Transcript Document

Biography for William Swan
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
Airline Competition
William M. Swan
Chief Economist
Seabury Airline Planning Group
Nov 2005
A Stylized Game
With Realistic Numbers
1. The Simplest Case, Airlines A & Z
2. Preferred Airline matches on price
3. Time-of-day Games
The Simplest Case: Airlines A & Z
•
•
Identical airlines in simplest case
Two passenger types:
1. Discount @ $100, 144 passengers demand
2. Full-fare @ $300, 36 passengers demand
Average fare $140
•
Each airline has
–
–
–
100-seat airplane
Cost of $126/seat
Break-even at 90% load, half the market
We Pretend Airline A is Preferred
• All 180 passengers prefer airline A
– Could be quality of service
– Maybe Airline Z paints its planes an ugly
color
• Airline A demand is all 180 passengers
–
–
–
–
–
–
–
Keeps all 36 full-fare
Fills to 100% load with 64 more discount
Leaves 80 discount for airline Z
Average A fare $172
Revenue per Seat $172
Cost per seat was $126
Profits: huge
Airline Z is not Preferred
•
•
•
•
•
Gets only spilled demand from A
Has 80 discount passengers on 100 seats
Revenue per seat $80
Cost per seat was $126
Losses: huge
“not a good thing”
Preferred Carrier Does Not Want to
Have Higher Fares
• Pretend Airline A charges 20% more
– Goes back to splitting market evenly with Z
– Profits now 20%
– Profits when preferred were 36%
• 25% extra revenue from having all of full-fares
• 11% extra revenue from having high load factor
• Airline Z is better off when A raises prices
– Returns to previous break-even condition
Major Observations
• Average fares look different in matched case:
– $172 for A vs. $80 for Z
• Preferred Airline gains by matching fares
– Premium share of premium traffic
– Full loads, even in the off-peak
– Even though discount and full-fares match Z
• Practice shows few lower-quality survivors
More Observations
• “Preferred wins” result drives quality
matching between airlines
• Result is NOT high quality
– Everybody knows everybody tries to match
– Therefore quality is standardized, not high
• Result is arbitrary quality level
Avoiding Competition
Airlines avoid head-to-head competition:
Serve a different time of day
Capture customers with loyalty
frequent flyer programs
control sales outlets
cultural dominance in home market
preferred for certain “style”
Use a different airport
Time-of-Day Games
• What if 2/3 preferred case was because Z was at a
different time of day?
– 1/3 of people prefer Z’s time of day
– 1/3 of people prefer A’s time of day
– 1/3 of people can take either, prefer Airline A’s quality (or color)
• Ground rules: back to simple case
– No peak, off-peak spill
– Back to 100% maximum load factor
– System overall at breakeven revenues and costs
• Simple case for clarity of exposition
– Spill issues add complication without insight
– Spill will merely soften differences
Simple Time-of-Day Model
Total Demand
Onl
y
AM
PM
any
Morning
Midday
Evening
17.5%
17.5%
17.5%
15%
15%
17.5%
Both A & Z in Morning
Z=0F, 80D
A=36F, 64D
RAS=$ 80
RAS=$172
Full
Fare
Morn Mid-ing Day
Even Dis- Morn Mid- Even
-ing count -ing Day -ing
Only 25% 25% 25% Only 10% 10% 10%
AM
PM
All
10%
10%
5%
AM
PM
All
20%
20%
30%
Z “Hides” in Evening
Z=17.1F,
A=18.9F, 81.1D
RAS=$138
Full
Fare
62.9D
RAS=$114
Morn Mid- Even Dis- Morn Mid- Even
-ing Day -ing count -ing Day -ing
Only 25% 25% 25% Only 10% 10% 10%
AM
PM
All
10%
10%
5%
AM
PM
All
20%
20%
30%
A Pursues to Midday
Z=13.5F,
A=22.5F, 77.5D
RAS=$145
Full
Fare
Morn Mid-ing Day
66.5D
RAS=$107
Even Dis- Morn Mid- Even
-ing count -ing Day -ing
Only 25% 25% 25% Only 10% 10% 10%
AM
PM
All
10%
10%
5%
AM
PM
All
20%
20%
30%
Competition involves 3 distributions
1. Demand varies by day of week
2ND AIRLINE GETS ALL PEAKS/VALLEYS
2. Demand has mix of prices
1ST AIRLINE GETS ALL OF HIGH FARES
3. Demand has time of day requirements
2ND AIRLINE AVOIDS 1ST AIRLINE’S TIMES
Summary and Conclusions
• Airlines have strong incentives to match
– A preferred airline does best matching prices
– A non-preferred airline does poorly unless it can
match preference.
