Fare Adjustment Strategies for
Airline Revenue Management and Reservation Systems
By
Yin Shiang Valenrina Soo
B.B.A. (Hons), NUS Business School
National University of Singapore, 2001
SUBMITTED TO THE DEPARTMENT OF CIVIL AND ENVIRONMENTAL
ENGINEERING IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
DEGREE OF
MASTER OF SCIENCE IN TRANSPORTATION
AT THE
INSTITUTE
OF TECHNOLOGY
MASSACHUSETTS
JUNE 2007
© 2007 Massachusetts Institute of Technology. All rights reserved.
Signature of Author: ................................................
1 ......
Department of Civil and Environmental Enginering
May 10, 2007
Certified by: ..........................................................
Peter P. Belobaba
Principal Research Scientist of Aeronautics and Astronautics
Thesi u ervisor
.... , ... ,..., ..........
Joseph 4At Sussman
ssor of Civil and Environmental Engineering
Thesis Reader
C ertified by: ...........................................................
JR East
.................................................
Daniele Veneziano
Chairman, Departmental Committee for Graduate Students
Accepted by : ................................................
MASSACHUSETTS INSTI
OF TECHNOLOGY
JUN 0 7 2007
LIBRARIES
E
ARKER
Fare Adjustment Strategies for
Airline Revenue Management and Reservation Systems
by
Yin Shiang Valenrina Soo
Submitted to the Department of Civil and Environmental Engineering on May 10, 2007
in Partial Fulfillment of the Requirements for the Degree of Master of Science in Transportation
ABSTRACT
With the growth of Low Cost Carriers (LCC) and their use of simplified fare structures, the
airline industry has seen an increased removal of many fare restrictions, especially in markets
with intense LCC presence. This resulted in "semi-restricted" fare structure where there are
homogenous fare classes that are undifferentiated except by price and also distinct fare classes
which are still differentiated by booking restrictions and advance purchase requirements. In this
new fare environment, the use of traditional Revenue Management (RM) systems, which were
developed based on the assumption of independence of demand of fare classes, tend to lead to a
spiral down effect. Airlines now have to deal with customers who systematically buy the lowest
fare available in the absence of distinctions between the fare classes. This result in fewer
bookings observed in the higher fare classes, leading to lower forecast and less protection of seats
for the higher yield passengers.
This thesis describes Fare Adjustment, a technique developed for network RM systems, which
acts at the booking limit optimizer level as it takes into account the sell-up potential of
passengers (the probability that a passenger is willing to buy a higher-fare ticket if his request is
denied). The goal of this thesis is to provide a more comprehensive investigation into the
effectiveness of fare adjustment as a tool to improve airline revenues in this new environment by
1) extending the investigation of the effectiveness of fare adjustment with standard forecasting to
leg-based RM systems (namely EMSRb and HBP) and also a mixed fare structure where different
fare structures are used for different markets, and 2) looking at the alternative use of fare
adjustment in the reservation system.
Experiments with the Passenger Origin-Destination Simulator demonstrate that RM Fare
Adjustment with standard forecasting can improve an airline's network revenue by 0.8% to 1.3%
over standard revenue management methods. In particular, RM Fare Adjustment reduces the
aggressiveness of path forecasting through the lowering of bid prices as it takes into account the
risk of buying-down. Simulations of Fare Adjustment in the Reservation System also showed
positive results with revenue improvement of about 0.4% to 0.7%.
Thesis Supervisor: Dr. Peter P. Belobaba
Title: Principal Research Scientist of Aeronautics and Astronautics
Thesis Reader: Dr. Joseph M. Sussman
Title: JR East Professor of Civil and Environmental Engineering
Acknowledgements
First and foremost, many thanks to Dr. Peter Belobaba for being my academic and
research advisor. He introduced me to the world of revenue management and taught me
all that I needed to know. I appreciate his guidance, mentorship and the opportunities he
has given me during these past 2 years here in MIT.
I must also thank all the airlines in the PODS consortium for making this research
possible through their financial support and continuous feedback. Acknowledgement also
goes to our programmer, Craig Hopperstad, for helping me understand the workings of
the PODS simulator.
As for all my fellow colleagues (past and present) in the MIT International Center for Air
Transportation and my friends in the MST program, thank you for helping me get
through the years. Special thanks to Maital Dar, Thierry Vanhaverbeke and Greg Zerbib
for teaching me the ropes and helping me learn about PODS.
I must also thank all my friends from the MIT Ballroom Dance Team in particular Jing
Wang, Mingzhi Liu and Royson Chong. Without them, my life would have been a lot less
exciting and my drawers would not be filled with so many ribbons and awards.
To my family back home in Singapore, thank you for all that you have done for me. I
would not be who I am today without you guys. Thank you for your unwavering love,
care and concern for me.
To Escamillo, my beloved husband, thank you for your love, understanding and support.
You are always there for me, encouraging me and cheering me on. Now that I am ending
this "Graduate School in MIT" chapter of my life, I look forward to starting a whole new
"Life in NYC" chapter with you. You are the best husband and friend one can ever hope
for. I love you.
And last but not least, I thank God for His love for me. Thanks to Him for giving me the
strength, the courage and faith to carry on even when things sometimes look impossible.
He has been my stronghold and shelter and I thank Him for all the provisions He had
given me and the miracles He had performed in my life.
-5-
-6-
-IMLE OF CONTENTS
Page
LIST OF FIGURES
10
LIST OF TABLES
13
1.
1.1
REVENUE MANAGEMENT
15
1.2
CHANGES IN THE INDUSTRY
17
1.3
NEW / RECENT APPROACHES
18
1.3.1
1.3.2
1.3.3
2.
15
CHAPTER ONE: INTRODUCTION
18
19
19
Forecasting and Sell-up
Q/Hybrid Forecasting
Fare Adjustment
1.4
OBJECTIVES OF THE THESIS
20
1.5
ORGANIZATION OF THE THESIS
21
CHAPTER TWO: LITERATURE REVIEW
2.1
23
REVENUE MANAGEMENT
2.1.1
2.1.2
23
25
26
Forecasting
Seat Inventory Control
2.2
LOW COST CARRIERS AND A LESS-RESTRICTED ENVIRONMENT
29
2.3
REVENUE MANAGEMENT TOOLS FOR THE NEW ENVIRONMENT
31
Fare Adjustment
32
33
CHAPTER SUMMARY
35
2.3.1
2.3.2
2.4
Q / Hybrid Forecasting
-7-
3.
CHAPTER THREE: SIMULATION APPROACH TO RM
37
3.1
THE PODS SIMULATION TOOL
37
3.1.1
3.1.2
39
41
3.2
3.3
4.
Passenger Choice Model
PODS Revenue Management Systems
SIMULATION ENVIRONMENT
42
3.2.1
3.2.2
42
44
Network D
Network S
FARE ADJUSTMENT IN REVENUE MANAGEMENT SYSTEM
48
3.3.1
3.3.2
3.3.3
FRAT5s
Sell up
Formulation and Parameters in PODS
49
50
52
3.4
FARE ADJUSTMENT IN RESERVATION SYSTEM
56
3.5
CHAPTER SUMMARY
57
CHAPTER FOUR: FARE ADJUSTMENT IN RM SYSTEM
59
4.1
NETWORK D
59
4.1.1
4.1.2
4.1.3
4.1.4
60
61
61
65
4.2
4.3
DAVN
EMSRb with Path Forecasting
EMSRb (Path) with Fare Adjustment
HBP
NETWORK S
67
4.2.1
4.2.2
4.2.3
4.2.4
68
69
70
73
EMSRb with Path Forecasting
Path-based EMSRb with Fare Adjustment
HBP with Fare Adjustment
DAVN with Fare Adjustment
CHAPTER SUMMARY
75
-8-
5.
