Biography for William Swan

1. 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

2. How Airlines CompeteFighting 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

3. A Stylized GameWith Realistic Numbers • 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

4. 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

5. 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

6. 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

7. 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

8. 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”

9. 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

10. 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

11. 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?

12. Variations in Demand Modify AnswerMatters are Worse for Z • Consider 3 seasons, matched fares case • 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

13. 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

14. 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

15. 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

16. Simple Time-of-Day Model

17. Both A & Z in MorningA=36F, 64DZ=0F, 80D RAS=$ 80 RAS=$172

18. Z “Hides” in EveningA=18.9F, 81.1DZ=17.1F, 62.9D RAS=$138 RAS=$114

19. A Pursues to MiddayA=22.5F, 77.5DZ=13.5F, 66.5D RAS=$145 RAS=$107

20. Demand Up 50%, A uses 200 seatsA=33.7F, 166.3DZ=20.3F, 49.7D RAS=$134, CAS=$95 RAS=$111; CAS=$126

21. Larger Airplanes are Cheaper Per Seat

22. Demand Up 50%, Z adds MorningA=27F, 73DZ=27F, 143D RAS=$154, CAS=$126 RAS=$112; CAS=$126

23. Demand Up 50%, A adds MorningA=40.5F, 157.4DZ=13.5F, 58.6D RAS=$139, CAS=$126 RAS=$ 99; CAS=$126

24. A adds Evening InsteadA=54F, 146DZ=0F, 70D RAS=$154, CAS=$126 RAS=$ 70; CAS=$126

25. 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 – but not much change • Partial preference – some people prefer the other • Time-of-day distribution – good time/bad airline