Transcript Airplane Costs (slides

Biography for William Swan
Chief Economist, Seabury-Airline
Planning Group. AGIFORS Senior
Fellow. ATRG Senior Fellow.
Retired Chief Economist for Boeing
Commercial Aircraft 1996-2005
Previous to Boeing, worked at
American Airlines in Operations
Research and Strategic Planning
and United Airlines in Research and
Development. Areas of work
included Yield Management, Fleet
Planning, Aircraft Routing, and
Crew Scheduling. Also worked for
Hull Trading, a major market maker
in stock index options, and on the
staff at MIT’s Flight Transportation
Lab. Education: Master’s,
Engineer’s Degree, and Ph. D. at
MIT. Bachelor of Science in
Aeronautical Engineering at
Princeton. Likes dogs and dark
beer. ([email protected])
© Scott Adams
What Airplanes Should Cost
William M. Swan
Chief Economist
Boeing Commercial Airplanes Marketing
August 2003
Two Things that “bug” Me
Thing One
1. Network Design would be very easy if:
Cost per flight were linear with size
Also handy if it were linear with distance
Two Things that “bug” Me
Thing 2
2. Policy tests would be very easy if:
Cost per ASK were linear with distance
Also handy if it were linear with airplane size
Why are Networks “Easy”?
• Linear cost with size implies
– A fixed cost per link or per frequency
• (in our example equal to about 100 seats)
– Constant marginal cost for capacity
– Capacity and Link Existence separable
• Linear cost with distance implies
– Mileage and Departure costs are separable
• Separable and linear costs allow Linear
Programming formulations of Networks
Why are Policy Tests “Easy”
• Known cost vs. distance allows adjustments
– Compare efficiency at different stage lengths
• $.083/seat-km @500km same as $.050 @1550km
– Linear means average does not depend on mix
• Known cost vs. size also allows adjustments
• $.050/seat-km @150 seats same as $.045 @200
– Although sizes are less significantly different
• Real data points too few to test policy and
calibrate cost function simultaneously
Thing 1 “bugs” me Because
Csot per Trip at 1500 Km (Index)
• I “know” that the distance relationship is:
35
Cost Linear with Seats
30
25
20
15
10
5
0
0
100
200
300
Seats
400
500
600
Thing 2 “bugs” me Because
• I “know” that the size relationship is:
Trip Cost at 150 Seats (Index)
35
Cost Linear with Distance
30
25
20
15
10
5
0
0
1000
2000
3000
Trip Distance (Km)
4000
5000
Arrogance Invented:
Why do I “know” these things?
1. At M.I.T. we found costs linear with with
size and distance for parametric designs of
airplanes with identical missions and
levels of the three basic technologies of
propulsion, structure, and drag
Arrogance Revisited:
Why do I “know” these things?
2. At United Airlines we found costs linear
with distance for existing airplanes used in
assignments over the network for route
planning and with size using the same
program with fleet planning costs.
Arrogance Revisited Again:
Why do I “know” these things?
3. At Boeing the relationships are linear with
distance and size based on data generated
by a standard cost program used both for
sales and for product planning. This
program has accumulated over 30 years of
calibration and use.
The Challenge: Share the Results
without sharing proprietary data
• We can use cost “index” values
– Applications require relative costs, not absolute
• We can fit trends to data points
– No need to publish airplane-model level values
• We can discuss motivation for shapes
– Engineering cost functions: each formula has a
reason for its algebraic structure
First: Imagine the data
Cost Per Airplane Trip
Trip Distance (KM)
SEATS
500
1000
1500
2000
2500
3000
4000
5000
100
2.5
3.5
4.5
5.5
6.5
7.5
9.5
11.5
150
3.8
5.3
6.8
8.3
9.8
11.3
14.3
17.3
200
5.0
7.0
9.0
11.0
13.0
15.0
19.0
23.0
250
6.3
8.8
11.3
13.8
16.3
18.8
23.8
28.8
300
7.5
10.5
13.5
16.5
19.5
22.5
28.5
34.5
400
10.0
14.0
18.0
22.0
26.0
30.0
38.0
46.0
About the Data
• Each cell is generated by one “run” of the
Cost Model
• Able to get data covering entire operating
range an airplane is capable of
• Able to get data for airplanes of many
different sizes
• Able to generate as many points as we want
About the Airplanes
• Boeing Airplanes only
– Covers all sizes from smallest to largest
• Competitive product in all categories
– No biases due to manufacturer preference
• Credibility not subject to whinging suspicions
– Conclusions checked offline using Airbus too
• Airbus data less certain, increased scatter
• Did not change conclusions
Comparable Seat Count Rules
Desired “fair” measure of airplane capacity
Seat counts at comparable “use”:
1. Same pitch and width by class
2. Same mix of classes onboard
3. Same ratios of lavatories, galleys, closets
Counts were “theoretical”
•
•
•
adjusted closest configuration to targets above
results were fair relative sizes
Results were near practical counts
Public Sources Failed
• Seating density changes with usage
– The longer the haul, the lower the density
– More low-cost usage, the higher the density
• Tried and failed
– Most popular/Median scheduled seat count
– Median seats on ownership records (Airclaims)
– Either manufacturers’ claims
“Fair” Seat Counts are Hard to Get
airplane
Short-Haul
Long-Haul
737-700
122
88
737-800
153
110
737-900
165
119
757 200
187
135
767 200
204
147
757 300
226
163
767 300
250
180
767 400
286
206
777 200
385
277
777 300
481
346
747-400
536
386
“Fair” Seat Counts are Hard to Get
airplane
Short-Haul
Long-Haul
A318
103
74
A319
121
87
A320
140
101
A321
177
127
A310-200/300
218
157
A300
263
189
A330 200
291
210
A340 200
304
219
A330/340 300
335
241
A340-500
367
264
A340-600
418
301
Comparable Design Missions