CHAPTER FIVE: FARE ADJUSTMENT IN RESERVATION SYSTEM
5.1
5.2
Introduction of RES Bid Price to Leg-Based EMSRb
Leg-Based EMSRb with RES Fare Adjustment
Leg-Based HBP with RES Fare Adjustment
77
79
81
83
NETWORK S
5.2.1
5.2.2
Leg-Based EMSRb with RES Fare Adjustment
Leg-Based HBP with RES Fare Adjustment
83
87
CHAPTER SUMMARY
89
CHAPTER SIX: CONCLUSIONS
91
5.3
6.
77
NETWORK D
5.1.1
5.1.2
5.1.3
77
6.1
SUMMARY OF FINDINGS
92
6.2
FURTHER RESEARCH DIRECTIONS
94
-9-
T OF FIGURES
Page
1.
CHAPTER ONE: INTRODUCTION
Figure 1-1
Use of Differential Pricing to Maximize Revenue
2.
CHAPTER TWO: LITERATURE REVIEW
Figure
Figure
Figure
Figure
2-1
2-2
2-3
2-4
3.
Example of a Third Generation System
Spiral-Down Effect
Co-existence of Different Fare Structures in a Network
Using Fare Adjustment to Decouple the Fare Structures
16
24
31
33
34
CHAPTER THREE: SIMULATION APPROACH TO RM
Figure 3-1
Figure 3-2
Figure 3-3
Figure 3-4
Figure 3-5
Figure 3-6
Figure 3-7
Figure 3-8
Figure 3-9
Figure 3-10
Figure 3-11
Figure 3-12
PODS Structure
Network D
ALl's Network
AL2's Network
ALl's Route Network in Network S
AL2's Route Network in Network S
AL3's Route Network in Network S
AL4's Route Network in Network S
FRAT5 Curves in PODS
PODS FA FRAT5s Values with Different Scaling Factors
Probability of Sell-up and FRAT5s
Relationship between Fare, Marginal Revenue and PE Cost
-
10
-
39
43
43
43
45
46
46
47
49
50
51
53
4.
CHAPTER FOUR: FARE ADJUSTMENT IN RM SYSTEM
Figure 4-1
Figure 4-2
Figure 4-3
Figure 4-4
Figure 4-5
Figure 4-6
Figure 4-7
Figure 4-8
Figure 4-9
Figure 4-10
Figure 4-11
Figure 4-12
Figure 4-13
Figure 4-14
Figure 4-15
Figure 4-16
Figure 4-17
Figure 4-18
Figure 4-19
Figure 4-20
Figure 4-21
5.
Base Case Scenario - Fare Class Mix
DAVN with FA in Network D
EMSRb with Path Forecasting in Network D - Fare Class Mix
EMSRb with FA in Network D - Fixed FRAT5s
Airline 1 Fare Class Mix - EMSRb with FA (Fixed FRAT5s)
Load Factor and Yield - EMSRb with FA (Fixed FRAT5s)
Selected Adjusted Fares at FRAT5 C
EMSRb with FA in Network D - Variable FRAT5s
HBP with FA in Network D - Fixed FRAT5s
HBP with FA in Network D - Variable FRAT5s
Airline 1 Fare Class Mix - HBP
Airline l's Loads and Yield - HBP
Base Case Results - Network S
EMSRb with Path Forecasting - Network S
EMSRb with FA in Network S - Variable FRAT5s
HBP with FA in Network S - Variable FRAT5s
Al Spill-in Fare Class Mix - Network D vs. Network S
MSP Fare Class Mix - Path based HBP with FA in Network S
MSP Yield and Load Factor - Path based HBP with FA in Network S
DAVN Results - Network S
DAVN with Fare Adjustment in Network S
59
60
61
62
63
63
64
64
65
66
66
67
68
69
70
71
72
73
73
74
74
CHAPTER FIVE: FARE ADJUSTMENT IN RESERVATION SYSTEM
Figure 5-1
Figure 5-2
Figure 5-3
Figure 5-4
Figure 5-5
Figure 5-6
Figure 5-7
Figure 5-8
RES Bid Price in Network D
Airline 1 Spill in Fare Class Mix in Network D - RES Bid Price
Local vs. Connect Passengers in Network D - RES Bid Price
EMSRb with RES FA in Network D- FRAT5 psup
LF, Yield & Fare Class Mix - EMSRb with RES FA (FRAT5 psup) in D
EMSRb with RES FA in Network D - Input psup
Al's LF & Yield - EMSRb with RES FA (Input psup) in D
HBP with RES FA in Network D - FRAT5 psup
- 11 -
77
78
78
79
79
80
81
81
Figure
Figure
Figure
Figure
Figure
Figure
Figure
Figure
Figure
Figure
6.
Figure
Figure
Figure
Figure
5-9
5-10
5-11
5-12
5-13
5-14
5-15
5-16
5-17
5-18
HBP with RES FA in Network D - Input psup
Fare Class Comparison - HBP with RES FA in Network D
LF & Yield Comparison - HBP with RES FA in Network D
EMSRb with RES FA in Network S - FRAT5 psup
Al's LF & Yield in S - EMSRb w RES FA in All Mkts (FRAT5 psup)
EMSRb with RES FA in Network S - Input psup
Spill-In Trend - EMSRb with RES FA (All Markets) in Net work D
Spill-In Trend - EMSRb with RES FA (All Markets) in Net work S
HBP with RES FA in Network S - FRAT5 psup
HBP with RES FA in Network S - Input psup
82
83
83
84
85
85
86
87
88
88
CHAPTER SIX: CONCLUSIONS
6-1
6-2
6-3
6-4
Results Summary
Results Summary
Results Summary
Results Summary
- RM Fare Adjustment in Network D
- RM Fare Adjustment in Network S
- RES Fare Adjustment in Network D
- RES Fare Adjustment in Network S
-
12
-
92
92
93
94
T OF TABLES
Page
3.
CHAPTER THREE: SIMULATION APPROACH TO RM
Table 3-1
Table 3-2
Table 3-3
Table 3-4
Table 3-5
Table 3-6
Table 3-7
Table 3-8
4.
User-Defined Time Frames in PODS
Network D Fare Structure and Restrictions
Network S O-D Markets and Paths
Fare Structure and Restrictions - LCC Markets
Fare Structure and Restrictions - Non LCC Markets
PSUP for different FA FRAT5s Input
Booking Limits without Fare Adjustment (Path-based EMSRb)
HBP with Fare Adjustment
38
44
47
48
48
52
55
55
CHAFER FOUR: FARE ADJUSTMENT IN RM SYSTEM
Table 4-1 Results of Base Case Scenario
Table 4-2 Results of DAVN in Network D
Table 4-3 Results of EMSRb with Path Forecasting in Network D
Table 4-4 Adjusted Fares with Fixed FRAT5s
Table 4-5 Results of HBP in Network D - Leg Forecasting
Table 4-6 Results of HBP in Network D - Path Forecasting
Table 4-7 Results of HBP in Network S - Leg Forecasting
Table 4-8 Results of HBP in Network S - Path Forecasting
Table 4-9 Al Revenue Improvement over Leg-Based EMSRb in Network D
Table 4-10 Al Revenue Improvement from Fare Adjustment in Network D
Table 4-11 Al Revenue Improvement over Leg-Based EMSRb in Network S
Table 4-12 A l Revenue Improvement from Fare Adjustment in Network S
-
13
-
59
60
61
62
65
65
70
71
75
75
76
76
5.
CHAPTER FIVE: FARE ADJUSTMENT IN RESERVATION SYSTEM
Table 5-1
Table 5-2
Table 5-3
Table 5-4
Al
Al
Al
Al
Revenue
Revenue
Revenue
Revenue
Improvement
Improvement
Improvement
Improvement
over Leg-Based EMSRb in Network D
from RES Fare Adjustment in Network D
over Leg-Based EMSRb in Network S
from RES Fare Adjustment in Network S
-14-
89
89
90
90
1.
INTRODUCTION
Largely pioneered by the passenger airline carriers, revenue management - the integrated
management of price and capacity - has been considered by Robert Crandall, Chairman
and CEO of AMR and American Airlines, to be "the single most important technical
development in transportation management since we entered the era of deregulation in
1979."' Faced with relatively fixed capacity, low marginal sales costs and perishable
inventory, the challenge for an airline is deciding how best to allocate a fixed number of
perishable seats on a distinct and transient flight leg amongst the many possible journeys
which are available for purchase at different prices in order to maximize its revenue.
