biography for william swan retired chief economist for boeing commercial aircraft 1996-2005 previous...

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])

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mailto:[email protected]

Airline Route DevelopmentsThe Unexpected

Bill Swan, Chief Economist, Boeing Marketing

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1970 1975 1980 1985 1990 1995 2000

AS

K i

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World ASK Growth

Growth Fit with 1% Annual Reduction

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 changeThe burden of proof lies on explaining why

I. Growth is Served by More Airplanes, Not Bigger

Jet Schedules Show Decreasing Seat CountsJet Schedules Show Decreasing Seat Counts

Data from August schedules

Year Seat Count (Average)

ASKs (100%=1985)

1985 192 100% 1990 195 138% 1995 194 174% 2000 187 225%

Year Seat Count (Average)

ASKs (100%=1985)

1985 192 100% 1990 195 138% 1995 194 174% 2000 187 225%

Average Capacities Are Static Or DownGrowth is similar for all regions

Airline 1990 2000 1900-2000 Domicile Seat Count Seat Count ASK growth

WORLD 195 187 163% China, Hong Kong 212 205 353% Southeast Asia 281 293 226% Europe 201 191 200% Oceania 215 212 185% Central America 150 135 185% Japan & Korea 290 271 182% Middle East 235 228 167% Africa 200 215 163% Southwest Asia 218 191 162% South America 155 136 158% North America 159 145 132% Russia Region 153 150 113%

Airline 1990 2000 1900-2000 Domicile Seat Count Seat Count ASK growth

WORLD 195 187 163% China, Hong Kong 212 205 353% Southeast Asia 281 293 226% Europe 201 191 200% Oceania 215 212 185% Central America 150 135 185% Japan & Korea 290 271 182% Middle East 235 228 167% Africa 200 215 163% Southwest Asia 218 191 162% South America 155 136 158% North America 159 145 132% Russia Region 153 150 113%

Forecasters in 1983 Had a Really Hard Time

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Forecasters in 1983 Had a Hard Time

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1970 1975 1980 1985 1990 1995 2000 2005 2010

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1990 FORECAST

2004 data1990 data

Forecasters in 1990 Were Still Confused

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Nonstop Pairs (index)

Departures/Pair

What We Missed: New Routes

Air Travel Growth Has Been Met By Increased Frequencies and Non-Stops

Air Travel Growth Has Been Met By Increased Frequencies and Non-Stops

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1985 1990 1995 2000

Index 1985=100

Average Airplane Size

Air Travel

Frequencies

Non-Stop Markets

Average Stage Length

Seat Count is -4% of World ASK Growth

New Markets

41%Added

Frequency 50%

Longer Ranges 13%

Smaller Airplanes - 4%

Growth Patterns the Same at Closer DetailSimilar patterns all over the world

SE Asia-Oceania

S America-N America

NE Asia-N America

SE Asia-SW Asia

Europe-Africa

Europe-N America

NE Asia-SE Asia

C America-N America

Europe-C America

N America regional

Europe-SW Asia

Europe-S America

Mid East regionalC America regionalS America regionalSW Asia regional

Oceania regionalSE Asia regionalEurope regionalNE Asia regional

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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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Seats/Departure in 1990, Atlantic pairs

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Size in 1990 Not a Forecast for Size in 2000

Size in 1990 Not a Forecast for Size in 2000

Small Airplanes Not on New Routes

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Distance (km)

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Atlantic Airport Pairs with Service Aug 2000 but not Aug 1995

Top 12 Markets in 12 World Regions

Big Airports Do Not Mean Big Airplanes

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Jet Departures Per Day

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Fast Growth Does not Mean Big Airplanes

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Change in City Population 1985-2000

Datatrend line

1985-2000Change Seats/

Departure

BOMFUKParis

HAVDacca

Lagos

Kirachi

DELSGN

BKKNGO

ATH CCS

a. Deregulation causes one-time move to smaller airplanes.

Competition drives airlines to more routes and frequencies.

b. Economic savings of larger airplanes diminish with size

For new airplanes of similar missions.

c. Cost savings come from avoiding intermediate stops.