• A preferred airline gains substantial revenue
– Higher load factor in the off peak
– Higher share of full-fare passengers in the peak
– Gains are greater than from higher prices
• A less-preferred airline has a difficult time
covering costs
• Preferred airline’s advantage is reduced by
1. Spill
2. Partial preference
3. Time-of-day distribution
Same Airport Pair Competition is a
Tough Game
• Airlines would prefer to be alone
• Deregulation allows airlines to start new
markets
• A competitive market means:
– Airlines like to start new routes
– Old routes loose connecting traffic to new
– Connecting competition is between hubs
– Nonstop markets have small number of
airlines
Herfindahl Number of Competitors
Long-Haul Competition Declines
Based on Airport-Pairs
3.5
3.0
2.5
2.0
1.5
1.0
1990
Asia-Europe
Pacific
Other Long
Atlantic
1992
1994
1996
1998
2000
2002
Herfindahl Number of Competitors
Regional Competition is Flat
3.1
2.9
2.7
Asia
Other Short
Europe
N.America
2.5
2.3
2.1
1.9
1.7
1.5
1990
1992
1994
1996
1998
2000
2002
Airport Pairs Served
“Reduced” Competition in Pairs Comes
with Many New Pairs
1000
900
800
700
600
500
400
300
200
100
0
1970
Atlantic
Other Long
Pacific
Asia-Europe
3x
4x
4x
1975
1980
1985
1990
1995
2000
2005
Forecasters in 1983 Had a Really Hard Time
Forecasters in 1983 Had a Hard Time
200
Seats Per Airplane
190
180
170
160
150
140
130
120
1970
1975
1980
1985
1990
1995
2000
Forecasters in 1990 Were Still Confused
230
1990
FORECAST
Seats Per Airplane
220
210
200
190
180
170
2004 data
1990 data
160
150
140
130
1970
1975
1980
1985
1990
1995
2000
2005
2010
What We Missed: New Routes
Daily Departures per Nonstop Pair, average
3.5
3.0
Nonstop Pairs (index)
Departures/Pair
2.5
2.0
1.5
1.0
1970
1975
1980
1985
1990
1995
2000
2005
Air Travel Growth Has Been Met By
Increased Frequencies and Non-Stops
Air Travel Growth Has Been Met By
Increased Frequencies and Non-Stops
250
225
200
Index
1985=100
Air Travel
Frequencies
175
150
Non-Stop Markets
125
Average Stage Length
100
75
1985
Average Airplane Size
1990
1995
2000
Seat Count is -4% of World ASK Growth
Smaller Airplanes - 4%
Longer Ranges 13%
New
Markets
41%
Added
Frequency
50%
Growth Patterns the Same at Closer Detail
Similar patterns all over the world
NE Asia regional
Europe regional
SE Asia regional
Oceania regional
SW Asia regional
S America regional
C America regional
Mid East regional
N America regional
NE Asia-SE Asia
SE Asia-SW Asia
SE Asia-Oceania
Europe-SW Asia
Europe-S America
C America-N America
Europe-C America
Europe-Africa
Europe-N America
S America-N America
NE Asia-N America
Big Routes Do Not Mean Big Airplanes
450
400
Seats Per Departure
350
300
250
200
150
Average
100
50
0
0
2000
4000
6000
8000
10000
12000
Seats Per Day
All Airport Pairs under 5000km and over 1000 seats/day
All Airport Pairs under 5000km and over 1000 seats/day
14000
16000
18000
Size in 1990 Not a Forecast for Size in 2000
Size in 1990 Not a Forecast for Size in 2000
Seats/Dep in 2000 (same pair)
450
400
350
300
250
200
150