Thus, revenue management can simply be described as "selling the right seats to the right
customers at the right prices"'. Through a set of practices and policies, airlines make use
of revenue management to guide them in their pricing and seat allocation decisions. In
the following sections of this chapter, we will take a closer look at the history as well as
the reason for revenue management. In addition, we will also present the recent
developments in the airline industry and the new approaches that were developed to meet
these changes.
1.1
REVENUE MANAGEMENT
Before revenue management was born, airlines were mainly using a combination of
forecasting and controlled overbooking in their reservation control, deliberately selling
more seats than what is available to mitigate financial damage by passengers who do not
show up for the flight. This helped them to achieve a moderate degree of success and
almost all quantitative research in reservation control during that period focused on such
controlled overbooking.
In the early 1970s however, BOAC (now known as British Airways) began to introduce
two different fare products for the same flight - lower fares for passengers who book in
advance and higher fares for passengers who book closer to the date of departure2 . This
presented the airline with a problem in determining the number of seats that needed to be
protected for late full fare passengers and revenue management (then known as yield
management) was thus born.
Despite these early pioneers of revenue management, the main catalyst for the intensive
development of revenue management techniques can be traced back to the deregulation
of the airline industry in 1979, where Congress granted carriers control over their own
respective product offerings3 .
'Smith, B.C., J.F. Leimkuhler, R.M. Darrow. 1992. Yield Management at American Airlines. Interfaces.
Volume 22, Issue 1, pp. 8-3 1.
2 McGill, J. I., G. J. van Ryzin. 1999. Revenue Management: Research Overview and Prospects.
TransportationScience. Volume 33, Issue 2, pp. 233-256.
3 General Accounting Office. 1999. Airline Deregulation: Changes in Airfares, Service Quality, and
Barriers to Entry. Report to Congressional Requesters. GAO/RCED-99-92. Washington, D.C.
-
15 -
With deregulation, the airlines started to compete on an origin-destination (OD) basis
where passengers choose between airlines and products on a city-pair, independent from
any transits or stopovers in between. As pricing structures evolved and prices dropped in
many markets reflecting the more competitive environment, yield management became
more critical. Airlines began to realize that in order to maximize their revenues, it would
be optimal if they would be able to charge each passenger a fare that matches their
individual willingness to pay.
As we can see from Figure 1-1, if an airline decides to charge a single high fare (P1 )
during the whole booking period in order to target high-yield passengers, its flights will
depart with a very low load factor (Qi out of Q4 seats) because supply exceeds demand at
that price. In this case, the revenue management system overprotects the inventory as it
refuses to sell the empty seats at a lower price due to inaccurate expectation that higher
yield passengers will book in the future. There is a lost of revenue not only due to
consumer surplus (Area ABP 1 ) but also due to unsold seats (Area BQ 1 Q4E).
Price
A
Capacity of Aircraft
Loss of Revenue
(Consumer Surplus)
P2
D
P3
L
E
z
P4
Q0
Q1
Q
Q2
Q3
Demand
QQuantity
Q4
Figure 1-1: Use of Differential Pricing to Maximize Revenue
Conversely, if the airline decides to charge a single low fare in order to fill up its flights
(P2), most of its passengers will end up paying a fare that is lower than their willingness
to pay. As a result, the airline experiences a lost of revenue due to consumer surplus
(Area AEP 4 ). There is a dilution of revenue as the strong revenue streams an airline can
expects from its high yield passengers is diluted with low fares even though the load
factor is very high (full capacity Q4).
- 16 -
So clearly, single fare policies are unappealing because they result in lost revenue from
unsold seats and/or diluted fares. The use of differential pricing therefore became a core
component of successful revenue management'. By setting different fares (P1 to P4 ) for
identical seats on a plane, an airline is able to reduce the amount of revenue loss from
diluted fares and maintain a healthy load factor (see Figure 1-1).
Although in reality, it would be difficult to charge each passenger a fare that matches
their individual willingness to pay, it is however possible to group the passengers into
different segments and practice differential pricing for each of these segments. By putting
different fare restrictions such as advance purchase, Saturday night stay, refund charges
and cancellation fee for each fare classes, airlines have been segmenting the markets into
two main groups.
First, there are the business passengers who are less price-sensitive and usually book their
tickets closer to the date of departure. Then there are the leisure passengers who are
known to be more price-sensitive and are more flexible in terms of schedule. Such fare
restrictions are more strongly enforced for the lower fare products, thus creating much
disutility for business passengers, who in the end, are willing (or forced) to pay more to
avoid the restrictions.
However, complication arises as high yield passengers tend to book late whereas leisure
passengers with a lower willingness to pay tend to book early. There is thus a need for
revenue management systems to accurately forecast the high yield demand that would be
coming and find a way to optimize the allocation of seats to a specific fare class with its
associated restrictions.
1.2
CHANGES IN THE INDUSTRY
In recent years however, airlines' ability to prevent business travelers from buying down
(buying a lower priced product) has decreased tremendously. The emergence and growth
of low fare carriers has changed the industry. Unlike their legacy counterparts, these new
airlines have very low operating costs. Thus they are able to provide frequent and cheap
service to popular destinations at comparable service. In addition to lower fares, the other
visible characteristic of these new airlines is their use of simplified fare structures, which
although is less complex and less confusing, is also far less differentiated than the rest of
the major airlines.
Belobaba, P. P. 1998. Airline Differential Pricing for Effective Yield Management. The Handbook of
Airline Marketing, D. Jenkins (ed.). The Aviation Weekly Group of the McGraw-Hill Companies, New
4
York, NY, pp. 349-361.
-17-
This together with the ease of information gathering through the Internet in this day and
age, the market power has been shifting to the consumers who almost have full
information on all the different products that are available in the market. As a result, the
legacy airlines have no choice but to match their competitors' fares in markets where they
compete head on to avoid losing too much revenue and market share.
In these competitive markets, they changed their fare structure - compressing fare ratios,
removing some if not all of the restrictions and advance purchase requirements. This
resulted in a "semi-restricted" fare structure where there are homogenous fare classes that
are undifferentiated except by price and also distinct fare classes which are still
differentiated by booking restrictions and advance purchase requirements. In these new
fare environment, the use of traditional revenue management systems (which were
developed based on the assumption of independence of demand of fare class) tend to lead
to a spiral down effect as the airlines have to deal with customers who now
systematically buy the lowest fare available in the absence of distinctions between the
fare classes.
Thus, with little or no barriers to segment the market according to each passenger group's
willingness to pay, there is now a need to rethink the way seats are sold. For how would
seat inventory control algorithm be able to trade-off one fare product with another when
different fare products no longer exist?
1.3
NEW / RECENT APPROACHES
Given the quick expansion of the low-cost carriers and the simplification of fare
structures, new algorithms have been recently developed to provide for an optimal seat
inventory control that would counteract the challenges discussed previously in section 1.1
and 1.2.
1.3.1
Forecasting and Sell-up
Forecasting is the estimation of bookings-to-come by fare class and by flight using
historical unconstrained data obtained from previous "equivalent" flight booking process
records. In order for the revenue management system to work (setting aside an optimal
number of seats for the late higher yield passengers), forecast needs to be as accurate as
possible. With no distinction between products, every passengers would buy the lowest
fare available and it becomes impossible for the revenue management system to forecast
demand for each fare class using traditional methods as demand for highest classes do not
materialize. Furthermore, no observed demand in the historical database means that no
demand can be forecast for the highest classes in future.
-18-
One way of overcoming this problem is through the use of sell-up probability, which is
the probability that a passenger is willing to buy a ticket at a higher fare for the same
flight if he is denied booking for the requested lower fare product. By taking this
probability into account, the optimization process will close down lower fare classes
whenever necessary and protect more seats for higher yield passengers. This would thus
force passengers with higher willingness-to-pay to buy-up when they are denied booking
for their first (and usually lower fare product) request.