Connecting passengers pay a time and cost penalty.

d. Natural network development.

Route networks move from skeletal to highly-connected.

e. Travelers’ priorities change as economies get richer.

Higher value for timely services, less emphasis on lowest cost.

II. Why Does Growth Add Frequency?Many expect more demand to lead to bigger airplanes

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

Skeletal Networks Develop Links to Secondary Hubs

Early Skeletal Network

Later Development bypasses Early Hubs

Consolidation Theory:A Story that Sounds Good

• Large markets will need larger airplanes

• Industry consolidation increases this trend

• Alliances increase this trend

• This trend is happening

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

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ASK growth Frequencygrowth

Airplane sizegrowth

World, 1993-2003

Top 100 Routes

Large Long Routes are Not Growing as bypass flying diverts traffic

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100%


Airplanesize growth

747Departures

World, 1993-2003

Top 100 Routes >5000 Km

Very Largest Long Routes are Not Growing

as bypass flying diverts traffic

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Airplanesize growth

747Departures

World, 1993-2003

Top 10 Routes >5000 Km

JFK Gateway Hub Stagnant for 30 Years

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JFK

August Jet Schedules

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:

Top 10 Airports’ Share of Departing ASKs Market Flow Share 1990 Share 2000

Asia-Europe 88% 70% Trans Pacific 80% 69% Atlantic 54% 49%

Top 10 Airports’ Share of Departing ASKs Market Flow Share 1990 Share 2000

Asia-Europe 88% 70% Trans Pacific 80% 69% Atlantic 54% 49%

Congestion Has Not Slowed Route DevelopmentsCongestion is not driving seats per departure up

1990 2000 World Region Seats/Dep Seats/Dep Japan & Korea 281 265 Middle East 226 213 Southeast Asia 227 241 Southwest Asia 219 184 China, Hong Kong 209 217 Oceania 173 176 Central America 173 144 Africa 169 169 Europe 168 168 North America 157 145 South America 154 145 Russia Region 147 139 All 60 Airports 180 176

1990 2000 World Region Seats/Dep Seats/Dep Japan & Korea 281 265 Middle East 226 213 Southeast Asia 227 241 Southwest Asia 219 184 China, Hong Kong 209 217 Oceania 173 176 Central America 173 144 Africa 169 169 Europe 168 168 North America 157 145 South America 154 145 Russia Region 147 139 All 60 Airports 180 176

Seat Counts at Top 5 Airports Show Little Congestion

Congestion: Solutions From HistoryCongestion has been a cost, not a constraint

Solutions favored by airports:1. Redefining measurement of capacity movements

2. Technical improvements to raise capacity

3. Added runways

4. Building replacement airport

Solutions provided by the airline market:5. Using un-congested times of day

6. By-passing congested gateways with new nonstop markets

7. Building frequencies and connections at secondary hubs

8. Using secondary airports at congested cities

Solutions beginning to be used:9. Reducing smaller, propeller aircraft movements

10. Moving small, short-haul jet movements to larger aircraft

Congestion Affects Short & Small Flights

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717 737 757 767/777 747Airplane Size Category (world fleet, all manufacturers)

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1990 Departures2000 Departures

Chicago Airplane Sizes Do Not Show Congestion

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Congestion is Not Driving 747 Shares UP

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PEK

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AMS

CDG

FRA

LGW

LAX

SFO

ORD

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

Implications of History for Airlines Route strategy should respect history

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

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Passengers per Day One Way

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Nonstop

Half of Travel is in Connecting Markets

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O&D Passengers per Day

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Connecting Markets

Nonstop Markets

Lots of O&D Connections

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4-leg connect

Double Connect

1-connect

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Half the Trips are Connecting

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AS

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3+legs

2-legs

Nonstops

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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Seats per Departure

