How Airlines Compete Fighting it out in a City-Pair Market William M. Swan Chief Economist Seabury Airline Planning Group Nov 200 Papers:

Slides:



Advertisements
Similar presentations
Biography for William Swan Chief Economist, Seabury-Airline Planning Group. Visiting Professor, Cranfield University. Retired Chief Economist for Boeing.
Advertisements

Modeling Sell-up in PODS enhancements to existing sell-up algorithms, etc. Hopperstad March 00.
Homework 4 (Airline Cost Analysis-United) Saba Neyshabouri.
Operations management Session 17: Introduction to Revenue Management and Decision Trees.
Tourism Economics TRM 490 Dr. Zongqing Zhou Chapter 5: Airline Economics.
Simultaneous games with continuous strategies Suppose two players have to choose a number between 0 and 100. They can choose any real number (i.e. any.
Managing Capacity and Demand Copyright © 2006 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin.
Managing Capacity and Demand
OPSM 405 Service Management
Impact and Model of Low-Cost Carriers
Airline Revenue Management
Slide 1 Capacity Planning and Pricing Against a Low-Cost Competitor Appendix 13A Piedmont Airlines and People Express present a case study of the reaction.
JetBlue Cost and Productivity Analysis Greg Koch HW for OR 750.
Biography for William Swan Retired Chief Economist for Boeing Commercial Aircraft Previous to Boeing, worked at American Airlines in Operations.
State and Local Public Finance Spring 2013, Professor Yinger Lecture 11 User Fees (=Public Prices)
Price Discrimination. Price Discrimination Defined ▫Single-price monopolist  A monopolist who charges everyone the same price.  Not all monopolists.
Copyright © 2004 South-Western Monopoly vs. Competition While a competitive firm is a price taker, a monopoly firm is a price maker. A firm is considered.
OLIGOPOLY Managerial Economics Lecturer: Jack Wu.
Economics Chapter 7 Market Structures
The Four Conditions for Perfect Competition
Economics 2010 Lecture 13’ Monopoly pricing Monopoly  Price discrimination  Price discrimination and total revenue  Price discrimination and consumer.
2 of 29 © 2014 Pearson Education, Inc. 3 of 29 © 2014 Pearson Education, Inc. 8 Short-Run Costs and Output Decisions CHAPTER OUTLINE Costs in the Short.
Biography for William Swan Chief Economist, Seabury-Airline Planning Group. Visiting Professor, Cranfield University. Retired Chief Economist for Boeing.
How Firms Make Decisions: Profit Maximization
Defining Revenue Management as a Game Gert Hartmans Agifors - Berlin 2002.
ISM 270 Service Engineering and Management Lecture 7: Forecasting and Managing Service Capacity.
Changing Markets, Changing Times Peter Morris Peter Morris, Chief Economist, Ascend
PART II The Market System: Choices Made by Households and Firms © 2012 Pearson Education Prepared by: Fernando Quijano & Shelly Tefft CASE FAIR OSTER.
Planning Demand and Supply in a Supply Chain
McGraw-Hill/Irwin © 2005 The McGraw-Hill Companies, Inc. All rights reserved. 7-1 Defining Competitiveness Chapter 7.
Economics 2010 Lecture 12 Perfect Competition. Competition  Perfect Competition  Firms Choices in Perfect Competition  The Firm’s Short-Run Decision.
Principles of Economics Ohio Wesleyan University Goran Skosples Firms in Competitive Markets 9. Firms in Competitive Markets.
AVIATION ECONOMICS CHAPTER 3 PASSENGER MOVEMENT: THE SIGNIFICANCE OF PASSENGER LOAD FACTORS & STRATEGY FOR PASSENGER MARKETING.
