Kawika Pierson MIT System Dynamics Group 3 nd Year PhD Fall 2008 Albany-MIT PhD Colloquium The...

Kawika Pierson MIT System Dynamics Group 3nd Year PhD

Fall 2008 Albany-MIT PhD Colloquium

The Cyclical Nature of Airline Industry Profits

OutlineRelevant LiteratureReference Mode for Airline ProfitsDigging DeeperThe Model

DemandPrice Capacity CostsProfit

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, 2000Commercial jet aircraft industryFocused on use of SD models as forecasts for

Jet OrdersProprietary, 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

loopControl theory perspective

Reference ModeThe 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 DeeperProfit = Revenue – CostsRevenue = Price * sales in unitsCosts = unit cost * productionSales is Revenue Passenger MilesPrice is the Price of TicketsProduction is Available Seat MilesThis gives us ProfitHow does financial reporting effect our

modeling?

Capacity – Causal Loop Diagram

Demand

Capacity

+Forecasted

Demand

DesiredCapacity

Investment

CapacityShortfall

+-

+

+

+

CapacityControl

B

Capacity - Model Structure

Airline CapacityAirline Capacity

Supply LineOrders ofAirplanes

AirplaneManufacturing

Completion

AirplaneRetirements

Initial Capacityon Order

Initial AirlineCapacity <Retired>

AdjustmentFor Capacity

Time to AdjustCapacity

Adjustment for theSupply Line

Weight on SupplyLine Adjustment

IndicatedCapacity

Adjustment

Cancellation

<CancelledOrders>

Mothballed Capacity

Mothballing Off Mothballing

<IntoStorage>

<Return toService>

<Return toService>

DesiredCapacity

+

-

-

Desired AircraftSupply Line

+

<Time Required toManufacture an

Airplane>

+

PlannedReplacement

Orders

Desired CapacityAcquisition Rate

Supply LineAdjustment Time

+

-

-

Indicated Ordersfor Capacity

+

Order ApprovalDelay Time

<AirplaneRetirements>

Third Order Stocks –Cancellation and Mothballing

Time Required toManufacture an

Airplane

Capacity onOrder 1

Capacity onOrder 2

Capacity onOrder 3

Ordering 1 to 2 2 to 3 Completion

<Orders ofAirplanes>

CancelledOrders

Time to Cancel

<Indicated Ordersfor Capacity>

+

<AirplaneManufacturingCompletion>

New CapacityCapacity 1 Capacity 2

Cap 1 to 2Capacity 3

Cap 2 to 3 Retired

AverageService Life

<OperatingMargin>

Into Storage

Time toMothball

Capacity on Mothball

Margin Threshold toInitiate Mothballing

Return toService

<OperatingMargin>

<Time toMothball>



Forecasting Demand

PerceivedDemand

Time to PercieveChanges in Demand

Time Horizion forReference Demand

ReferenceDemand

Change inReference Demand

IndicatedGrowth Rate

<Time Horizion forReference Demand>

Time to PercieveTrend in Demand

Expected GrowthRate for Demand

Change inExpected Growth

Rate

Initial ExpectedGrowth Rate in

Demand

<Actual DemandFor Seat Miles>

<HistoricalAirline Demand>

Change inDemand

Perception

Gap in DemandPerception

<Switch forHistoricalVariables>

Correction For Growth

Airline CapacityAirline Capacity

Supply LineOrders ofAirplanes

AirplaneManufacturing

Completion

AirplaneRetirements

Desired LoadFactor

Initial Capacityon Order

Initial AirlineCapacity

Supply LineAdjustment for

Growth in Demand

<Retired>

AdjustmentFor Capacity

Desired SeatMiles

Time to AdjustCapacity

Adjustment for theSupply Line

Weight on SupplyLine Adjustment

IndicatedCapacity

Adjustment

Weight on DemandForecast Orders

Cancellation

<CancelledOrders>

Mothballed Capacity

Mothballing Off Mothballing

<IntoStorage>

<Return toService>

<Return toService>

DesiredCapacity

+

-

-

Desired AircraftSupply Line

+

<Time Required toManufacture an

Airplane>

+

PlannedReplacement

Orders

<AirlineCapacity>

<Airline CapacitySupply Line>

+

Desired CapacityAcquisition Rate

Supply LineAdjustment Time

+

-

-

Indicated Ordersfor Capacity

+

Order ApprovalDelay Time

ExpectedDemand

Demand ForecastHorizion

<Expected GrowthRate for Demand>

<Expected GrowthRate for Demand>

<Time to PercieveChanges in Demand>

<PerceivedDemand>

<Number of MilesFlown per Seat>

Capacity Adjustmentfor Growth in Demand

<AirplaneRetirements>

Fitting to DataGet historical data on important stocks

Airlines are great for thisAirlines.org, MIT Airline Data Project, BTS

Set up summary statisticsJohn Sterman’s Book plus MAE, RMSE, %E,

Thiel, SSE/M^2Drive each model sector with historical

variables Use Vensim’s model fitting functions

Lets walk through this

Summary Statistics

R^2

rM X

M Y

MxySx