Loca

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Trend

Markets over 5000km

Point-to-Point Markets

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

ORD

ATL

DFW

DEN

JFK

LAX

MIA

SFO

Regional and Gateway Hubs in US

ORD

ATL

DFW

DEN

JFK

LAX

MIA

SFO

Secondary Hubs in US

STL

SLC

CVG

PHX

IAH

MSP

DTW PITEWR

SEA

Minot, N. D., USA, is served over one Hub

airport Destination Dist (km) Passengers fareMSP MINNEAPOLIS/ST. PAUL-INTL 724 23.9 191$ PHX PHOENIX, ARIZONA, USA-INTL 1879 8.7 215$ LAS LAS VEGAS, NEVADA, USA-MCCARRA 1771 5.6 213$ DFW DALLAS/FT. WORTH, TEXA-INTL 1747 5.0 241$ SEA SEATTLE/TACOMA, WASHIN-SEA/TAC 1574 4.9 209$ DCA WASHINGTON, DC, USA-NATIONAL 2211 4.8 309$ DEN DENVER, COLORADO, USA-INTL 974 4.7 197$ MCO ORLANDO, FLORIDA, USA-INTL 2797 4.5 226$ LAX LOS ANGELES, CALIFORNI-INTL 2139 3.7 220$ SAT SAN ANTONIO, TEXAS, USA 2097 3.6 331$ ANC ANCHORAGE, ALASKA, USA 3360 3.0 245$ ORD CHICAGO, ILLINOIS, USA-O'HARE 1263 2.9 225$ ATL ATLANTA, GEORGIA, USA 2152 2.7 255$ SFO SAN FRANCISCO, CALIFORNIA, USA 2082 2.7 219$ EKO ELKO, NEVADA, USA 1418 2.6 48$ IAH HOUSTON, TEXAS, USA-INTERCONT 2097 2.6 290$ LGA NEW YORK LA GUARDIA 2323 2.6 228$

Rest of World (117 more Cities) 2022 91 240$ TOTAL/avg 1818 177 230$

Minot Feeds to Minneapolis Hub

MOT

MSP

18:00 Bank Gives Minot 38 DestinationsInbound Bank Outbound Bank

Origin Depart Hub Origin Depart Hub Hub Arrive Destin' Hub Arrive Destin'city time time city time time ==> time time city time time cityONT 1200 1727 DLH 1655 1748 1848 2116 MBSBOS 1505 1728 SAN 1210 1748 1849 2136 CMHSNA 1200 1728 IND 1604 1749 1850 2227 HPNPSP 1210 1729 TUL 1550 1750 1850 2130 AZOPDX 1210 1729 DTW 1700 1753 ==> 1835 2030 MEM 1850 2130 AZOMSO 1355 1730 GRB 1641 1755 ==> 1836 1932 FAR 1850 2215 TYSCWA 1630 1731 MKE 1635 1756 ==> 1837 2159 IAD 1850 900 LGWGFK 1620 1731 SJC 1215 1756 ==> 1838 2159 RDU 1851 2142 DTWRST 1650 1732 RAP 1530 1757 ==> 1839 2209 PVD 1852 2128 FNTSMF 1205 1732 DTW 1705 1759 ==> 1839 2214 GSO 1853 2217 BWIORD 1600 1734 DSM 1650 1759 ==> 1840 2207 BDL 1854 2246 BOSDFW 1510 1735 MSN 1645 1800 ==> 1841 2108 GRR 1855 2255 ORFYEG 1355 1735 MOT 1635 1800 ==> 1842 2139 BUF 1855 2008 MLIYYC 1357 1735 SFO 1220 1800 ==> 1843 2104 OKC 1855 2124 LANABQ 1405 1739 BOI 1415 1804 ==> 1844 2210 ATL 1856 2126 DFWLNK 1615 1740 GEG 1312 1804 ==> 1845 2159 ROC 1857 2158 YYZDCA 1559 1741 ATL 1620 1805 ==> 1845 2022 SBN 1858 2007 GRBSTL 1600 1742 MDW 1635 1809 ==> 1845 2134 DAY 1859 2002 OMALAX 1215 1744 CVG 1655 1809 ==> 1846 2208 CLT 1900 2200 PITYWG 1618 1744 CWA 1715 1815 ==> 1847 2208 DCA 1900 2027 ORDBIS 1630 1747 1847 2253 TPA 1901 2030 MCI

Minot Connects to the World

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 Build Loads First, then Frequency