Biography for William Swan Retired Chief Economist for Boeing Commercial Aircraft Previous to Boeing, worked at American Airlines in Operations.
Airline Evolution William M Swan Chief Economist Boeing Commercial Airplanes, Marketing; Retired Spring 2007.
Biography for William Swan Chief Economist, Seabury-Airline Planning Group. Visiting Professor, Cranfield University. Retired Chief Economist for Boeing.
In this chapter, look for the answers to these questions:
Managing Supply and Demand. Strategies for Matching Supply and Demand for Services DEMAND STRATEGIES Partitioning demand Developing complementary services.
RM Coordination and Bid Price Sharing in Airline Alliances: PODS Simulation Results Peter Belobaba Jeremy Darot Massachusetts Institute of Technology AGIFORS.
Ch10. The Basic of Capital Budgeting Goal: To understand the advantage and disadvantage in different investment analyzing tools Tool: - Net Present Value.
Slide 1Copyright © 2004 McGraw-Hill Ryerson Limited Chapter 12 Monopoly.
Pricing and Strategies
Route and Network Planning
Value of a Nonstop William Swan Chief Economist Boeing Commercial Airplanes Marketing April 2004.
Chapter 22: The Competitive Firm Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin 13e.
McGraw-Hill/Irwin © 2005 The McGraw-Hill Companies, Inc. All rights reserved. 7-1 Defining Competitiveness Chapter 7.
Perfect Competition.
Marketing & Sales – 3rd Hour
21-1 The Costs of Production  Before anyone can consume to satisfy wants and needs, goods and services must be produced.  Producers are profit-seeking,
1 of 34 PART II The Market System: Choices Made by Households and Firms © 2012 Pearson Education 8 Short-Run Costs and Output Decisions CHAPTER OUTLINE.
Misconception: Price is the same thing as cost. What is a pricing strategy?
Biography for William Swan Chief Economist, Seabury-Airline Planning Group. AGIFORS Senior Fellow. ATRG Senior Fellow. Retired Chief Economist for Boeing.
Money and Banking Lecture 11. Review of the Previous Lecture Application of Present Value Concept Internal Rate of Return Bond Pricing Real Vs Nominal.
Misconception: Price is the same thing as cost. What is a pricing strategy?
Biography for William Swan Currently the “Cheap” Economist for Boeing Commercial Aircraft. Previous to Boeing, worked at American Airlines in Operations.
Biography for William Swan Chief Economist, Seabury-Airline Planning Group. AGIFORS Senior Fellow. ATRG Senior Fellow. Retired Chief Economist for Boeing.
PRICING SPORTS AND ENTERTAINMENT MARKETING. PRICING IN SER INDUSTRIES Pricing in SER is largely dependent on consumer perception and demand Taylor Swift.
© 2009 Pearson Education, Inc. Publishing as Prentice Hall Principles of Economics 9e by Case, Fair and Oster 8 PART II THE MARKET SYSTEM Choices Made.
Why Hubs Work Revenue Benefits for Hubbing Spring 2005 Research Working Paper.
Biography for William Swan
Capacity Planning and Pricing Against a Low-Cost Competitor Appendix 13A Piedmont Airlines and People Express present a case study of the reaction to.
microeconomics spring 2016 the analytics of
Price Discrimination.
Chapter 14 Perfectly competitive Market
Tell me when you want to stay,
Chapter 5: Business-Level Strategy
Perfect Competition Long Run Overheads.
Chapter 9: Setting the list or quoted price
Biography for William Swan
Presentation transcript:

How Airlines Compete Fighting it out in a City-Pair Market William M. Swan Chief Economist Seabury Airline Planning Group Nov 200 Papers: Contact:

A Stylized Game With Realistic Numbers 1.The Simplest Case, Airlines A & Z 2.Case 2: Airline A is Preferred 3.Peak and Off-peak days 4.Full Spill model version 5.Airline A is “Sometimes” Preferred 6.Time-of-day Games

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

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

The Simplest Case: Airlines A & Z Identical airlines in simplest case Two passenger types: $100, 144 passengers demand $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

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?

Variations in Demand Change Answer Consider 3 seasons, matched fares case 1.Off peak at 2/3 of standard demand (120) 2.Standard demand of 180 total, as before 3.Peak day at 4/3 of standard demand (240) 4.Each season 1/3 of year 5.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

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-factor A =0.7; C-factor AZ =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

Airline A is “Sometimes” Preferred 2/3 of customers prefer airline A 1/3 of customers prefer airline Z Full spill case 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

Cases of Increasing Realism Airline A$/seat Load Factor Avg. Fare Simple$172100%$172 3-seasons$172100%$172 Spilled$16189%$181 2/3 Pref.$13385%$157 Airline Z$/seat Load Factor Avg. Fare Simple$ 8080%$100 3-seasons$ 6767%$100 Spilled$ 6868%$100 2/3 Pref.$ 9773%$133 Total A & Z$/seatLoad FactorAvg. Fare Simple$12690%$140 3-seasons$11983%$143 Spilled$11579%$146 2/3 Pref.$11579%$146

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

Simple Time-of-Day Model Total Demand MorningMiddayEvening Only17.5% AM15% PM15% any17.5%

Both A & Z in Morning A=36F, 64D Z=0F, 80D Full Fare Morn -ing Mid- Day Even -ing Only25% AM10% PM10% All5% Dis- count Morn -ing Mid- Day Even -ing Only10% AM20% PM20% All30% RAS=$172 RAS=$ 80

Z “Hides” in Evening A=18.9F, 81.1D Z=17.1F, 62.9D Full Fare Morn -ing Mid- Day Even -ing Only25% AM10% PM10% All5% Dis- count Morn -ing Mid- Day Even -ing Only10% AM20% PM20% All30% RAS=$138RAS=$114

A Pursues to Midday A=22.5F, 77.5D Z=13.5F, 66.5D Full Fare Morn -ing Mid- Day Even -ing Only25% AM10% PM10% All5% Dis- count Morn -ing Mid- Day Even -ing Only10% AM20% PM20% All30% RAS=$145RAS=$107

Demand Up 50%, A uses 200 seats A=33.7F, 166.3D Z=20.3F, 49.7D Full Fare Morn -ing Mid- Day Even -ing Only25% AM10% PM10% All5% Dis- count Morn -ing Mid- Day Even -ing Only10% AM20% PM20% All30% RAS=$134, CAS=$95RAS=$111; CAS=$126

Larger Airplanes are Cheaper Per Seat

Demand Up 50%, Z adds Morning A=27F, 73D Z=27F, 143D Full Fare Morn -ing Mid- Day Even -ing Only25% AM10% PM10% All5% Dis- count Morn -ing Mid- Day Even -ing Only10% AM20% PM20% All30% RAS=$154, CAS=$126RAS=$112; CAS=$126

Demand Up 50%, A adds Morning A=40.5F, 157.4D Z=13.5F, 58.6D Full Fare Morn -ing Mid- Day Even -ing Only25% AM10% PM10% All5% Dis- count Morn -ing Mid- Day Even -ing Only10% AM20% PM20% All30% RAS=$139, CAS=$126RAS=$ 99; CAS=$126

A adds Evening Instead A=54F, 146D Z=0F, 70D Full Fare Morn -ing Mid- Day Even -ing Only25% AM10% PM10% All5% Dis- count Morn -ing Mid- Day Even -ing Only10% AM20% PM20% All30% RAS=$154, CAS=$126RAS=$ 70; CAS=$126

case A pax F A Pax D A Avg Fare A Rev/ Seat B pax F B Pax D B Avg Fare B Rev/ Seat A in morning B in morning 3664$ $100$80 A in morning B in evening 1981$ $142$112 A in midday B in evening 2278$ $133$105 A 200 in midday B in evening 35165$ $158$114 A in midday B morn & eve 2872$ $133$114 A morn & mid B in evening 42156$142$ $138$102 A morn & eve B in evening 56144$ $100$72

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 1.Spill 2.Partial preference 3.Time-of-day distribution