However, in order to use these sell-up probabilities in the revenue management systems,
one need to know how to estimate them dynamically during the forecast process as the
probabilities depend on the specific flight and they would not be optimal if set arbitrarily.
To meet this need, several developments had occurred in the area of revenue management
algorithms for less restricted fare structures such as Q-forecasting', Hybrid forecasting6
by Belobaba/Hopperstad and Fare Adjustment by Isler and Fiig?.
1.3.2
QiHybrid Forecasting
Q-forecasting was developed for fully undifferentiated fare structures where there is
absolutely no distinction between the different fare products and passengers would buy
the lowest available fare. Given this buying behavior, Q-forecasting seeks to forecast
only the lowest class demand (Q-class) and using estimates of passenger's willingness-topay, closes down lower fare classes in order to force "sell-up" into higher ones.
Hybrid forecasting on the other hand was designed for semi-restricted fare structures. It
seeks to classify all bookings into two categories - "product-oriented" demand and
"price-oriented" demand. It then predicts the future demand of both these two demand
categories (which are assumed to exhibit different booking behavior) using different
methods of forecasting for each category.
1.3.3
Fare Adjustment
7
Developed by Isler and Fiig at Scandinavian Airlines (SAS) and Swissair, Fare
Adjustment aims to increase airline revenues in a network where different fare structures
co-exist, typically a less restricted fare structure with almost no restrictions and a
tradition restricted fare structure. Using estimates of passengers' willingness to pay, it
adjusts the less restricted fares used by the network seat allocation optimizer (not the
Belobaba and Hopperstad, Q investigations - Algorithms for Unrestricted Fare Classes, PODS
presentation, Amsterdam, 2004
6Belobaba, P., C. Hopperstad. 2004. Algorithms for Revenue Management in Unrestricted Fare Markets.
Presented at the Meeting of the INFORMS Section on Revenue Management, Massachusetts Institute of
Technology, Cambridge, MA.
7 Isler and Fiig, SAS O&D Low Cost Project, PODS presentation, Minneapolis-St Paul, 2004
5
-19-
actual fares offered to passengers) to lower fare buckets and proactively close down
selected lower fare classes, thus forcing passengers to pay more for the higher-priced fare
products.
1.4
OBJECTIVES OF THE THESIS
As discussed, demand forecast is a critical component of the RM process. A precise and
sophisticated revenue management system can become useless if it uses inaccurate
forecasted demand for its seat allocation optimizer. Research have shown that by
changing the forecasting method, from the current more commonly used standard (pickup) forecasting to Q- or hybrid forecasting, airlines are able to experience a great increase
in revenue.
However, the impact of fare adjustment on airlines' revenue is still unclear. Research into
fare adjustment has so far been focusing on price-oriented passengers with the airline
using DAVN in a 2 carrier network. In his thesis, Cl6az-Savoyen' showed a 0.2%
increase in revenue with fare adjustment alone (standard forecasting) in a totally
undifferentiated environment using DAVN and a 0.63% increase in revenue when fare
adjustment is combined with Q-forecasting. Reyes' on the other hand investigated the
impact of fare adjustment and hybrid forecasting with DAVN in a semi-restricted
environment. The results showed that the use of fare adjustment alone (with standard
forecasting) in DAVN does not produce any revenue improvement while a combination
of fare adjustment and hybrid forecasting produces an increase of 4.15% revenue.
Therefore, the goal of this thesis is to provide a more comprehensive investigation into
the effectiveness of fare adjustment as a tool to improve airline revenues in this new
environment. This thesis will extend the investigation of the effectiveness of Fare
Adjustment with standard forecasting to other revenue management systems such as
EMSRb and Heuristic Bid Price.
In addition, it would also examine the impact fare adjustment has in a 4 carrier network
with mixed fare structure where different fare structures are used for different markets.
This would thus allow us to further investigate the results when fare adjustment is used in
all markets versus when fare adjustment is used only in markets with competition from
low cost carriers. Furthermore, we will also look at the impact fare adjustment has when
it is used in the reservation system instead of the revenue management system itself.
8 C16az-Savoyen,
R. L. 2005. Airline Revenue Management for Less Restricted Fare Structures. Master's
thesis, Massachusetts Institute of Technology, Cambridge, MA.
9 Reyes, M. H. 2006. Hybrid Forecasting for Airline Revenue Management in Semi-Restricted Fare
Structures. Master's thesis, Massachusetts Institute of Technology, Cambridge, MA.
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20-
1.5
ORGANIZATION OF THE THESIS
This thesis consists of three main parts: the literature review, PODS simulator approach
to Revenue Management, and an analysis of the results of PODS simulations.
Chapter 2 presents a discussion of previous work done on revenue management with an
emphasis on the problem of simplified and mix fare structures examined in this thesis.
Topics covered in the chapter include forecasting, specific RM models, the emergence of
simplified fare structures due to LCCs, and a discussion of fare adjustment technique.
An overview of the Passenger Origin-Destination Simulator (PODS) that is used for the
thesis is provided in Chapter 3. We will look at the workings of PODS with a focus on
the elements relating to standard forecasting and fare adjustment, as well as an
introduction to the simulations that were performed.
The results of the simulations are presented in Chapters 4 and 5. Chapter 4 deals mainly
with fare adjustment in the revenue management system while Chapter 5 deals with the
results of fare adjustment in the reservation system. Not only are the potential revenue
benefits (or losses) quantified, we also analyze some of the underlying effects fare
adjustment have on loads, yields, fare class mix, etc. in order to isolate the ramifications
of each experiment.
Finally, Chapter 6 attempts to summarize the experiments performed, as well as the
revenue benefits possible with the use of fare adjustment in the revenue management
system and reservation system. Several directions for future work are also presented.
- 21 -
-22-
2.
LITERA URE REVIEW
Revenue management has been much studied over the past 25 years. In the beginning, it
could have been considered as a narrow area of interest to academics and airline
operations enthusiast. Today however, revenue management is an indispensable tool that
is used by nearly every carrier in the world seeking to maximize their revenue.
This chapter starts by reviewing the evolution of revenue management systems and the
use of forecasting and seat inventory control in the airline industry. Next, we examine the
emergence of low cost carriers and the resulted less-restricted fare structure which require
changes in traditional revenue management. This chapter then concludes with the
presentation of two revenue management methods that were developed for the new
environment: Q/Hybrid forecasting and Fare Adjustment.
2.1
REVENUE MANAGEMENT
Using a combination of pricing and seat inventory control, the goal of revenue
management has been to determine what amount of capacity to offer to which customers
at what price so as to maximize revenue. Although this objective of revenue management
has never changed, our understanding of the problem and the approaches to solving this
problem has changed quite tremendously in the relatively short amount of time.
A very good review and description of this evolution of revenue management models in
the airline industry can be found in McGill and Van Ryzin'". Barnhart et al." and Clarke
and Smith" on the other presented more detailed overviews of fleet assignment, revenue
management and aviation infrastructure operations, although Clarke and Smith1 2 focused
more on the contributions of operations research to the airline industry.
The earliest revenue management system came in the form of the databases that airlines
used to keep track of their bookings. However, the process was more like data collection
or bookings observation rather than actual revenue management. This process was soon
improved upon in the second generation of revenue management system where airlines
were able to track the bookings of a specific flight before departure and compare that to
the forecasted booking patterns.
' McGill, J. I., G. J. van Ryzin. 1999. Revenue Management: Research Overview and Prospects.
TransportationScience. Volume 33, Issue 2, pp. 233-256.
'1 Barnhart, C., P. Belobaba, A. R. Odoni. 2003. Applications of Operations Research in the Air Transport
Industry. TransportationScience, Volume 37, Issue 4, pp. 368-391.
12
Clarke, M., B. Smith. 2004. Impact of Operations Research on the Evolution of the Airline Industry.
Journal of Aircraft. Volume 41, Issue 1, pp. 62-72.
