Biography for William Swan

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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 MITs Flight
Transportation Lab. Education Masters,
Engineers Degree, and Ph. D. at MIT. Bachelor
of Science in Aeronautical Engineering at
Princeton. (bill.swan_at_cyberswans.com)

• Scott Adams

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Airline Route DevelopmentsThe Unexpected
Bill Swan, Chief Economist, Boeing Marketing
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Airline Route Networks Change Over
TimeOutline of Discussion
I. The History of Route Developments Similar
patterns from all regions of the world II. Why
Do These Patterns Dominate? Several reasons,
which is most important? III. Implications for
Airline Strategies Historical trends could
change The burden of proof lies on explaining why
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I. Growth is Served by More Airplanes, Not
Bigger
Jet Schedules Show Decreasing Seat Counts
Data from August schedules
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Average Capacities Are Static Or DownGrowth is
similar for all regions
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Forecasters in 1983 Had a Really Hard Time
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Forecasters in 1990 Were Still Confused
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What We Missed New Routes
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Air Travel Growth Has Been Met By Increased
Frequencies and Non-Stops
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Seat Count is -4 of World ASK Growth
Smaller Airplanes - 4
Longer Ranges 13
New
Markets
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Added
Frequency
50
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Growth Patterns the Same at Closer DetailSimilar
patterns all over the world
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Big Routes Do Not Mean Big Airplanes
All Airport Pairs under 5000km and over 1000
seats/day
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Size in 1990 Not a Forecast for Size in 2000
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Small Airplanes Not on New Routes
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Big Airports Do Not Mean Big Airplanes
Top 12 Markets in 12 World Regions
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Fast Growth Does not Mean Big Airplanes
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II. Why Does Growth Add Frequency?Many expect
more demand to lead to bigger airplanes

• Deregulation causes one-time move to smaller
  airplanes.
• Competition drives airlines to more routes and
  frequencies.
• Economic savings of larger airplanes diminish
  with size
• For new airplanes of similar missions.
• Cost savings come from avoiding intermediate
  stops.
• Connecting passengers pay a time and cost
  penalty.
• Natural network development.
• Route networks move from skeletal to
  highly-connected.
• Travelers priorities change as economies get
  richer.
• Higher value for timely services, less emphasis
  on lowest cost.

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d. Networks Develop from Skeletal to
ConnectedHigh 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

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Skeletal Networks Develop Links to Secondary Hubs
Early Skeletal Network
Later Development bypasses Early Hubs
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Consolidation TheoryA Story that Sounds Good

• Large markets will need larger airplanes
• Industry consolidation increases this trend
• Alliances increase this trend
• This trend is happening

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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

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Route Development DataMeasures What Really
Happens

• Compare top 100 markets from Aug 1993
• Top 100 by seat departures
• Growth to Aug 2003
• Data from published jet schedules

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Largest Routes are Not Growing as bypass flying
diverts traffic
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Large Long Routes are Not Growing as bypass
flying diverts traffic
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Very Largest Long Routes are Not Growing as
bypass flying diverts traffic
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JFK Gateway Hub Stagnant for 30 Years
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JFK Gateway Hub Airplane Size Is Declining
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Competition Rising in Long-Haul Flows
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Networks Develop Beyond Early Airports

• Decline of Long-Haul Gateway Hubs 1990-2000

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Congestion Has Not Slowed Route
DevelopmentsCongestion is not driving seats per
departure up
Seat Counts at Top 5 Airports Show Little
Congestion
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Congestion Solutions From HistoryCongestion
has been a cost, not a constraint

• Solutions favored by airports
• Redefining measurement of capacity movements
• Technical improvements to raise capacity
• Added runways
• Building replacement airport
• Solutions provided by the airline market
• Using un-congested times of day
• By-passing congested gateways with new nonstop
  markets
• Building frequencies and connections at secondary
  hubs
• Using secondary airports at congested cities
• Solutions beginning to be used
• Reducing smaller, propeller aircraft movements
• Moving small, short-haul jet movements to larger
  aircraft

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Congestion Affects Short Small Flights
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Chicago Airplane Sizes Do Not Show Congestion
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Congestion is Not Driving 747 Shares UP
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Implications of History for Airlines Route
strategy should respect history

• Plan for growth
• 70-100 of it in added frequencies
• Plan for flexibility
• Long-term commitments should not hang on one
  specific future
• Plan to have more routes
• Growth will include new nonstop markets
• Plan to have more frequencies
• Growth will include more flights at more times of
  day
• Plan to face competition
• Competitors will by-pass your hub
• Plan to discuss history
• Leaders may imagine growth patterns different
  from history

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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 localsaving 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

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Half of Travel is in Connecting Markets
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Half the Trips are Connecting
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Connecting Share of Loads Averages about 50
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Long-Haul Flights are from Hubs, and carry mostly
connecting traffic
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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

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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

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Regional and Gateway Hubs in US
JFK
ORD
SFO
DEN
LAX
ATL
DFW
MIA
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Secondary Hubs in US
SEA
MSP
DTW
JFK
PIT
ORD
SLC
SFO
EWR
DEN
CVG
STL
LAX
ATL
PHX
DFW
IAH
MIA
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Why Secondary Hubs?Airlines Hate Competition

• Avoid head-to-head whenever possible
• Preferred carrier wins big
• Gets first choice of premium fare demand
• Gets full loads during off peaks
• Leaves 2nd choice carrier low yield, high peaking
• Result Lots of new routes