150
200
250
300
350
Seats/Departure in 1990, Atlantic pairs
400
450
Big Airports Do Not Mean Big Airplanes
Seats per Departure
350
300
250
200
150
100
50
0
0
200
400
600
800
Jet Departures Per Day
Top 12 Markets in 12 World Regions
1000
1200
1400
d. Networks Develop from Skeletal to Connected
High growth does not persist at initial gateway hubs
 Early developments build loads to use larger airplanes:
Larger airplanes at this state means middle-sized
Result is a thin network – few links
A focus on a few major hubs or gateways
In Operations Research terms, a “minimum spanning tree”
 Later developments bypass initial hubs:
Bypass saves the costs of connections
Bypass establishes secondary hubs
New competing carriers bypass hubs dominated by incumbents
Large markets peak early, then fade in importance
 Third stage may be non-hubbed low-cost carriers:
The largest flows can sustain service without connecting feed
High frequencies create good connections without hub plan
Skeletal Networks Develop Links to
Secondary Hubs
Early Skeletal Network
Later Development bypasses Early Hubs
Fragmentation Theory
• Large markets peak early
• Bypass flying bleeds traffic off early markets
– Some connecting travelers get nonstops
– Others get competitive connections
– Secondary airports divert local traffic
• New airlines attack large traffic flows
• Frequency competition continues
Route Development Data:
Measures What Really Happens
• Compare top 100 markets from Aug 1993
– Top 100 by seat departures
– Growth to Aug 2003
• Data from published jet schedules
Largest Routes are Not Growing
as bypass flying diverts traffic
60%
50%
World, 1993-2003
Top 100 Routes
40%
30%
20%
10%
0%
-10%
-20%
ASK growth
Frequency
growth
Airplane size
growth
JFK Gateway Hub Stagnant for 30 Years
1400
5% of US 48
1200
Departures/Day
JFK
1000
800
600
400
200
03
20
01
20
99
19
97
19
95
19
93
19
91
19
89
19
87
19
85
19
83
19
81
79
19
August Jet Schedules
19
77
19
75
19
73
19
19
71
0
Herfinadahl Number of Competitors
Competition Rising in Long-Haul Flows
This time not pairs—but oceans
30
25
20
15
10
Atlantic
Pacific
Asia-Europe
Other Long
5
0
1970
1975
1980
1985
1990
1995
2000
2005
Hubs: The Whys and Wherefores
•
•
•
•
Just over half of trips are connecting
Thousands of small connecting markets
Early hubs are Gateways
Later hubs bypass Gateways
– One third of bypass loads are local—saving the
connection
– One third of bypass loads have saved one connect of
two
– One third of bypass loads are merely connecting over
a new, competitive hub
• Growth is stimulated by service improvements
– Bypass markets grow faster than average
Most Markets are Small
14%
12%
Too Small For
Nonstop
10%
8%
6%
4%
00
<1
60
0
16
00
+
<8
00
<4
00
<2
00
<1
0
<5
5
<2
.1
25
<6
.2
5
<1
2.
5
2%
0%
<3
Share of RPKs
16%
Passengers per Day One Way
Half of Travel is in Connecting Markets
14%
12%
10%
Connecting Markets
8%
6%
4%
Nonstop Markets
2%
O&D Passengers per Day
+
16
00
00
<1
6
0
<8
0
0
<4
0
0
<2
0
0
<1
0
<5
0
<2
5
.5
<1
2
25
<6
.
12
5
0%
<3
.