-
23
-
Yet, advances in operations research were not integrated into the revenue management
process until the late 1980's and early 1990's when the ability to forecast and optimize
each future flight leg by booking classes was added to the third generation revenue
management system. And as shown in Figure 2-1, a typical third generation system thus
consists of three component models: the demand forecaster, the fare class mix optimizer
and the overbooking module.
Input Data
Revenue
Data
Historical
Booking
Data
Actual
Bookings
N
Data
RM Components
MOl
I
Output Data
Z
Figure 2-1: Example of a Third Generation System
1,1
Developed to reduce revenue loss due to no-shows, overbooking models have the longest
research history of among the three component models. It involves trading off between
denied boarding (which affects the image of the airline) and potential revenue loss from
unsold or spoiled seats by accepting more reservations than what is actually available for
a flight. An early, static overbooking model was produced by Beckman" and further
Belobaba, P.P. 2002. Airline Network Revenue Management: Recent Developments and State of the
Practice. The Handbook ofAirline Marketing, D. Jenkins (ed.). The Aviation Weekly Group of the
McGraw-Hill Companies, New York, NY, pp. 141-156.
1 Beckman, J. M. 1958. Decision and Team Problems in Airline Reservations. Econometrica.Volume 26,
pp. 134-145.
13
-
24-
researched by Thompson", Taylor" and Littlewood". Dynamic optimization approaches
to overbooking had also been developed by Rothstein" and Alstrup et al.' 9 More
information on the development of overbooking research can be found in McGill and
Van Ryzinl0 . For the purpose of this thesis however, no overbooking models will be used
in the simulations. Rather, we will only look at the forecasting and optimizer models
which are further elaborated in Section 2.1.1 and Section 2.1.2.
Using the airline's database of historical bookings, current booking data, revenue
(pricing) data as well as no-show data, the revenue management system then generates
recommended optimal booking limits for each flight and fare class. Such third generation
systems are used today by the vast majority of airlines across the world, and they
typically generate revenue improvement of 2% to 6% as compared to no seat inventory
control ",1.
2.1.1
Forecasting
As mentioned in Section 1.1, passengers with higher willingness-to-pay (typically
business travelers) also tend to make their bookings much later in the booking process
than those with a low willingness-to-pay. Therefore when practicing revenue
management, the airline have to make a initial guess as to how many seats to offer to the
early-booking but low-fare passengers and how many seats to reserve for the high-yield
passengers. Although the booking limits are dynamic in that they changes as time
progresses and bookings come in (i.e. the airline starts to have actual information about
the specific flight as opposed to just forecasted information), good initial estimates are
necessary to avoid filling up the plane early in the process with too many low-fare
passengers and later having to turn away high-fare demand.
Therefore, forecasting is arguably the most critical component of airline revenue
management because of the direct influence forecasts have on the booking limits that
determine airline revenues. It provides airlines with projected demand by fare class for a
given flight, based on complete historical observations of similar flights and incomplete
current observations for future flights. Different forecasting methods can be used to
obtain such estimate by transforming the data in different ways, using some or all of the
information that is available.
15
Thompson, H.R. 1961. Statistical Problems in Airline Reservations Control. OperationsResearch
Quarterly. Volume 12, pp. 167-185.
16 Taylor, C. J. 1962. The Determination of Passenger Booking Levels. "d AGIFORS Annual Symposium
2
Proceedings,Fregene, Italy.
7 Littlewood, K. 1972. Forecasting and Control of Passenger Bookings. 12'hAGIFORSAnnual Symposium
Proceedings,Nathanya, Israel, pp. 95-117.
" Rothstein, M. 1968. Stochastic Models for Airline Booking Policies. Ph.D. Thesis, Graduate School of
Engineering and Science, New York University, New York, NY.
19 Alstrup, J., S. Boaz, O.B.G. Madsen, R. Vidal, V. Victor. 1986. Booking Policy for Flights with Two
Types of Passengers. European Journal of Operations Research, Volume 27, pp. 274 -288
-
25
-
Pick-up forecasting, exponential smoothing, moving average, regression and
multiplicative pick-up are some of the revenue management forecasting methods that are
commonly used in practice20' ". However, an evaluation of the performance of several
forecasting techniques conducted by Wickham" found that pick-up models consistently
outperformed simple time-series and regression models. As we will be focusing on pickup forecasting in this thesis, this model is reviewed in greater detail in the next section.
For information on other forecasting techniques, one can refer to Zeni21 , Wickham2 2 ,
Zickus23 , Skwarek' and Gorin'.
Whereas a simple time series forecast would simply be the average of final bookings on a
set of similar flights, pick-up forecasting goes one step further by including the average
incremental bookings received in each time interval before departure. This pick-up data,
obtained from booking information of previous flights (i.e. historical data), is added to
the number of bookings on-hand to forecast the total demand at the end of a particular
period.
Alternatively, the advanced pick-up model (developed by L'Heureux 2 ) can also be used.
Similar in formulation, it builds on the classical model by incorporating data from flights
that have not yet departed. However, we will only be using the classical pick-up model
for this thesis. For additional information on pick-up forecasting, readers can refer to
Wickham22 , Zickus2 3 and Skwarek 2 4 .
2.1.2
Seat Inventory Control
As discussed earlier, the use of differential pricing results in the need for airlines to make
use of seat inventory control (either on a single leg or in a network) to ensure that the
low-fare leisure passengers do not consume all the seats on high demand flights.
Weatherford, L. 1999. Forecast Aggregation and Disaggregation. IATA Revenue Management
Conference Proceedings.
21 Zeni, R. H. 2001. Improved Forecast Accuracy in Revenue
Management by Unconstraining Demand
Estimates
from Censored Data. Ph.D. Thesis. Rutgers, the State University of New Jersey, Newark, NJ.
22
Wickham, R. R. 1995. Evaluation of Forecasting Techniques for Short-Term Demand
of Air
Transportation. Master's Thesis, Massachusetts Institute of Technology, Cambridge, MA.
23 Zickus, J. S. 1998. Forecasting for Airline Network Revenue
Management: Revenue and Competitive
Impacts. Master's Thesis, Massachusetts Institute of Technology, Cambridge, MA.
24 Skwarek, D. K. 1996. Competitive Impacts of Yield Management
Systems Components: Forecasting and
Sell-up Models. Master's Thesis, Massachusetts Institute of Technology, Cambridge, MA.
25 Gorin, T. 0. 2000. Airline revenue management: sell-up
and forecasting algorithms. Master's thesis,
Massachusetts Institute of Technology, Cambridge, MA.
26 L'Heureux, E. 1986. A New Twist in Forecasting Short-term Passenger Pickup. 26 AGIFORS Annual
Symposium Proceedings,Bowness-on-Windemere, England, pp. 248-261.
20
-
26
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2.1.2.1
Leg based Control
The most commonly used fare class mix allocation is the idea of serial "nesting" of fare
classes which was first solved by Littlewood 17 at BOAC for the case of a single-leg two
fare classes environment and expanded upon by Bhatia and Parekh" and Richter".
Instead of making the allocating of seats to the two fare classes separately, seats in the
higher fare class are protected by limiting the number of seats sold in lower fare classes
based on the demand forecast for each class, as well as the expected seat revenue.
This approach was subsequently generalized to a heuristic by Belobaba29 30 to determine
nested booking limits for a flight with any number of fare class using the concept of
Expected Marginal Seat Revenue (EMSR). He subsequently proposed a small adjustment
to this heuristic to make it more robust. This new heuristic became known as the EMSRb
method" and it has become one of the most widely used methods in the industry for
establishing booking limits on a flight leg basis.
By assuming that the fare classes demand are normal and independent, EMSRb uses legbased demand forecasts by fare class to produce leg-based seat protection levels for the
nested booking classes. The booking limits are determined based on the expected
marginal revenue, which is the probability of selling an additional seat in a given fare
class multiplied by the average fare of the booking class under consideration. Thus seats
are protected for a fare class as long as the seats' expected marginal revenue is greater or
equal to the fare in the next lower fare class. In addition, by creating joint demand
distributions (using mean demand and standard deviation from the individual classes), the
EMSRb approach allows for joint upper classes to be protected from the fare class just
below. More information on the EMSRb algorithm can be found in Mak", Lee" and
Williamson".
