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FLIGHT TRANSPORTATION LABORATORY REPORT R87-7 AIR TRAVEL DEMAND AND AIRLINE SEAT INVENTORY MANAGEMENT PETER PAUL BELOBABA Air Travel Demand and Airline Seat Inventory Management by Peter Paul Belobaba Flight Transportation Laboratory Massachusetts Institute of Technology May 1, 1987 ABSTRACT Many airlines practice differential pricing of fare products that share a common inventory of available seats on an aircraft. Seat inventory management is the process of limiting the number of seats made available to each fare class. The objective of both strategies is to maximize the total revenues generated by the mix of fare products sold for a flight. This dissertation first examines the evolution of airline marketing and seat in- ventory management practices. A demand segmentation model is developed to help explain current airline fare structures. A conceptual model of the consumer choice process for air travel is then presented, and extended to describe the airline reserva- tions process and the probabilistic elements that can affect seat inventory control. A survey of current airline practice in this area revealed that seat inventory control is an ad-hoc process which depends heavily on human judgement. Past work on quantitative approaches has focused on large-scale optimization models that solve simple representations of the problem. A primary objective of this research was the development of a quantitative approach based on the practical constraints faced by airlines. The Expected Marginal Seat Revenue (EMSR) model developed in this thesis is a decision framework for maximizing flight leg revenues which can be applied to multiple nested fare class inventories. It is applied to a dynamic process of booking limit revision for future flight departures, and overbooking factors as well as fare class upgrade probabilities are incorporated. Examples of EMSR model results are presented, and a critical analysis of the demand assumptions and sensitivity of the model is performed. The EMSR model was implemented as part of an automated seat inventory con- trol system at Western Airlines and tested on a sample of actual flights. Compared to flights managed by existing manual methods, flights for which fare class booking limits were set and revised automatically on the basis of the EMSR decision model carried more passengers at a lower yield, and generated higher total revenues. Acknowledgements I was extremely fortunate to have a Doctoral Committee comprised of three members who contributed substantially and in different respects to the format and contents of this dissertation. Professor Robert Simpson was involved most closely in the various research efforts that contributed to this work, guiding the empiri- cal analysis and the development of revenue maximization approaches. Professor Amedeo Odoni contributed to the quantitative sections of this thesis, helping metto explain the mathematical formulations as clearly as possible. Professor Nigel Wils~n provided much needed assistance with respect to the logic and consistency of the discussion from a systems analysis perspective, ensuring that the concepts presented can be understood by those not intimately familiar with airline operations. I am privileged to acknowledge financial support from a number of sources. I would like to thank the National Social Sciences and Humanities Research Coun- cil (NSSHRC) of Canada for the Doctoral Fellowship support. Additional support during the 1985-86 academic year was provided by the United Parcel Service Foun- dation Doctoral Fellowship, administered by the Center for Transportation Studies at M.I.T. Research funding and technical assistance was also provided by the M.I.T.- Industry Cooperative Research Program at the Flight Transportation Laboratory. As a member of the Program, Trans World Airlines furnished reservations data for empirical analysis early in this research. McDonnell-Douglas representative John Stroup assisted in the initial development of computer code for seat allocation mod- els. A research agreement between the Flight Transportation Laboratory and West- ern Airlines allowed me to develop and implement my ideas in an actual airline environment. Many thanks to Cal Rader and Dennis Fitzpatrick, who initiated what proved to be an extremely rewarding cooperative effort. Those involved in the project at Western were very helpful, and deserve special mention: Judy Whitaker, Rainer Siegert, Diane McGinty, Sherry Jackson, Lynne Graham, and John Sefton. Personal thanks go to my closest friends: Mike Meyer, for being the first to encourage me to pursue a Ph.D.; Brad Martin, for providing last-minute help with tables and figures; Carole Marks, for her genuine interest in my progress and feigned interest in my research; and Eric and Liane Schreffler, for their hospitality in Los Angeles. Contents Introduction PART ONE - The Evolution of Seat Inventory Management: Theory and Practice 1 Airline Economics and Pricing Practices 1.1 Empty Seats and Capacity-Controlled Fares .............. 