DISSERTATION Simulation-Based Analysis of Forecast Performance Evaluations for Airline ...

234 Pages·2010·1.78 MB·English

D I S S E R T A T I O N
Simulation-Based Analysis of
Forecast Performance Evaluations
for Airline Revenue Management
submitted to
Faculty of Business Administration and Economics
University of Paderborn
by
Dipl.-Wirt.Inf. Catherine Cleophas
Dean of the Faculty of Business Administration and Economics:
Prof. Dr. Peter F. E. Sloane
Referees:
1.) Prof. Dr. Natalia Kliewer
2.) Prof. Dr. Leena Suhl
Paderborn, July 2009
I
This thesis was created thanks to a cooperation between the International Graduate
School Dynamic Intelligent Systems at the University of Paderborn and the German
airline Deutsche Lufthansa AG. It includes the consideration of problems occurring in
applied revenue management under the aspect of academic research. The goal is to use
methodological approaches to airline revenue management, demand forecast and simula-
tion presented in the further text as well as expert knowledge and data available in the
industry.
The purpose of this text is the development of a new view of forecast performance, in
order to avoid some of the complications connected to evaluation of demand forecasts for
revenuemanagement. Toenablethis, atheoreticalconceptofdecomposingandevaluating
forecasts under the laboratory conditions provided by a simulation and using information
exclusive to simulation environments is developed. To demonstrate the potential of this
concept, the implementation of a simulation environment including a choice-based de-
mand model is documented. Finally, a number of statements about the implications of
forecast quality and forecast evaluation is expressed formally and tested using simulation
experiments to demonstrate the use of the proposed concept.
Subject classifications: Simulation, Forecasting, Revenue Management, Yield Manage-
ment, Inventory Control, Pricing, Price-Elasticity, Econometrics
Contents II
Contents
I. State of the Art and Research Opportunities 1
1. Introduction 3
1.1. Background and Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2. Motivation and Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3. Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2. Existing Research on Airline Revenue Management 12
2.1. Available Overview Literature . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2. State of the Art of Optimization . . . . . . . . . . . . . . . . . . . . . . . . 13
2.3. Appraisals of Recent Challenges . . . . . . . . . . . . . . . . . . . . . . . . 15
3. Demand Forecasting for Revenue Management 21
3.1. Demand Volume . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.2. Unconstraining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3.3. Demand Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4. Demand Forecast Performance Measurements 33
4.1. Theoretical Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
4.2. Applied Forecast Performance Evaluation . . . . . . . . . . . . . . . . . . . 44
5. Research Gap and Opportunities 46
II. Solution Approach - Concept and Implementation 48
6. Simulation for Decomposition and Evaluation of RM Systems 50
6.1. Overall System View . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
Contents III
6.2. Forecasting Component . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
6.3. Demand Volume Aspect . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
6.4. Unconstraining Aspect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
6.5. Demand Behavior Aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
7. Simulation Environment for Revenue Management 63
7.1. Simulation Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
7.1.1. Data Management . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
7.1.2. Simulation Runs and Lists of Events . . . . . . . . . . . . . . . . . 68
7.1.3. Reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
7.2. Supply and Demand Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
7.2.1. Supply Information . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
7.2.2. Demand Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
7.2.3. Exemplary Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
7.3. Revenue Management Components . . . . . . . . . . . . . . . . . . . . . . 89
7.3.1. Forecast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
7.3.2. Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
7.3.3. Inventory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
7.4. Market Implementations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
7.4.1. Demand Variations . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
7.4.2. Supply Variations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
III. Experiments and Conclusions 110
8. Simulation Based Analysis of Forecast Performance 112
8.1. Observations on Long-Term Effects of Forecast Methods . . . . . . . . . . 112
8.2. Consequences of Possible Definitions of Psychic Forecasts . . . . . . . . . . 137
8.3. Evaluation of Standard Accuracy Indicators . . . . . . . . . . . . . . . . . 154
8.4. Definitions and Effects of Uncertainty of Demand . . . . . . . . . . . . . . 168
8.5. Evaluation Approaches for Price-Sensitive Forecasts . . . . . . . . . . . . . 182
8.6. Simulation-Based Findings Recaptured . . . . . . . . . . . . . . . . . . . . 187
Contents IV
9. Conclusion 191
9.1. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191
9.2. Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193
List of Figures V
List of Figures
6.1. Evaluating the RM System . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