Bhatia, A.V. & Parekh, S.C. 1973. Optimal Allocation of Seats by Fare, AGIFORS Reservations and
Yield Management Study Group.
28 Richter, H. 1982. The Differential Revenue Methods to Determine Optimal Seat Allotments
by Fare
362
pp
339
Proceedings,
Symposium
Annual
Type,
22"
AGIFORS
29
Belobaba, P. P. 1987. Air Travel Demand and Airline Seat Inventory Management. Ph.D. Thesis,
Massachusetts Institute of Technology, Cambridge, MA.
30
Belobaba, P.P. 1989. Application of a Probabilistic Decision Model to Airline Seat Inventory Control,
Operations Research, Volume 37, pp. 183 - 197
31 Belobaba, P. P. 1992. "Optimal versus Heuristic Methods for Nested Seat Allocation."
AGIFORS
Reservations Control Study Group Meeting. Brussels, Belgium.
32 Mak, C. Y. 1992. Revenue Impacts of Airline Yield Management. Master's Thesis,
Massachusetts
Institute of Technology, Cambridge, MA.
3 Lee, A. Y. 1998. Investigation of Competitive Impacts of Origin-Destination Control using PODS.
Master's Thesis, Massachusetts Institute of Technology, Cambridge, MA.
3 Williamson, E. L. 1992. Airline Network Seat Inventory Control: Methodologies and Revenue Impacts.
Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, MA.
27
-
27
-
Others such as Brumelle and McGill", Curry36' Robinson", and Wollmer" approach the
multiple nested class problems using optimal formulations to determine the optimal
nested booking limits for multiple fare classes. However, such alternatives require a lot
more computational effect and their numerical results suggest that the much simpler
EMSRb is close to optimal.
2.1.2.2
Network O-D Control
Although leg-based control is vastly used in the industry today, it was designed to
maximize yields and not total revenue. Seats for connecting itineraries must be available
in the same class for all legs of the itinerary in order for the airline to accept the booking.
This means that bottle-leg legs can thus block out long-haul passengers even though their
contribution to the total revenues could be higher than local passengers.
Therefore, an airline would need to increase the availability of seats to high-revenue
connecting passengers regardless of yield in a network environment. However, at the
same time, the airline would also need to prevent these same connecting passengers from
displacing high-yield local passengers on full flights. The revenue management system
thus needs to be able to respond to different flight requests with different seat availability
based on the network value of the request. For this reason, much effort has been
expended to develop algorithms for path-based protection of booking classes (or OriginDestination Fare Class control).
First developed and implemented by American Airlines39 , the use of "virtual value
buckets" is one approach to network OD control. Here, the fixed relationship between
fare type and booking class is abandoned. Instead, the value buckets are defined
according to their revenue value, regardless of the restrictions. Each ODF is then
assigned to a revenue value bucket base which determines the availability of seat to the
ODF request. However, this method of grouping ODF gives priority to all long haul
higher revenue passengers over short haul lower revenue passengers, which does not
necessarily lead to network revenue maximization.
35
Brumelle, S. L. and McGill, J. I. 1988. Airline Seat Allocation with Multiple Nested Fare Classes. Paper
presented at the Fall ORSA/TIMS Conference, Denver, CO. Also presented at the University of British
Columbia, 1987.
36 Curry, R. E. 1990. Optimal Airline Seat Allocation with Fare Classes Nested by Origin and Destinations.
TransportationScience. Volume 24, Issue 3, pp. 193-204.
37
Robinson, L. W. 1995. Optimal and Approximate Control Policies for Airline Booking with Sequential
Nonmonotonic Fare Classes. OperationsResearch. Volume 43, Issue 2, pp. 252-263.
38
Wollmer, R. D. 1992. An Airline Seat Management Model for a Single Leg Route when Lower Fare
Classes Book First. OperationsResearch. Volume 40, Issue 1, pp. 26-37.
39 Smith, B.C. and Penn, C.W. 1988. Analysis of Alternate Origin-Destination Control Strategies,
AGIFORS Annual Symposium Proceedings,vol. 28, pp 123 - 144.
-
28
-
A slight adjustment was then proposed by American3 9 and United' to make the value
bucket assignments base on an ODF "net value", which take into account the
displacement of up-line and down-line passengers. The specific virtual bucket method
that will be investigated in this thesis is known as Displacement Adjusted Virtual Nesting
(DAVN). Using a deterministic linear program, DAVN calculates a "pseudo fare" for
each fare class in the network. This "pseudo fare" corrects the revenue value for network
displacement effects by deducting the revenue displacement that might occur on
connecting flight legs if the passenger's request for a multiple-leg itinerary is accepted
(other than the legs under consideration) from the total itinerary fare. More information
on virtual value buckets and DAVN can be found in Lee, Vinod" and Williamson.
Another approach to network O-D control is the Bid Price model as discussed in Smith
and Penn , Simpson42 and Wei43 . This is a much simpler inventory control than virtual
buckets as the airline only need to store the bid price (which is the approximated
displacement cost) value for each leg. The ODF is then compared to the itinerary bidprice at the time of availability request. If the bid-price is greater than the ODF, the
request will be rejected. Otherwise, the request will be accepted by the airline.
Specific bid price algorithms include the Network Bid Price (NetBP) method, the
Probabilistic Bid Price (ProBP) as described by Bratu" and the Heuristic Bid Price (HBP)
developed by Belobaba", which will be the main bid-price method used and discussed in
this thesis.
2.2
LOW COST CARRIERS AND LESS-RESTRICTED FARE ENVIRONMENT
The emergence of the low cost carriers (LCCs) has dramatically changed the landscape of
the airline industry. The main characteristic of the LCC model, apart from the very low
costs, is its simple fare product structure. Whereas the legacy airlines tend to have a mix
of many different types of tickets (also known as fare products) ranging from high to low
fare with various restrictions, LCCs tend to have only low fares with a few fare products
for each O-D market and very few restrictions if any.
40
Wyson, R. 1988. A Simplified Method for Including Network Effects in Capacity Control, AGIFORS
Annual Symposium Proceedings, vol. 28, pp 113 - 121.
41 Vinod, B. 1995. Origin and Destination Yield Management. The Handbook of Airline Economics,
D.
Jenkins (ed.). The Aviation Weekly Group of the McGraw-Hill Companies, New York, NY, pp. 459-468.
42 Simpson, R.W. 1989. Using Network Flow Techniques to Find Shadow Prices for Market
and Seat
Inventory Control, Memorandum M89-1, MIT Flight Transportation Laboratory, Cambridge, MA.
43
Wei, Y.J. 1997. Airline O-D Control using Network Displacement Concepts, Master's Thesis,
Massachusetts Institute of Technology, Cambridge, MA.
4 Bratu, S. J-C. 1998. Network Value Concept in Airline Revenue Management. Master's Thesis,
Massachusetts Institute of Technology, Cambridge, MA.
45 Belobaba, P. P. 1998. The Evolution of Airline Yield Management: Fare Class to Origin-Destination Seat
Inventory Control. The Handbook of Airline Marketing, D. Jenkins (ed.). The Aviation Weekly Group of
the McGraw-Hill Companies, New York, NY, pp. 285-302.
-
29
-
25
In his Ph.D. thesis, Gorin provides a comprehensive summary of changes in the U.S.
airline industry since deregulation, focusing especially on the entry and impact of LCCs.
In addition, comparisons of the characteristics that are traditionally associated with the
LCCs' model and legacy carriers' model can be found in Weber and Thie 4 , Dunleavy
and Westerman 7 , Cl6az-Savoyen 8 and Reyes 9 .
In response to the increasing competition, legacy carriers began to partially or fully match
the LCCs in terms of their fare products by lowering their fares, removing certain
restrictions and/or reducing the advance purchase requirements 8 ' 49, 50, 51. With their
revenue management systems still based on highly segmented market structures, this
advent of less differentiated fare environment made it difficult or even impossible for
airlines to effectively segment demand as the restrictions fencing business and leisure
passengers into their respective fare classes disappear.
