Download Airline Industry Profit Cycles

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?