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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. (bill.swan@cyberswans.com) • Scott Adams
Airline Competition William M. Swan Chief Economist Seabury Airline Planning Group Nov 2005
A Stylized GameWith Realistic Numbers • The Simplest Case, Airlines A & Z • Preferred Airline matches on price • Time-of-day Games
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
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. $80 for Z • Preferred Airline gains by matching fares • Premium share of premium traffic • Full loads, even in the off-peak • Even though discount and full-fares match Z • Practice shows few lower-quality survivors
More Observations • “Preferred wins” result drives quality matching between airlines • Result is NOT high quality • Everybody knows everybody tries to match • Therefore quality is standardized, not high • Result is arbitrary quality level
Avoiding Competition Airlines avoid head-to-head competition: Serve a different time of day Capture customers with loyalty frequent flyer programs control sales outlets cultural dominance in home market preferred for certain “style” Use a different airport
Time-of-Day Games • What if 2/3 preferred case was because Z was at a different time of day? • 1/3 of people prefer Z’s time of day • 1/3 of people prefer A’s time of day • 1/3 of people can take either, prefer Airline A’s quality (or color) • Ground rules: back to simple case • No peak, off-peak spill • Back to 100% maximum load factor • System overall at breakeven revenues and costs • Simple case for clarity of exposition • Spill issues add complication without insight • Spill will merely soften differences
Both A & Z in MorningA=36F, 64DZ=0F, 80D RAS=$ 80 RAS=$172
Z “Hides” in EveningA=18.9F, 81.1DZ=17.1F, 62.9D RAS=$138 RAS=$114
A Pursues to MiddayA=22.5F, 77.5DZ=13.5F, 66.5D RAS=$145 RAS=$107
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
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
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
Air Travel Growth Has Been Met By Increased Frequencies and Non-Stops
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 DetailSimilar patterns all over the world
Big Routes Do Not Mean Big Airplanes All Airport Pairs under 5000km and over 1000 seats/day
Big Airports Do Not Mean Big Airplanes Top 12 Markets in 12 World Regions
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
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 Growingas bypass flying diverts traffic
Competition Rising in Long-Haul FlowsThis time not pairs—but oceans
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
Long-Haul Flights are from Hubs, and carry mostly connecting traffic
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