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Minot, N. D., USA, is served over one Hub
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Minot Feeds to Minneapolis Hub
MOT
MSP
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1800 Bank Gives Minot 38 DestinationsInbound
Bank Outbound Bank
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Minot Connects to the World
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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

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Hubs Build Loads First, then Frequency
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Hubs Give Competitive Advantages

• Less peaking of demands, as variations in
  different markets average out
• Dominate feeder legs
• Connect loads allow dominant frequency
• Connect loads avoid small, expensive airplanes
• Feeder cities can be owned
• Dominant airline will get 15 market share
  advantage
• Dominant airline can control sales channels
• Control of feeder cities makes airline attractive
  to alliances

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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

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Hubs Develop Pricing Mixes

• Higher fares in captive feeder 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
• It pays to discount and fill
• Unless you discount your own high-fare markets

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Hubs Win

• The dominant form of airline networks is hubs and
  connections
• This is because networks are thin
• Meaning only a few, larger city pairs are nonstop
• As networks grow, secondary hubs develop
• Competing with early hubs
• Hubs dominate because they create good travel
• Save time over un-coordinated connections
• Avoid the use of small, expensive airplane sizes

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Industry Growth is Small Markets

• Virtuous Circle
• Better services More Value
• Faster connections (add 15 demand for online)
• Fewer Stops (add 15 for each lost stop)
• Higher frequencies (add 15 for full-day
  schedule)
• Lower Costs Lower Prices
• Higher traffic volumes mean lower costs
• Competitive choices eliminate monopoly pricing
• New small markets get new services
• Smaller towns, secondary city airports
• Grow network from below

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Why Hubs Work

• Revenue Benefits for Hubbing
• Spring 2005 Research
• Working Paper

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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

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Hub Cost Carriers (HCCs) FareTrend is Linear
with DistanceO-D Markets Without Low-Cost (LCC)
Competition
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Low-Cost Carriers (LCCs) Faresare Linear With
Distance
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Hub Cost Carriers (HCCs) FaresMatch Low-Cost
(LCC) Competition
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HCC Fares Decline with Market Size
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LCC Fares Decline with Market Size
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Fares Decline with Market Size
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HCC Fares are Slightly Higher Than LCC Fares,
adjusted for Market Size
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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

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Hubs Emphasize Smaller Markets
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LCCs Share of Small Markets is 5Share of Larger
Nonstop Markets is 25
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HCCs Raise Average FareBy Emphasizing Connecting
Markets

• Average Fare for All Passengers 146
• Average Fare for HCC Passengers 166
• Average Fare for LCC Passengers 102

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HCC Revenues are 1/3 Small MarketsLCC Revenues
are 10 Small Markets
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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

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Economics of Small Markets

• Half of world-wide loads are connecting
• Small cities have small markets
• Small Markets pay more
• Value is there
• Small cities have lower living costs
• Lower housing costs
• Higher air travel costs
• Air Travel connects small cities to trade

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Fares are Linear With Distance

• Average Fare 153 0.043 Dist
• R-square 0.13
• All US domestic markets with valid data
• Excluding Hawaii
• Mix of HCC and LCC markets
• 18,000 data points (Airport Pair O-Ds)

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Fares are Higher for Small Markets(Includes both
Small and LCC Presence Effects)

• For Pax lt 10/day
• Fare 117 0.046 Distance
• 257 data points R-square 0.42
• For 10/day lt Pax lt 100/day
• Fare 106 0.037 Distance
• 758 data points R-square 0.37
• For Pax gt 100/day
• Fare 98 0.035 Distance
• 671 data points R-square 0.34

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HCC Fares are Higher for Small Markets

• For Pax lt 10/day
• Fare 127 0.042 Distance
• R-square 0.24
• For 10/day lt Pax lt 100/day
• Fare 110 0.036 Distance
• R-square 0.30
• For Pax gt 100/day
• Fare 115 0.031 Distance
• R-square 0.22

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LCC Fares are Higher for Small Markets

• For Pax lt 10/day
• Fare 111 0.0442 Distance
• R-square 0.33
• For 10/day lt Pax lt 100/day
• Fare 100 0.034 Distance
• R-square 0.31
• For Pax gt 100/day
• Fare 83 0.032 Distance
• R-square 0.38

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LCCs Price Close to HCCs in Very Small Markets

• Pax lt 10/day
• HCC fare 127 0.042 Distance
• LCC fare 111 0.044 Distance

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LCCs Price Connections Close to HCCs

• 10/day lt Pax lt 100/day
• HCC fare 100 0.036 Distance
• LCC fare 100 0.034 Distance

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LCCs Fares In Nonstop Markets are LowHCC fares
are a mix of all-HCC and with-LCC Markets

• Pax gt 100/day
• HCC fare 115 0.031 Distance
• LCC fare 83 0.032 Distance

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Full Model Includes 3-4 Variables

• Fare 102 0.040 Distance
• (R2 0.36)
• Fare 131 0.038 Distance 6.4 Ln(Pax)
• (R2 0.36)
• Fare 153 0.037 Distance 6.9 Ln(Pax)
• - 23 if LCC presence (R2 0.48)
• Fare 151 0.037 Distance 7.0 Ln(Pax)
• - 20 if LCC presence 26 if HCC only
  (R2 0.48)

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William Swan
Data Troll Story Teller Economist