1.2 Changes to the Surplus Seat Concept ...................... 1.3 Current Airline Fare Structures ..................... 2 Consumer Decisions and Air Travel Demand 2.1 Individual Consumer Choice ........................... 2.2 Airline Reservations Framework ..................... 3 The Seat Inventory Control Problem 62 3.1 Problem Definition and Context ............ 63 3.2 A Survey of Seat Inventory Control Practices ... 68 3.2.1 Organizational Issues ................ 69 3.2.2 Reservations and Decision Support Systems 71 3.2.3 Setting and Monitoring Booking Limits .. 75 PART TWO - Mathematical Models for Seat Inventory Management 80 4 An Overview of Previous Research 4.1 Distinct Versus Nested Fare Class Inventories 4.2 Seat Allocation Among Distinct Fare Classes 4.3 Evolution of the "Marginal Seat" Model ... 6 Expected Marginal Seat Revenue Model 101 5.1 A Probabilistic Approach ............. .. .. 102 . 5.2 Expected Revenues in Nested Fare Classes .. .... 107 . 5.3 Dynamic Applications of the EMSR Model . .119 5.4 Passenger No-shows and Flight Overbooking .121 5.5 Incorporating Passenger Choice Shifts .... .. .. 128 . 5.6 Examples of Model Results ............ ... 131 . 6 EMSR Model Assumptions and Sensitivity 140 6.1 Demand Inputs and Assumptions ........... 140 6.1.1 Demand Density Shapes ............ 142 6.1.2 Correlation of Fare Class Demands ..... 143 6.1.3 Booking Activity Correlation Over Time. 150 6.2 Model Sensitivity to Input Variables . ...... 153 6.3 Applications to O-D Seat Inventory Control . .. 157 PART THREE - System Development and EMSR Model Applica- tions 163 7 EMSR Model Implementation and Testing 164 7.1 System Development and Implementation Issues ........ * . .. 165 7.1.1 Airline Policies and Procedures ............. .. 165 7.1.2 Data Availability and Estimation Methods ....... 172 7.2 Automated Booking Limit System (ABLS) .......... 179 7.3 EMSR Revenue Impact Test ...................... 181 7.3.1 Test Methodology .................... .... 181 7.3.2 Revenue Impact Results and Assessment ........ ... 186 8 Conclusions for Future System Development 203 8.1 Research Findings and Contributions ........ .. ... ... .. 203 8.2 Directions for Further Research ............ .... ...... 207 References 211 List of Figures 1.1 Differential Pricing of Airline Seats: Single Flight Example ..... 15 1.2 Trip Characteristics and the Price vs. Service Trade-off .. .. .. 21 1.3 Market Demand Segmentation Model .. ... .. ... .. ... .. 25 1.4 Typology of Airline Fare Products .. .. ... .. ... .. ... .. 33 1.5 Fare Structure Relationships . ... .. ... .. ... .. ... .. 36 2.1 Consumer Choice Shift Process . .. ... .. ... .. ... .. ... 56 2.2 Cancellations and No-shows ... ... ... ... ... ... ... .. 59 3.1 Example of "Threshold Curve" Monitoring .. .. ... ... .. .. 78 4.1 Booking Limits in a Nested Reservations System .. ... ... ... 85 4.2 Network Representation of Seat Allocation Problem ... ... ... 92 5.1 Typical Probability Distributions of Requests .. ... .. ... ... 104 5.2 Optimal Seat Allocation, Two Demand Classes .. ... .. ... .. 106 5.3 Total and Marginal Seat Revenue Curves . .. ... .. ... .. .. 110 5.4 Maximizing Expected Revenues, Two Class Example ... .. ... 111 5.5 EMSR Solution for Nested 3-Class Example ... .. ... ... ... 117 5.6 Dynamic Application of EMSR Model, Two Nested Classes . ... 122 6.1 EMSR Protection Levels for Different Estimate of Demand Variation 155 6.2 EMSR Protection Levels Plotted Against Input Variables ... ... 156 6.3 The Virtual Nesting Concept . ... ... ... .. ... ... ... 160 7.1 Historical Booking Build-Up for a Departed Flight ... .. ... .. 173 7.2 Valid Flight Pair Comparison - Example 1 .. ... ... .... .. 189 7.3 Valid Flight Pair Comparison - Example 2 ... .. ... ... ... 190 List of Tables 3.1 Comparison of Seat Control Analyst Staffing Levels ... .... .. 70 4.1 Optimal Seat Allocation - Deterministic vs. Stochastic Demand . 89 5.1 Optimal Limits and Expected Revenues . ...... ...... ... 113 5.2 EMSR Example - Initial Booking Limits ......... ...... 132 5.3 EMSR Example - Day 35 Booking Limit Revision ...... .... 134 5.4 EMSR Example - Dynamic Revisions Day 28 to Day 7 .... ... 135 5.5 EMSR Example - Overbooking Limits ... ........ ..... 137 5.6 EMSR Example - Initial Limits With Upgrade Probabilities . .. 139 6.1 Editing of Day of Week Data Subsets ............. ..... 144 6.2 Impact of Correlation of Fare Class Demands ........ ..... 146 6.3 Empirical Analysis of Fare Class Demand Correlation ..... ... 149 7.1 ABLS