With no differentiation among the fare products (except price), passengers naturally
would buy the lowest fare available (even though their willingness-to-pay is much
higher). Such buying down behavior results in a spiral-down effect where airlines get
stuck in a cycle leading to lower and lower revenues (Figure 2-2).
With fewer high-fare products being purchased by passengers, airlines' historical
booking data contains fewer records of these products being sold and the revenue
management system forecasts less demand for these products. This in turn results in less
seats being protected for the higher fare classes and more seats being made available to
the lower fare classes. This surplus of lower fare class seats starts the whole cycle again
and with each iteration, revenues becoming more diluted as there is now even less
inducement to buy high-priced products.
46
Weber, K. and Thiel, R. 2004. Optimisation Issues in Low Cost Revenue Management.
AGIFORS
Reservations & Revenue Management Study Group Meeting, Auckland, New Zealand.
47
Dunleavy, H. and Westermann, D. 2005. Future of Airline Revenue Management. Journalof Revenue
and PricingManagement. Volume 3, Issue 4, pp. 380-282.
48 Windle, R. and Dresner, M. 1999. Competitive Responses to Low Cost Carrier Entry. Transportation
Research PartE, Volume 35, pp. 59-75.
49 Forsyth, P. 2003. Low-cost Carriers in Australia: Experiences and Impacts. Journal of Air Transport
Management,Volume 9, Issue 5, pp. 277-284.
'0 Morrell, P. 2005. Airlines within Airlines: An Analysis of US Network Airline Responses to Low Cost
Carriers, Journalof Air TransportManagement,Volume 11, Issue 5, pp. 303-312.
51 Ratliff, R., Vinod, B. 2005. Airline Pricing and Revenue Management: A Future Outlook. Journalof
Revenue and Pricing Management.Volume 4, Issue 3, pp. 302-307.
-
30-
High-Fare Demand
"Buy Down" to Lower
Fare Classes
More Seats Made
Available at Lower
Fare Classes
..
.
T
Less Protection for the
Higher Fare classes
a
a
L
Fewer Bookings
Observed in Higher
Fare Classes
a
Lower Forecast of
High-Fare Demand
Figure 2-2: Spiral-Down Effect 8' 9
For a mathematical model of the spiral down effect, the reader is referred to Kleywegt et
al." and Cooper et al." In addition, the effects of buy-down and spiral down as a result of
fare structure simplification are also described and analyzed by Cusano", CldazSavoyen 8 , Ozdaryal and Saranathan"5 and Dar 6 .
2.3
REVENUE MANAGEMENT TOOLS FOR THE NEW ENVIRONMENT
As discussed in the previous section, the fare environment for legacy carriers has changed
as they react to the emergence of the LCCs. However, the introduction of fare products
with almost no restrictions or advance purchase requirements violates the fundamental
52
Kleywegt, A. J., T. Homem-de-Mello, W. L. Cooper. 2003. Models of the Spiral Down Phenomenon.
Paper presented at the Meeting of the INFORMS Section on Revenue Management, Columbia University,
New York, NY.
5 Cooper, W. L., T. Homem-de-Mello, A. J. Kleywegt. 2004. Models of the Spiral-down Effect in Revenue
Management. Working Paper, Department of Mechanical Engineering, University of Minnesota,
Minneapolis, MN.
54 Cusano, A. J. 2003. Airline Revenue Management under Alternative Fare Structures. Master's Thesis,
Massachusetts Institute of Technology, Cambridge, MA.
55 Ozdaryal, K., Saranathan, B. 2004. "Revenue Management in Broken Fare Fence Environment."
AGIFORS Reservations & Revenue Management Study Group Meeting, Auckland, New Zealand.
56
Dar, M. 2005.
"Spiral-Down" in Intermediate Fare Structures. PODS ConsortiumMeeting, Copenhagen,
Sweden.
-31-
assumption of independence of demand by fare class on which traditional revenue
management methods are based. There is therefore a need to develop new methods that
can adapt to this new environment.
2.3.1
Q / Hybrid Forecasting
To deal with totally unrestricted fare structure where the only differentiator is price,
Belobaba and Hopperstad , 6 have developed modified forecasting and optimization
approaches that do not rely on independent class demand. By transforming historical
bookings into Q-equivalent bookings (i.e. the total potential demand for the lowest
available class), "Q-forecasting" forecast demand only at the lowest classes (denoted as
Q-class). It then uses estimates of passengers' willingness-to-pay to close down lower
fare classes in order to force some of the Q-bookings to sell-up onto higher fare classes
(using the existing fare class structure to set the booking limits). This method has proved
to be an effective technique for forecasting when restriction-free fare structures are used.
However, Q-forecasting is not completely appropriate when an airline uses a semirestricted fare structure, where there are undifferentiated fare classes (usually at the lower
fare classes, differentiated only by price) and also higher fare classes that are
differentiated by some restrictions from the lower fare classes. This is because the
restrictions would deter some passengers from buying certain classes and therefore, not
all the passengers in this case would be price-oriented. Using traditional forecasting
methods would also prove to be suboptimal as the presence of the undifferentiated fare
classes again invalidates the assumption of independence among the fare classes.
To solve this problem, hybrid forecasting was developed by Boyd and Kallesen". Using a
new segregation of demand (yieldable versus priceable passengers), hybrid forecasting
aims to classify all bookings into one of these two demand categories and a separate
demand forecasting method is used for each segment.. For product-oriented demand,
bookings are treated as a historical data for the given class, and standard time series
forecasting applied. For price-oriented demand on the other hand, Q-forecasts by
willingness-to-pay based on expected sell-up behavior is used. More information on
hybrid forecasting can be found in Reyes's 9 thesis where he examined the process and
demonstrated that hybrid forecasting can improve an airline's network revenue by about
3% as compared to traditional forecasting methods.
57
Boyd, E. A., Kallesen, R. 2004. The Science of Revenue Management when Passengers Purchase the
Lowest Available Fare. Journalof Revenue and PricingManagement. Volume 3, Issue 2, pp. 171-177.
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32
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2.3.2
Fare Adjustment
Developed by Fiig and Isler7 in 2004, Fare Adjustment methods also aim to address the
problem of the coexistence of different fare structures in a single leg. With the entrance
of LCCs and the need to match their fare structures, legacy airlines typically end up with
a network consisting of a traditional, restricted fare structure (connecting passengers)
and a less-restricted, low-cost-carrier-type fare structure (local passengers).
Therefore, there would be a situation where an airline using DAVN may have 2 ODFs,
each of them belonging to a different OD market and fare structure, co-existing in the
same virtual bucket (Figure 2-3).
Restricted Fare
Structure
Pseudo Fare
Unresti
Fare Str
Pseudo Fare
Virtual
Buckets
Closed
Buckets
Figure 2-3: Co-existence of Different Fare Structures in a Network8
Conflict then arises because of the different characteristics of these two structures. Under
the traditional restricted structure, demand is assumed to be independent and sell-up
behavior is not taken into account. Demand in such a fare structure is segmented by the
restrictions and advance purchase requirements thus reducing the consumer surplus.
However, in the less-restricted fare structure, revenues are maximized by incorporating
sell-up behavior into forecasting and optimization. In this case, higher-class bookings are
achieved by closing down lower classes.
Thus, the closure of the bucket V4, which automatically make both ODF unavailable,
may be optimal under the less restricted strategy but not under the restricted strategy and
revenue will not be maximized.
-
33
-
With fare adjustment, we can lower the pseudo fare (the fare that is used by the system to
decide on the seat allocations and thus, may differ from the actual fare offered to
customers) of the unrestricted fare structure by a certain amount in order to shift it to a
lower virtual bucket. The amount to be decreased is referred to as the Price Elasticity cost
(PE cost). It accounts for the risk of buy-down and is dependent on the passenger
willingness-to-pay. With the introduction of this PE cost, we can now decouple the fares
which previously were in the same virtual buckets and thus, manage the two different
fare structures separately from each other. The airline can therefore close down the
unrestricted fare earlier, yet at the same time, keep the restricted fare open (Figure 2-4).
