adjustment for the Supply Line - Order Approval Delay Time <Time Required to Manufacture an Airplane> Adjustment For Capacity - - Weight on Supply Line Adjustment + Indicated Orders for Capacity Mothballed Capacity <Cancelled Orders> Mothballing <Return to Service> Off Mothballing Cancellation <Into Storage> Orders of Airplanes <Return to Service> Airline Capacity Supply Line Initial Capacity on Order Airplane Manufacturing Completion Airline Capacity Initial Airline Capacity Airplane Retirements <Retired> Third Order Stocks –Cancellation and Mothballing <Indicated Orders for Capacity> Time Required to Manufacture an Airplane Time to Cancel + Cancelled Orders <Orders of Airplanes> Ordering Capacity on Order 1 1 to 2 Capacity on Order 2 Completion Average Service Life <Airplane Manufacturing Completion> Capacity 1 New Capacity 2 to 3 Capacity on Order 3 Capacity 2 Cap 1 to 2 Capacity 3 Retired Cap 2 to 3 <Operating Margin> <Time to Mothball> <Indicated Orders for Capacity> <Operating Margin> Return to Service Into Storage Margin Threshold to Initiate Mothballing Time to Mothball Capacity on Mothball <Indicated Orders for Capacity> Forecasting Demand Time Horizion for Reference Demand <Time Horizion for Reference Demand> Time to Percieve Trend in Demand Change in Expected Growth Rate Reference Demand Change in Reference Demand Time to Percieve Changes in Demand Indicated Growth Rate Expected Growth Rate for Demand Initial Expected Growth Rate in Demand Perceived Demand Change in Demand Perception Gap in Demand Perception <Historical Airline Demand> <Actual Demand For Seat Miles> <Switch for Historical Variables> Correction For Growth <Perceived Demand> <Airline Capacity> Desired Load Factor Planned Replacement Orders Capacity Adjustment for Growth in Demand Weight on Demand Forecast Orders <Airline Capacity Supply Line> <Number of Miles Flown per Seat> + + Supply Line Adjustment for Growth in Demand <Airplane Retirements> + Desired Capacity Acquisition Rate <Expected Growth Rate for Demand> + <Expected Growth Rate for Demand> Desired Aircraft Supply Line Indicated Capacity Adjustment Adjustment For Capacity - - Time to Adjust Capacity Weight on Supply Line Adjustment + Indicated Orders for Capacity Mothballed Capacity <Cancelled Orders> Mothballing <Return to Service> Off Mothballing Cancellation <Into Storage> Orders of Airplanes <Return to Service> Airline Capacity Supply Line Initial Capacity on Order Airplane Manufacturing Completion Airline Capacity Initial Airline Capacity Demand Forecast Horizion <Time to Percieve Changes in Demand> Desired Capacity Supply Line Adjustment Time + Adjustment for the Supply Line - Order Approval Delay Time <Time Required to Manufacture an Airplane> Expected Desired Seat Demand Miles Airplane Retirements <Retired> 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 Summary Statistics Payoff Element <r> dif cov Sum of Error Squared over Mean Error over Mean Squared <Yi> MAE over Mean Sum AE <dt> <Yi> Residuals <Xi> <dt> <dif mean> MSE Us <dif var> <Count> RMSE Uc <dif cov> dif var <M Y> <M X> dif mean <M X> <Xi> Sum APE <M X> MAPE RMSE over Mean <Xi> <Yi> <Xi> <Available Seat Miles> SumX2 Simulated <dt> SumXY Y Yi Sum Yi <M X> Mxy <Time> Percent Error Sx <M Y> MY End Time Count Sy <Count> pick <Simulated> Interval <TIME STEP> dt <dt> <Count> MX2 <Count> <Historical> Start Time Um <Sy> <Sx> MX r MY2 <dt> SumY2 Sum Xi X <Historical Available Seat Miles> Historical Xi R^2 <Yi> Example of Fitting the Model 1. Open Simulation Control 2. Create a Payoff Example of Fitting the Model 3. Run “Policy” Negative Example of Fitting the Model 4. Set Parameters Example of Fitting the Model 5. Capacity Fit – Historical Inputs Historical and