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Biography for William Swan PowerPoint PPT Presentation


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 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


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Airline Competition

William M. Swan

Chief Economist

Seabury Airline Planning Group

Nov 2005


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A Stylized GameWith Realistic Numbers

  • The Simplest Case, Airlines A & Z

  • Preferred Airline matches on price

  • Time-of-day Games


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The Simplest Case: Airlines A & Z

  • Identical airlines in simplest case

  • Two passenger types:

    • Discount @ $100, 144 passengers demand

    • 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


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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


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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”


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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


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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


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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


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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


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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


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Simple Time-of-Day Model


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Both A & Z in MorningA=36F, 64DZ=0F, 80D

RAS=$ 80

RAS=$172


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Z “Hides” in EveningA=18.9F, 81.1DZ=17.1F, 62.9D

RAS=$138

RAS=$114


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A Pursues to MiddayA=22.5F, 77.5DZ=13.5F, 66.5D

RAS=$145

RAS=$107


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Competition involves 3 distributions

  • Demand varies by day of week

    2ND AIRLINE GETS ALL PEAKS/VALLEYS

  • Demand has mix of prices

    1ST AIRLINE GETS ALL OF HIGH FARES

  • Demand has time of day requirements

    2ND AIRLINE AVOIDS 1ST AIRLINE’S TIMES


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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

    • Spill

    • Partial preference

    • Time-of-day distribution


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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


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Long-Haul Competition DeclinesBased on Airport-Pairs


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Regional Competition is Flat


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“Reduced” Competition in Pairs Comes with Many New Pairs


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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

41%

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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Big Airports Do Not Mean Big Airplanes

Top 12 Markets in 12 World Regions


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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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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 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


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Largest Routes are Not Growingas bypass flying diverts traffic


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JFK Gateway Hub Stagnant for 30 Years


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Competition Rising in Long-Haul FlowsThis time not pairs—but oceans


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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 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


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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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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 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

    • 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


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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

  • High Cost Carriers offer some low prices

    • Discount fares on HCCs match average LCC fares


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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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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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Long-Haul Flights are from Hubs, and carry mostly connecting traffic


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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


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William Swan:

Data Troll

Story Teller

Economist


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