Share of World RPKs
16%
Lots of O&D Connections
Share of O&D Passengers
100%
90%
4-leg connect
Double Connect
1-connect
thru
nonstop
80%
70%
60%
50%
40%
30%
20%
10%
0%
0
0
0
0
0
0
0
0
0
0
0
0
0
30 100 200 300 400 500 600 700 800 900 000 100 200
1
1
1
St. Mi. Range Block (excludes US domestic O&Ds)
400000
350000
300000
250000
200000
150000
100000
50000
0
3+legs
2-legs
Nonstops
30
0
10
00
20
00
30
00
40
00
50
00
60
00
70
00
80
00
90
00
10
00
11 0
00
12 0
00
0
ASMs (000/day)
Half the Trips are Connecting
St. Mi. Range Block (excluding US domestic)
Local Traffic Share of
Onboard
Connecting Share of Loads
Averages about 50%
80%
70%
60%
50%
40%
30%
20%
10%
0%
0
2000
4000
6000
Flight Distance (Km)
8000
10000
Local % of Onboard Load
Long-Haul Flights are from Hubs,
and carry mostly connecting traffic
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
100
Point-to-Point Markets
Trend
150
Markets over 5000km
200
250
300
350
400
450
500
Seats per Departure
Hub Concepts
• Hub city should be a major regional center
– Connect-only hubs have not succeeded
– Early hubs are centers of regional commerce
• Early Gateway Hubs get Bypassed
– Early International hubs form at coastlines
– Interior hubs have regional cities on 2 sides
• Later hubs duplicate and compete with early hubs
–
–
–
–
Many of the same cities served
Which medium cities become hubs is arbitrary
Often better-run airport or airline determines success
Also the hub that starts first stays ahead
Three Kinds of Hubs
• International hubs driven by long-haul
–
–
–
–
Gateway cities
Many European hubs: CDG, LHR, AMS, FRA
Some evolving interior hubs, such as Chicago
Typically one bank of connections per day
• Regional hubs connecting smaller cities
– Most US hubs, with at least 3 banks per day
– Some European hubs, with 1 or 2 banks per day
• High-Density hubs without banking
– Continuous connections from continuous arrivals and departures
– American Airlines at Chicago and Dallas
– Southwest at many of its focus cities
Value Created by Hubs
The idea in business is to Create Value
Do things people want at a cost they will pay
Hubs make valuable travel options
Feeder city gets “anywhere” with one connection
Feeder city can participate in trade and commerce
Hubs are cost-effective
Most destinations attract less than 10 pax/day
Connecting loads use cost-effective airplanes
Hubs Compete with Other Hubs
• Compete on quality of connection
– Does the airport “work?”
•
•
•
•
•
•
Short connecting times
Reasonable walking distances
Reliable baggage handling
Few delayed flights
Recovery from weather disruptions
Later flights for when something goes wrong
Hubs Develop Pricing Mixes
• Higher fares in captive feeder markets
– Captive small cities
• Low fares in competitive large markets
– Markets with low-cost competition
• High connecting fares in small connecting
markets
• Low discount fares in selected connecting
markets to fill up empty seats
– Low connecting fares compete against nonstops
– Select low fare markets against competition
Hubs Work
•
•
•
•
•
Fare Rise Linearly with Distance
Fares decline Linearly with Market Size
Hubs serve Smaller Connecting Markets
Hubs get premium revenues for connects
Low Cost Carriers price Connections High
– Tend to charge sum of local fares
– Prices match Hub Carriers’ prices
• High Cost Carriers offer some low prices
– Discount fares on HCCs match average LCC fares
The Real Difference is
Hubs Serve Many more Small Markets
• US HCCs have “given up” local markets
–
–
–
–
–
Nonstop markets to hub city
Used to gain premium revenues
Now required to match LCCs
Revenues no longer cover union labor costs
HCCs have given up most traffic to LCCs
• Hubs serve connecting markets
–
–
–
–
Share of HCC revenues in small markets high
Share of LCC revenues in small markets low
Fares in small markets higher
More small market revenues mean higher HCC fares
HCC Revenues are 1/3 Small Markets
LCC Revenues are 10% Small Markets
70%
Connections
50%
HCCs
LCCs
40%
30%
20%
10%
O-D City Pair Market Size (log scale)
9
47
1
28
2
17
2
12
86
61
41
30
20
13
9
7
5
3
0%
1
Cumulative Revenues
60%
Hubs Make Travel Possible
• Hubs exist to serve small markets
• For US domestic network
– 25% of revenues are from small markets
– Over 30% of HCC revenues
– Under 10% of LCC revenues
• International “small markets” add to this
• US has higher share nonstop than world
Local % of Onboard Load
Long-Haul Flights are from Hubs,
and carry mostly connecting traffic
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
100
Point-to-Point Markets
Trend
150
Markets over 5000km
200
250
300
350
400
450
500
Seats per Departure
Final Words
•
•
•
•
•
•
Matching game in nonstop markets is tough
Airlines prefer to start new routes
Connections needed to support nonstops
New routes are started to new hubs
Secondary hubs bypass early hubs
Early gateway hubs grow first, then stagnate
William Swan:
Data Troll
Story Teller
Economist