Simulated Airline Capacity Seat*miles/Year 1e+012 750 B 500 B 250 B 0 1970 1974 1978 1982 1986 Time (Year) 1990 1994 1998 Historical Available Seat Miles : Match All Available Seat Miles : Match All “R^2” MAE/Mean RMSE/Mean Um 0.995 0.0224 0.0309 0.0043 Uc 0.7475 Us 0.2480 Demand – Causal Loop Diagram + Ticket Price R Price War Load Factor + Congestion + B - Delivery Delay - + Demand + Capacity R + B Capacity Shortfall Capacity Control + Investment GDP + Forecasted Demand Route Networks + + Desired Capacity Demand – Model Structure Historical Population Data and Projections Initial Population <Time> Population Actual Demand For Seat Miles Historical GDP Data and Projections Miles per Person per Dollar of GDP GDP per Capita Seat Miles Desired from GDP per Capita <Time> Sensitivity of Demand to Congestion Congestion Perception Time Indicated Effect of Congestion on Demand <Indicated Load Factor> <Comfortable Industry Load Factor> Demand for Seat Miles per Capita Effect of Congestion on Demand Constant Demand Adjustment <Ticket Price> Effect of New Capacity on Demand Effect of Price on Demand Strength of New Capacity Effect on Demand Elasticity of Demand with Respect to Price Reference Ticket Price One Year Percent Change in Capacity <Lag for <Available Seat Measuring Miles> Changes> <Initial Ticket Price> Demand Fit – Historical Inputs Historical and Simulated Revenue Passanger Miles Seat*miles/Year 800 B 600 B 400 B 200 B 0 1970 1974 1978 1982 1986 Time (Year) 1990 1994 1998 Historical Airline Demand : Match All Actual Demand For Seat Miles : Match All “R^2” MAE/Mean RMSE/Mean Um 0.99 0.0273 0.0356 0.0033 Uc 0.9595 Us 0.0371 Price – Model Structure <Cost per Available Seat <Ticket Price> Mile> Target Margin Sensitivity of Price to Margin Target Percentage Above Cost Effect of Margin on Price <Operating Margin> <Load Factor> <Desired Load Factor> Sensitivity of Price to Costs Effect of Costs on Price Effect of Demand Supply Balance on Price + Sensitivity of Price to Demand Supply Balance + Minimum Ticket Price Indicated Ticket Price per Seat Mile Indicated Price Ticket Price Change in Ticket Price Initial Ticket Price Time to Adjust Ticket Prices <Cost per Available Seat Mile> Price Fit – Historical Inputs Historical and Simulated Ticet Price dollars/(Seat*mile) 0.2 0.15 0.1 0.05 0 1970 1974 1978 1982 1986 Time (Year) 1990 1994 1998 Historical Airline Ticket Prices : Match Price Ticket Price : Match Price “R^2” MAE/Mean RMSE/Mean Um 0.98 0.0583 0.0710 0.0004 Uc 0.2980 Us 0.7015 Costs – Causal Loop Diagram + Costs + Ticket Price + B R Costs Price War Load Factor B + Congestion Competition + Revenue + B - - Delivery Delay + Demand + Capacity B + Forecasted Demand R + Capacity Shortfall Route Networks + + Desired Capacity Capacity Control + Investment+ GDP R Investment + Profit Costs - Model Structure Table for Producer Price Index <Time> Initial Other Variable Costs Producer Price Index Other Variable Costs Variable Costs per Seat Mile <Available Seat Miles> Cost per Available Seat Mile 1982 Other Variable Costs Variable Costs from Jet Fuel Historical Jet Fuel Cost per Gallon Projected Fuel Cost Fuel Cost per Gallon <Time> Variable Cost from Operations <Average Worker Compensation> Operating Costs from Passengers Costs from + Wages Total Operating Costs Gallons per Seat Mile Operating Costs From Freight Freight as a Percentage of Passenger Operations Total Worker Salary by Type + Workers Per Seat of Capacity + Total Workers by Type + <Airline Capacity> Cost Fit – Historical Inputs Historical and Simulated Operating Costs 200 B dollars/Year 150 B 100 B 50 B 0 1970 1974 1978 1982 1986 Time (Year) 1990 1994 1998 Historical Airline Operating Costs : Match Cost Total