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Trip

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Good Balance

Add Frequency

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

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

• 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

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

Why Hubs Work

Revenue Benefits for Hubbing

Spring 2005 Research

Working Paper

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

Hub Cost Carriers’ (HCCs) FareTrend is Linear with Distance

O-D Markets Without Low-Cost (LCC) Competition

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Linear (HCCs Alone)

US Domestic 2q04 Data

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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HCCs AloneHCCs with LCCsLCC FaresLinear (HCCs Alone)Linear (HCCs with LCCs)Linear (LCC Fares)


HCC Fares Decline with Market Size

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Market Size, Log Scale

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Connecting

HCC fare Trend= $196 - 2.26 * Ln(Pax)

LCC Fares Decline with Market Size

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Connecting

LCC Trend = $178 + 11 * Ln(Pax)

Fares Decline with Market Size

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Connecting

HCC Fares are Slightly Higher Than LCC Fares, adjusted for Market Size

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HCCs without LCCsHCCs with LCCsLCC fares

Connecting

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

Hubs Emphasize Smaller Markets

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O-D City Pair Market Size (log scale)

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LCCs Share of Small Markets is 5%Share 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

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

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Connections

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

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

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)

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

For Pax < 10/dayFare = $117 + 0.046 * Distance

257 data points; R-square = 0.42For 10/day < Pax < 100/day

Fare = $106 + 0.037 * Distance 758 data points; R-square = 0.37

For Pax > 100/dayFare = $98 + 0.035 * Distance

671 data points; R-square = 0.34

HCC Fares are Higher for Small Markets


R-square = 0.24For 10/day < Pax < 100/day

Fare = $110 + 0.036 * Distance R-square = 0.30

For Pax > 100/dayFare = $115 + 0.031 * Distance

R-square = 0.22

LCC Fares are Higher for Small Markets


R-square = 0.33For 10/day < Pax < 100/day

Fare = $100 + 0.034 * Distance R-square = 0.31

For Pax > 100/dayFare = $83+ 0.032 * Distance

R-square = 0.38

LCCs Price Close to HCCs in Very Small Markets

Pax < 10/day

HCC fare = $127 + 0.042 * Distance

LCC fare = $111 + 0.044 * Distance

LCCs Price Connections Close to HCCs

10/day < Pax < 100/day



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

Pax > 100/day



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)


- $23 if LCC presence (R2 = 0.48)


- $20 if LCC presence + $26 if HCC only (R2 = 0.48)

William Swan:

Data Troll

Story Teller

Economist

Minot, N. Dakota, USA, is served over one Hub

airport Destination Dist (km) Passengers fareMSP MINNEAPOLIS/ST. PAUL-INTL 724 23.9 191$ PHX PHOENIX, ARIZONA, USA-INTL 1879 8.7 215$ LAS LAS VEGAS, NEVADA, USA-MCCARRA 1771 5.6 213$ DFW DALLAS/FT. WORTH, TEXA-INTL 1747 5.0 241$ SEA SEATTLE/TACOMA, WASHIN-SEA/TAC 1574 4.9 209$ DCA WASHINGTON, DC, USA-NATIONAL 2211 4.8 309$ DEN DENVER, COLORADO, USA-INTL 974 4.7 197$ MCO ORLANDO, FLORIDA, USA-INTL 2797 4.5 226$ LAX LOS ANGELES, CALIFORNI-INTL 2139 3.7 220$ SAT SAN ANTONIO, TEXAS, USA 2097 3.6 331$ ANC ANCHORAGE, ALASKA, USA 3360 3.0 245$ ORD CHICAGO, ILLINOIS, USA-O'HARE 1263 2.9 225$ ATL ATLANTA, GEORGIA, USA 2152 2.7 255$ SFO SAN FRANCISCO, CALIFORNIA, USA 2082 2.7 219$ EKO ELKO, NEVADA, USA 1418 2.6 48$ IAH HOUSTON, TEXAS, USA-INTERCONT 2097 2.6 290$ LGA NEW YORK LA GUARDIA 2323 2.6 228$

Rest of World (117 more Cities) 2022 91 240$ TOTAL/avg 1818 177 230$