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. (bill.swan@cyberswans.com)
© Scott Adams
How Airlines Compete
Fighting it out in a City-Pair Market
William M. Swan
Chief Economist
Seabury Airline Planning Group
Nov 200
Papers: http://www.seaburyapg.com/company/research.html
Contact: bill.swan@cyberswans.com
A Stylized Game
With Realistic Numbers
1.
2.
3.
4.
5.
6.
The Simplest Case, Airlines A & Z
Case 2: Airline A is Preferred
Peak and Off-peak days
Full Spill model version
Airline A is “Sometimes” Preferred
Time-of-day Games
Model the Fundamentals
• Capture all relevant characteristics
–
–
–
–
–
–
Different passengers pay high and low fares
Different passengers like different times of day
Different passengers have less or more time flexibility
Airlines block space to accommodate higher fares
Demand varies from day to day
Demand that exceeds capacity spills
• to other flights, if possible
– Airlines can be preferred, one over another
– Passengers have a hierarchy of decisions
• Price; Time; Airline
– Bigger airplanes are cheaper per seat than smaller ones
Example Simple but True
• Example here as simple as we could devise
– Covers all fundamentals
– Uses simplest possible distributions
•
•
•
•
•
Time of day
Fares paid
Airline choices
Demand variations
Choice Hierarchy
– Means and Standard Deviations are realistic
• Each is a “cartoon”
– Reflects industry experience with detailed models
– Based on best practices at
• AA; UA; Boeing; MIT
• Other airlines that were Boeing customers
• University contacts
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
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
– add qualities that people value beyond cost?
Variations in Demand Modify Answer
Matters are Worse for Z
•
Consider 3 seasons, matched fares case
1.
2.
3.
4.
5.
•
Off peak at 2/3 of standard demand (120)
Standard demand of 180 total, as before
Peak day at 4/3 of standard demand (240)
Each season 1/3 of year
Same average demand, revenue, etc.
Off-peak A gets 24 full-fare, 76 discount
–
•
Z gets only 20 discount
Peak A gets 48 full-fare, 52 discount
–
–
•
Z gets 100 discount, still below break-even
Z is spilling 40 discounts, lost revenues
Overall, A at $172/seat and Z at $67
–
–
Compared to $172 & $80 in simple case
Some revenue in the market is “spilled’ – all from Airline Z
Full Spill Model Case
• Spill model captures normal full variations of seasonal demand
– Spill is airline industry standard model*
• Spill model exercised 3 times:
– Full-fare demand against A capacity
• For full-fare spill, which is zero
– Total demand against A capacity
• Spill will be sum of discount and full-fare
– Total demand against A + Z capacity
• Spill will be sum of A and Z spills
• K-cyclic = 0.36; C-factorA=0.7; C-factorAZ=0.7
• Results
– A $11/seat below 3-season case
– Z $1/seat better than 3-season case
• Qualitatively the same conclusions: A wins big; Z looses.
*See Swan, 1997
Airline A is “Sometimes” Preferred
•
•
•
•
2/3 of customers prefer airline A
1/3 of customers prefer airline Z
Full spill case (full spill model employed)
Results:
– A has 85% load; $133/seat—15% above avg.
– Z has 73% load; $97/seat—15% below avg.
• If Z is low-cost by 15%, can break even
• This could represent new-entrant case
Time-of-Day Games
• What if 2/3 preferred case was because Z was at a different
time of day?
– 1/3 of people prefer Z’s time of day
– 1/3 of people prefer A’s time of day
– 1/3 of people can take either, prefer Airline A’s quality (or color)
• Ground rules: back to simple case
– No peak, off-peak spill
– Back to 100% maximum load factor
– System overall at breakeven revenues and costs
• Simple case for clarity of exposition
– Spill issues add complication without insight
– Spill will merely soften differences
Simple Time-of-Day Model
Total Demand
Only
AM
Morning
Midday
Evening
17.5%
17.5%
17.5%
15%
PM
any
15%
17.5%
Both A & Z in Morning
Z=0F, 80D
A=36F, 64D
RAS=$ 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
A=18.9F, 81.1D
Z=17.1F, 62.9D
RAS=$138
RAS=$114
Full
Fare
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
A=22.5F, 77.5D
Z=13.5F, 66.5D
RAS=$145
RAS=$107
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%
Demand Up 50%, A uses 200 seats
A=33.7F, 166.3D
Z=20.3F, 49.7D
RAS=$134, CAS=$95
RAS=$111; CAS=$126
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%
CAS - Cost per Available Seat
$230
Larger Airplanes are Cheaper
Per Seat
$210
Too Expensive
$190
$170
$150
$130
Reasonable Range of Sizes
$110
$90
Declining Advantages to Scale
$70
$50
0
50
100
150
200
SEATS
250
300
350
400
Demand Up 50%, Z adds Morning
A=27F, 73D
RAS=$154, CAS=$126
Full
Fare
Z=27F, 143D
RAS=$112; CAS=$126
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%
Demand Up 50%, A adds Morning
A=40.5F, 157.4D
Z=13.5F, 58.6D
RAS=$139, CAS=$126
RAS=$ 99; CAS=$126
Full
Fare
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 adds Evening Instead
Z=0F, 70D
A=54F, 146D
RAS=$154, CAS=$126
Full
Fare
RAS=$ 70; CAS=$126
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%
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 – but not much change
2. Partial preference – some people prefer the other
3. Time-of-day distribution – good time/bad airline