Operating Costs : Match Cost “R^2” MAE/Mean RMSE/Mean Um 0.99 0.055 0.0719 0.0603 Uc 0.6697 Us 0.2698 National Average Wage Data Wages – Model Structure Wage Premium for Skill Initial Worker Compensation National Average Wage Average Worker Compensation <Time> Wage Relative to Average <Number of Miles Flown per Seat> Strength of Outside Opportunities on Worker Compensation Historical Airline Capacity <Airline Capacity> <Lag for Measuring Changes> Recent Airline Capacity Time to Change Worker Compensation Effect of Outside Opportunities on Worker Compensation Change in Worker Compensation Strength of Inflation on Worker Compensation Gap For Worker Compensation Effect of Inflation in Worker Compensation One Year Change in New Hires Effect of New Hires on Worker Compensation Indicated Compensation CPI Data Effect of Operating Margin on Worker Compensation Strength of New Hire Effect on Worker Compensation Effect of Unemployment on Worker Compensation Historical Unemployment <Time> Normal Unemployment CPI Normal Margin Strength of Margin on Worker Compensation Recent Margin Strength of Unemployment Effect on Wages Historical Unemployment Data CPI Percentage <Time> Change <Operating Margin> Margin Perception Delay Profits – Model Structure <Load Factor> Revenue Seat Miles <Ticket Price> Freight as a Percentage of Passenger Operations Passenger Revenue <Available Seat Miles> Freight Revenue Operating Revenue Operating Margin <Time> Operating Profit check year <Total Operating Costs> New Operating Profit Accumulated Operating Profit Drain Operating Profit <TIME STEP> Reported Operating Profit Wages Fit – Historical Inputs Historical and Simulated Average Wages dollars/(Year*worker) 80,000 60,000 40,000 20,000 0 1970 1974 1978 1982 1986 Time (Year) 1990 1994 1998 Historical Airline Salaries : Match All Average Worker Compensation : Match All “R^2” MAE/Mean RMSE/Mean Um 0.99 0.0278 0.0398 0.0294 Uc 0.9426 Us 0.0278 Real Wages Fit – Historical Inputs Real Wages sim vs actual dollars/(Year*worker) 20,000 17,500 15,000 12,500 10,000 1970 1974 1978 1982 1986 Time (Year) 1990 1994 1998 Simulated Real Wage : Match All Historical Real Wage : Match All “R^2” MAE/Mean RMSE/Mean Um 0.68 0.0262 0.0339 0.0290 Uc 0.9370 Us 0.0339 Full model Optimization Move from partial model tests to full model parameterization Fits are slightly worse, parameters more believable Historical and Simulated Ticet Price Historical and Simulated Airline Capacity 0.2 dollars/(Seat*mile) Seat*miles/Year 2e+012 1.5e+012 1e+012 500 B 0 1971 1975 1979 1983 1987 Time (Year) 1991 1995 Historical Available Seat Miles : Match All Available Seat Miles : Match All MAE/Mean RMSE/Mean 0.0459 0.0564 1999 0.15 0.1 0.05 0 1971 1975 1979 1983 1987 Time (Year) 1991 1995 1999 Historical Airline Ticket Prices : Match All Ticket Price : Match All MAE/Mean RMSE/Mean 0.0508 0.0595 Full model Optimization Historical and Simulated Operating Costs 800 B 200 B 600 B 150 B dollars/Year Seat*miles/Year Historical and Simulated Revenue Passanger Miles 400 B 50 B 200 B 0 1971 100 B 1975 1979 1983 1987 Time (Year) 1991 1995 Historical Airline Demand : Match All Actual Demand For Seat Miles : Match All MAE/Mean RMSE/Mean 0.0345 0.0434 1999 0 1971 1975 1979 1983 1987 Time (Year) 1991 1995 Historical Airline Operating Costs : Match All Total Operating Costs : Match All MAE/Mean RMSE/Mean 0.0372 0.0465 1999 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 Profits Still Questionable Historical and Simulated Profit 10 B dollars/Year 5B 0 -5 B -10 B 1971 1975 1979 1983 1987 Time (Year) Historical Airline Operating Profit : Match All Reported Operating Profit : Match All 1991 1995 1999 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?