Sy

Sum Xi

dt

Xi

pick

X

Historical

Count

Sum YiYi

Y

Simulated

<dt> SumXY

<Xi> <Yi>

MY2

SumY2

<Count>

<dt>

<M Y>

<Yi>

<Count>MX2

SumX2 <dt>

<Xi>

<M X>

<Count>

Start Time

End Time

Interval

<Time>

<TIME STEP>

<HistoricalAvailable Seat

Miles>

<Available SeatMiles>

<Historical>

<Simulated>

Percent Error

<Yi>

<Xi>

Residuals <Yi> <Xi> <dt>

Sum AE

Sum APE

<Count>

MAPE

MAE overMean

<M X>

<r>

<Sy> <Sx>

dif cov

dif var

dif mean

<M X>

<M Y>

MSE

RMSE

Um

Us

Uc <dif cov>

<dif mean>

<dif var>

RMSE overMean

Payoff Element

Error over MeanSquared

Sum of ErrorSquared over Mean

<M X>

<dt>

Example of Fitting the Model1. Open Simulation

Control

2. Create a

Payoff

Example of Fitting the Model3.

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

1e+012

750 B

500 B

250 B

0

1970 1974 1978 1982 1986 1990 1994 1998Time (Year)

Sea

t*m

iles/

Yea

r

Historical Available Seat Miles : Match AllAvailable Seat Miles : Match All

“R^2” MAE/Mean RMSE/Mean Um Uc Us0.995 0.0224 0.0309 0.0043 0.7475 0.2480

Demand – Causal Loop Diagram

Demand

Capacity

+Forecasted

Demand

DesiredCapacity

Investment

CapacityShortfall

+-

+

+

+

CapacityControl

B

+

R

RouteNetworks

Load Factor

+-

Congestion+

-Delivery Delay

B

Ticket Price+

GDP+

-

R

Price War

Demand – Model Structure

Actual DemandFor Seat Miles

ReferenceTicket Price

Effect of Priceon Demand

<Ticket Price>

Historical PopulationData and Projections

Population

<Time>InitialPopulation

Demand for SeatMiles per Capita

Seat Miles Desiredfrom GDP per Capita

GDP perCapita

Miles per Person perDollar of GDP

Historical GDP Dataand Projections

Elasticity of Demandwith Respect to Price

<Time>

ConstantDemand

Adjustment

<Initial TicketPrice>


Effect of NewCapacity on

Demand

Strength of NewCapacity Effect on

Demand

One Year PercentChange in Capacity

<Lag forMeasuringChanges>

Effect ofCongestion on

Demand

Indicated Effect ofCongestion on

Demand

CongestionPerception Time

Sensitivity ofDemand toCongestion

<ComfortableIndustry Load

Factor>

<Indicated LoadFactor>

Demand Fit – Historical Inputs


Historical and Simulated Revenue Passanger Miles

800 B

600 B

400 B

200 B

0

1970 1974 1978 1982 1986 1990 1994 1998Time (Year)

Sea

t*m

iles/

Yea

r

Historical Airline Demand : Match AllActual Demand For Seat Miles : Match All

Price – Model Structure

Ticket PriceChange in

Ticket Price

MinimumTicket Price

Time to AdjustTicket Prices

Indicated TicketPrice per Seat Mile

Indicated Price

Initial TicketPrice

TargetPercentageAbove Cost

<Cost perAvailable Seat

Mile>Effect of DemandSupply Balance

on Price

+

<Desired LoadFactor>

Sensitivity of Priceto Demand Supply

Balance

<Load Factor>

-+

<Cost perAvailable Seat

Mile>

Effect of Costson Price

<Ticket Price>

Sensitivity ofPrice to Costs

Effect of Marginon Price

<OperatingMargin>

Sensitivity ofPrice to Margin

Target Margin

Price Fit – Historical Inputs


Historical and Simulated Ticet Price

0.2

0.15

0.1

0.05

0

1970 1974 1978 1982 1986 1990 1994 1998Time (Year)

dolla

rs/(

Sea

t*m

ile)

Historical Airline Ticket Prices : Match PriceTicket Price : Match Price

Costs – Causal Loop Diagram

Demand

Capacity

+Forecasted

Demand

DesiredCapacity

Investment

CapacityShortfall

+-

+

+

+

CapacityControl

B

+

R

RouteNetworks

Load Factor

+-

Congestion+

-Delivery Delay

BRevenue

Ticket Price+

+

Profit

+

+

Costs+

+

-

Costs

B

R

Investment

Competition

B

GDP+

-

R

Price War

Costs - Model Structure

Operating Costsfrom Passengers

Costs fromWages

Variable Costfrom Operations

Variable Costsper Seat Mile

<AirlineCapacity>


Cost per AvailableSeat Mile

Variable Costsfrom Jet Fuel

Other VariableCosts

Gallons perSeat Mile

Fuel Cost perGallon

Historical Jet FuelCost per Gallon

<Time>

Projected FuelCost

Total WorkerSalary by Type Total Workers

by Type

Workers Per Seatof Capacity

++

+

+

<Average WorkerCompensation>

Initial OtherVariable Costs

1982 OtherVariable Costs

Producer PriceIndex

Table forProducer Price

Index

<Time>

Operating CostsFrom Freight

Total OperatingCosts

Freight as aPercentage of

Passenger Operations

Cost Fit – Historical Inputs


Historical and Simulated Operating Costs

200 B

150 B

100 B

50 B

0

1970 1974 1978 1982 1986 1990 1994 1998Time (Year)

dolla

rs/Y

ear

Historical Airline Operating Costs : Match CostTotal Operating Costs : Match Cost