6.2. Comparing Forecasts and Bookings . . . . . . . . . . . . . . . . . . . . . . 54
6.3. Evaluating the Forecast Component . . . . . . . . . . . . . . . . . . . . . . 55
6.4. Evaluating the Trend Component . . . . . . . . . . . . . . . . . . . . . . . 58
6.5. Evaluating the Unconstraining Component . . . . . . . . . . . . . . . . . . 60
6.6. Evaluating the Choice Component . . . . . . . . . . . . . . . . . . . . . . . 62
7.1. The Simulation Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
7.2. Revenue Management Simulation . . . . . . . . . . . . . . . . . . . . . . . 70
7.3. Defining the Product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
7.4. Request Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
7.5. Example – Product . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
7.6. Example – Customer Types . . . . . . . . . . . . . . . . . . . . . . . . . . 88
7.7. Inventory: Protected and Available Seats . . . . . . . . . . . . . . . . . . . 100
7.8. Mix of Customer Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
7.9. SLF Average and Deviation depending on Error Term Deviation . . . . . . 105
7.10.Increase in Bookings by Additional Classes . . . . . . . . . . . . . . . . . . 109
8.1. Predicted Demand per Class with Exp050 . . . . . . . . . . . . . . . . . . 116
8.2. Decrease in Demand Predicted for Class “A” . . . . . . . . . . . . . . . . . 117
8.3. Protected Seats per Class with Exp050 . . . . . . . . . . . . . . . . . . . . 119
8.4. Decrease in Seats Protected for Class “A” . . . . . . . . . . . . . . . . . . 120
8.5. Observed Bookings per Class with Exp050 . . . . . . . . . . . . . . . . . . 122
8.6. Decrease in the Share of Bookings Observed for Class “A” . . . . . . . . . 123
8.7. Revenue in Percent of Revenue Earned in Run 1 . . . . . . . . . . . . . . . 125
8.8. Yield in Percent of Yield Earned in Run 1 . . . . . . . . . . . . . . . . . . 128
8.9. Yield in Percent of Yield Earned with First-Come-First-Serve . . . . . . . 129
List of Figures VI
8.10.Mean Absolute Deviation (MAD): Constrained FC from Observed BKD . . 131
8.11.Root Mean Squared Error (RMSE): : Constrained FC from Observed BKD 132
8.12.Mean Avg. Percentage Error (MAPE): Constrained FC from Observed BKD133
8.13.Theil’s U2 (U2): Constrained FC from Observed BKD . . . . . . . . . . . 134
8.14.Revenue Resulting from Exp050 and Exp050upd . . . . . . . . . . . . . . . 136
8.15.MAD during the Booking Horizon of Run 1 . . . . . . . . . . . . . . . . . 137
8.16.Uses of the Psychic Forecast in the Simulation . . . . . . . . . . . . . . . . 138
8.17.Average Revenue over 50 Runs in Percent of First-Come-First-Serve . . . . 145
8.18.Average Yield over 50 Runs in Percent of First-Come-First-Serve . . . . . . 147
8.19.Average SLF over 50 Runs in Percent of First-Come-First-Serve . . . . . . 149
8.20.Revenue in Percent of First-Come-First-Serve . . . . . . . . . . . . . . . . 152
8.21.Deviation of Revenue between Simulation Experimenets . . . . . . . . . . . 153
8.22.Possible Error Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . 157
8.23.Rank of Methods according to MAD in Product-Based Scenario with “Vol.
= 050, Dev. = 00” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
8.24.Rank of Methods according to MAD in Product-Based Scenario with “Vol.
= 100, Dev. = 00” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
8.25.MAD: Constrained Psychic Forecasts vs. Actual Bookings in the Product-
Based Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
8.26.MAD of Unconstrained Forecasts from Psychic Forecast in Product-Based
Scenario with “Vol. = 050, Dev. = 00” . . . . . . . . . . . . . . . . . . . . 163
8.27.Revenue in Percent of Revenue Earned by Psychic Forecast – Product-
Based Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
8.28.Ranks According to Different Error Measurements . . . . . . . . . . . . . . 167
8.29.Increase of Yield in Percent of FCFS from Vol. = 050 to Vol. 100 . . . . . 170
8.30.Revenue in Percent of FCFS on Price- and Product-Based Markets . . . . 171
8.31.Difference in MAD (Price-Based - Product-Based Market) in Percent . . . 173
8.32.MAPE Averaged over 50 Runs . . . . . . . . . . . . . . . . . . . . . . . . . 175
8.33.“Percent Better” Averaged over 50 Runs . . . . . . . . . . . . . . . . . . . 177
8.34.MAD for Naive Forecast Averaged over 50 Runs . . . . . . . . . . . . . . . 178
8.35.Runs Required for Confidence Level . . . . . . . . . . . . . . . . . . . . . . 179
8.36.Variance of MAD for Naive Forecast over 50 Runs . . . . . . . . . . . . . . 180
List of Figures VII
8.37.Revenue Robustness based on Rev. Averaged over 50 Runs . . . . . . . . . 181
8.38.Revenue in Percent of Revenue Earned in Run 1 . . . . . . . . . . . . . . . 184
8.39.MAD of Elasticity vs. Psychic Elasticity . . . . . . . . . . . . . . . . . . . 186
List of Tables VIII
List of Tables
7.1. Simulation Environment: Supply Lists . . . . . . . . . . . . . . . . . . . . 66
7.2. Simulation Environment: Demand Lists . . . . . . . . . . . . . . . . . . . . 67
7.3. Output of Simulation Experiments . . . . . . . . . . . . . . . . . . . . . . 72
7.4. Booking Classes Differentiated by Product-Feature . . . . . . . . . . . . . 106
7.5. Booking Classes Differentiated by Price . . . . . . . . . . . . . . . . . . . . 107
7.6. Booking Classes Differentiated by Product Characteristics and Price (Hy-
brid Differentiation) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
8.1. Possible Variations of Choice in Psychic Forecasts . . . . . . . . . . . . . . 139
1
Part I.
State of the Art and Research Opportunities