• Within a type (“737”) and model (“-700”)
– Different engines, Different weight options
• Select for Comparable missions
– As close to 5000km max range for short-haul
– As close to 10000km range for long-haul
• 757s used although they fell short on range
• 767s at highest weights
• 777s and 747 at lowest weights
Major Cost Components: Crew
• Flight Crew cost per block hour
– Hourly costs increase with airplane size
– Pilots are about 12% of airplane costs
• Cabin Crew cost per block hour
– Hourly costs independent of airplane
– Crew in proportion to seat count (about 1:40)
– Cabin crew are about 10% of airplane costs
Major Cost Component: Fuel
• Fuel cost nearly linear with distance
– Cost per departure/landing cycle
– Cost per cruise kilometer
– Very small increase in cost for very long haul
• Due to cost of carrying weight of fuel used at end
• Fuel cost about 12% of total costs
• Actual use widely and accurately available
Major Cost Component:
Maintenance
• Maintenance on Engines and Airframe
– Per departure-landing cycle and per hour
– Includes labor, parts, and facilities
• Periodic Maintenance accrued
– Major checks every 3-4 years
• Steady-state values used
– 5-year Maintenance holiday from new is over
• Maintenance averages 13% of airplane costs
Major Costs: Ownership
• Ownership at 32% for new designs
– Estimate based on lease rates
– 0.8%-0.9% of market price, per month
• Lease rates include reported and unreported
costs:
– Return to capital
– Tax benefits
– Depreciation
Allocating Ownership
• Lease rates allocated based on trips per year
• Formula is:
– Trips = 4560/(1.5+800/distance(km))
• Matches hours and cycles by tail number
• No allocation of ownership to peak season
or peak flight of the day
• Works out to ownership per km and per
cycle
Cost Categories: Fees
• Landing fees based on weight
• Air Traffic Control based on weight & hrs.
• Run 8%-14% of airplane costs
– Outside of US (US is lower)
• Security and other passenger charges not
included in airplane costs
– These costs are growing
Cost Categories: Overheads
• Overheads not covered in this analysis
• Airplane costs above are 60% of total costs
• Additional 20-30% is General and
Administrative
– Thought to be proportional to airplane costs
• Additional 20% is passenger costs
– Mostly proportional to revenues, or costs
– Some on a per-passenger-trip basis
Rule Sets for Costing Methodology
• Cost formulas have various rule sets
– US domestic, European Short-haul, .......
• European Short-Haul used under 5000km
– Representative for all non-US regional flying
• International Long-Haul used over 5000km
• Choice of rule sets secondary for relative
costs of different airplanes or ranges.
Results
• Short-Haul Cost per Airplane trip:
$ = k * (Dist + 722) * (Seats + 97)
Dist is trip distance in km
Seats are airplane size in short-haul seat count
k is about $0.02
• Long-Haul Cost per Airplane trip:
$ = K * (Dist + 530) * (Seats + 205)
Dist is trip distance in km
Seats are airplane size in long-haul seat count
K is about $0.03
• Long-haul seats = 72% of short-haul seats
Calibration of Results
• Linear Regression of the form:
– $ = a + b * dist + c * seats + d * (seats*dist)
• Two-stage calibration used to get planar
form: $ = k * (dist + c) * (seats + b)
$ = k* {c*b + b*dist + c*seats + dist*seats}
Stage 1: for each airplane size $ = z * (dist + c)
Stage 2: pick z = k * (seats + b)
least squares unbiased estimate of k and b
Comments on Calibration
• Values almost unchanged doing size first, then
range rather than range, then size
• Values almost unchanged using least squared
percentage error, or summed absolute error
• Matrix of input data points had correct averages
for seats and range
• Matrix of input data points covered wide spectrum
of ranges and seats
Linear Regression Comparison
• Run against same data points
• Format:
$ = a + b * dist + c * seats + d * (seats*dist)
Formulated to test whether “d” is different from
planar
• Results:
short $ =
long $ =
Cobb-Douglas Comparison
• Run against same data points
• Gives Elasticities from “certain” data
– Very broad spectrum of ranges
– Very broad spectrum of airplane sizes
• Results:
– Short $ = x * dista * seatsb
– Long $ = x * dista * seatsb
Cobb-Douglas is no Less Accurate
• Plot C-D vs Planar
• --just less linear and therefore less
convenient
C-D elasticities compare to other
work
• Note other work results.
• Why different?
– Few points’
– Little change in range or size
– Step changes in labor costs, year to year
• Based on contract renegotiations
Why I like the Planar results
• Points are “perfect” – no complicating
factors
• Points cover entire range of size and
distances
• No false correlation between size and range
• No false correlation between range and
seating density
• No airport-specific effects
What is NOT in the soup
• Factor input prices the same for all points
– Includes labor, fuel, capital, airports
• Per passenger (therefore load factor) out
– No passenger-related costs, just seats
• Supplemental crews at long distances
What it is Good For
• Great for adjusting different airlines’ or
years’ costs to same stage length and seating
capacity—for comparing firm-specific costs
• Great for designing networks based on
typical cost functions
Adjusting for Southwest
• Ownership (trips per year)
• Seating count
• Factor inputs: same
Adjusting for Pacific
• Seat count
• Crew costs?
Conclusion
• Linear works
– Close enough for network design
– Makes network problem much simpler
• Relative costs work
– Good for normalizing firm-specific
– Helps overcome small sample problems
(eliminates size, range variables)
William Swan:
Data Troll
Story Teller
Economist