Wages – Model Structure

<OperatingMargin>

AverageWorker

CompensationChange in Worker

Compensation

IndicatedCompensation

Initial WorkerCompensation

<AirlineCapacity> One Year Change in

New Hires

Effect of Inflation inWorker Compensation

<Lag forMeasuringChanges>

Effect of OutsideOpportunities on Worker

Compensation

Effect of New Hireson Worker

Compensation

Recent AirlineCapacity

Time to ChangeWorker

Compensation

Effect of OperatingMargin on Worker

Compensation

Gap For WorkerCompensation

National AverageWage Data

NationalAverage Wage

Wage Relative toAverage

<Time>

CPI PercentageChange

CPI Data

<Time>

Strength of OutsideOpportunities on Worker

Compensation

Strength of New HireEffect on WorkerCompensation

Strength of Margin onWorker Compensation

Strength of Inflationon Worker

Compensation

Wage Premiumfor Skill

<Number of MilesFlown per Seat>

Historical AirlineCapacity

CPI

Normal Margin

HistoricalUnemployment

Data

HistoricalUnemployment

<Time>

Strength ofUnemployment Effect

on Wages

Effect ofUnemployment on

Worker Compensation

NormalUnemployment

Recent MarginMargin

Perception Delay

Profits – Model Structure

Revenue SeatMiles

PassengerRevenue

OperatingProfit

OperatingMargin


<Load Factor>

<Ticket Price>

OperatingRevenue

AccumulatedOperating

ProfitNew OperatingProfit

Drain OperatingProfit

<TIME STEP>

check year

<Time>

<Total OperatingCosts>

FreightRevenue

Freight as aPercentage of

Passenger Operations

ReportedOperating Profit

Wages Fit – Historical Inputs


Historical and Simulated Average Wages

80,000

60,000

40,000

20,000

0

1970 1974 1978 1982 1986 1990 1994 1998Time (Year)

dolla

rs/(

Yea

r*w

orke

r)

Historical Airline Salaries : Match AllAverage Worker Compensation : Match All

Real Wages Fit – Historical Inputs


Real Wages sim vs actual

20,000

17,500

15,000

12,500

10,000

1970 1974 1978 1982 1986 1990 1994 1998Time (Year)

dolla

rs/(

Yea

r*w

orke

r)

Simulated Real Wage : Match AllHistorical Real Wage : Match All

Full model OptimizationMove from partial model tests to full model

parameterizationFits are slightly worse, parameters more

believable

Historical and Simulated Airline Capacity

2e+012

1.5e+012

1e+012

500 B

0

1971 1975 1979 1983 1987 1991 1995 1999Time (Year)

Sea

t*m

iles/

Yea

r

Historical Available Seat Miles : Match AllAvailable Seat Miles : Match All

Historical and Simulated Ticet Price

0.2

0.15

0.1

0.05

0

1971 1975 1979 1983 1987 1991 1995 1999Time (Year)

dolla

rs/(

Sea

t*m

ile)

Historical Airline Ticket Prices : Match AllTicket Price : Match All

MAE/Mean RMSE/Mean MAE/Mean RMSE/Mean 0.0459 0.0564 0.0508 0.0595

Full model Optimization

MAE/Mean RMSE/Mean MAE/Mean RMSE/Mean 0.0345 0.0434 0.0372 0.0465

Historical and Simulated Revenue Passanger Miles

800 B

600 B

400 B

200 B

0

1971 1975 1979 1983 1987 1991 1995 1999Time (Year)

Sea

t*m

iles/

Yea

r

Historical Airline Demand : Match AllActual Demand For Seat Miles : Match All

Historical and Simulated Operating Costs

200 B

150 B

100 B

50 B

0

1971 1975 1979 1983 1987 1991 1995 1999Time (Year)

dolla

rs/Y

ear

Historical Airline Operating Costs : Match AllTotal Operating Costs : Match All

Parameters More BelievableIn Partial Model Test SLAT = 0.05 TAC = 1

Theoretically should be very similarIn 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

5 B

0

-5 B

-10 B

1971 1975 1979 1983 1987 1991 1995 1999Time (Year)

dolla

rs/Y

ear

Historical Airline Operating Profit : Match AllReported Operating Profit : Match All

ConclusionsGrowth CorrectionPartial Model Tests with Historical InputsCyclical Nature not alleviated by

Cancellations or MothballingStandard SD Structures fit the industry

reasonably wellMore dynamics exist in the real system

Comments? Questions?

Kawika Pierson MIT System Dynamics Group 3 nd Year PhD Fall 2008 Albany-MIT PhD Colloquium The...

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