Restricted Fare
Structure
Unrestricted
Fare Structure
Pseudo Fare
Virtual
Buckets
-'
V4
------------a
~
Pseudo Fare - PE Cost
Closed Buckets
Figure 2-4: Using Fare Adjustment to Decouple the Fare Structures8
Cl6az-Savoyen 8 tested the FA methodology in the context of the DAVN optimization
process and concluded that FA had the potential to be an effective technique for seat
inventory control in restriction-free fare structure environments where two airlines are
competing head-to-head. It provides a 0.2% increase in revenue with standard forecasting
and a 0.63% increase when used with Q-forecasting.
Vanhaverbeke" and Reyes9 on the other hand, tested its applicability in a two airline
network with semi-restricted fare structure environments and both obtained an increase in
revenue ranging from 3.6% to 4.2% when FA is used with Hybrid Forecasting. Although
the use of FA alone in DAVN did not produce any revenue improvement, it increases
revenue obtained from HF alone by 1% to 1.5%. Vanhaverbeke" also tested the impact of
FA and Hybrid Forecasting when used with dynamic programming. However, the results
in this scenario were not conclusive.
Vanhaverbeke, T. 2005. DAVN with Hybrid Forecasting and Fare Adjustment
in Semi-Restricted
Environments. PODS ConsortiumMeeting, Boston, USA.
59
Vanhaverbeke, T. 2005. Dynamic Programming with Hybrid Forecasting and Fare Adjustment. PODS
Consortium Meeting, Boston, USA.
58
-
34-
2.4
CHAPTER SUMMARY
In this chapter, we started with a review of the literature on revenue management and its
three component models, with our discussion focusing mainly on forecasting and seat
inventory control. We then went on to take a look at the entry of Low Cost Carriers and
their impact on the fare structures of legacy airlines in section 2.2; here we described the
inadequacy of traditional revenue management in a less restricted fare environment and
how it leads to a spiral down effect. To reduce the negative impact of a less restricted fare
structure, Section 2.3 introduces reader to two new revenue management tools that were
designed for use in such simplified fare structures: Q/hybrid forecasting and fare
adjustment.
Fare adjustment has been shown to be effective when used with Q/hybrid forecasting for
airlines using DAVN. However, the impact of using fare adjustment alone (with standard
pick-up forecasting) is still unclear and given that not all airlines make use of virtual
bucketing or compete in markets where there is only one competitor, this thesis will
present scenarios where fare adjustment is used with other optimization models and also
in a network where there are more than one competitor. In addition, we will also be
looking at the possibility of using fare adjustment in the reservation system instead of the
revenue management system.
-
35
-
-36-
3.
SIMULATION APPROACH TO REVENUE MANAGEMENT
Competitive environments change very quickly and it is very hard to determine the
effects of a particular revenue management technique, forecaster or optimizer explicitly
in real airline environments as they are complex with far too many variables. This is why
simulation (where one can change one variable at a time and hold all others constant) is a
valuable tool as it allows for the experimentation and validation within a realistic model
of an airline environment.
Due to their static nature, analytic revenue management models entail a certain level of
simplification, hence leaving such models oftentimes inadequate for arbitrary passenger
booking behaviors or competitive actions among the airlines'. By taking a simulation
approach, a dynamic representation of revenue management practices can be modeled in
a competitive framework characterized by realistic interactions between passengers'
booking decisions and revenue management systems.
In this chapter, an overview of the Passenger Origin-Destination Simulator (PODS) that
is used to test Fare Adjustment for this thesis is provided. In addition, we will look at the
simulated air transportation network used for experimentation and the various component
modules that comprise PODS, including passenger choice, forecasting, and seat inventory
control methods.
3.1
THE PODS SIMULATION TOOL
Evolving from the Decision Window Model (DWM)61 , the Passenger Origin-Destination
Simulator (PODS) was developed at Boeing by C. Hopperstad, M. Berge, and S.
Filipowski. It simulates the environment of competing airlines and is used to study,
develop, and test new revenue management techniques. While the DWM choice model
determined passenger preferences based on the schedules, airline characteristics and a set
of other factors such as aircraft type, it omitted several important variables; namely, the
fares offered on each of the flights and the restrictions associated with each of the fares.
The PODS model has these added capabilities built in and although a fundamental part of
it replicates the schedule choice model of DWM, it is also capable of simulating
passenger choice of fare options. These capabilities make it possible to test the
competitive impacts of the implementation of a new revenue management system by one
or more of the hypothetical airlines.
60
Gorin, T., P. Belobaba. 2004. Revenue Management Performance in a Low-Fare Airline Environment:
Insights from the Passenger Origin-Destination Simulator. Journal of Revenue and PricingManagement.
Volume 3, Issue 3, pp. 215-236.
61 Boeing Airplane Company. 1997. Decision Window Path Preference Methodology Description. Seattle,
WA.
-
37 -
When PODS was first developed, it was only able to simulate a single flight leg. Now,
after much research and efforts to expand the model, it can simulate a typical airline
network of many spoke cities interconnected by a few airport hubs, in which airlines have
not only a wide variety of choices for their revenue management system but also one in
which they can vary their forecasting and optimization methodologies. In addition, the
network environment of PODS allows for passenger choice among different paths and
fare classes. Therefore, passenger demand is not modeled as an independent variable but
rather, it is treated to be interrelated and modeled using a more realistic aggregation of
many passenger level choices among competing airlines, schedules and fare products.
In PODS, passengers book or cancel their flights over 16 successive time frames. These
time frames have been user-defined to start 63 days prior to departure and end on the day
of departure. In this thesis's simulations, a time frame starts out lasting for one week, but
shrinks to two days as one approaches the date of departure in order to capture the
expected increase in booking activity as shown in Table 3-1. Passenger-related events
such as bookings and cancellations are spread randomly within each of these time frames,
while the revenue management system's major inventory control actions typically occur
at the start of each time frame.
Table 3-1: User-Defined Time Frames in PODS
Days to Departure
Time Frame
Length of Time Frame
63
56
49
42
35
31
28
24
21
17
14
10
7
_
5
_
3
_
1
_
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
(7)
(7)
(7)
(7)
(4)
(3)
(4)
(3)
(4)
(3)
(4)
(3)
(2)
(2)
(2)
(1)
0
To obtain the overall operating statistics for each simulated airline on a per day basis,
PODS averages the results obtained from the simulation. In PODS terminology, a "run"
consists of "trials" and "samples". For this study, a run (or a single simulation) is the
average of five independent trials and each trial is the iterative result of 600 samples (to
ensure statistical significance of a simulation's results), with each sample representing a
single departure day.
Data for the first sample of each trial are defined arbitrarily by the user. These user inputs
are gradually replaced with calculated values from simulation (with each sample having
some degree of correlation to the next sample for they are used as historical data) and the
first 200 samples of each trial are discarded. Thus, the results of a 600 sample trial are
based only on the last 400 samples, and every PODS run is actually the averaged result of
2,000 samples.
Fundamentally, PODS is the simulation of the interactions between passengers and
airlines (See Figure 3-1). On the passengers side, PODS simulates passengers are who
looking to travel in a specific OD market and trying to make their decisions based on a
variety of factors such as airlines, itineraries and fare classes. This contributes to the
demand side of the equation and booking information obtained on this side is passed over
the fence to the airlines.
-38-
Revenue Management
System
Passenger Choice Model
.- -- ~
Demand
Decision
Window
Seat Allocation
Optimizer
I
Generation
Model
Path/Class
,fl
-
Availability
Current
Bookings
Passenger
-Characteristic
Future
Bookings
Forecaster
Passenger
Choice Set
Path/Class
Bookings and
Cancellations
Update
Historical
Bookings
Historical Booking
Database
Passenger
Decision
Figure 3-1: PODS Structure
The airline side of the simulator on the other hand, consists of a third generation revenue
management system (as described in Section 2.1) that is used by airlines that supply air
travel offerings to customers. Using both the current and historical booking information,
the airlines determine the number of seats to offer for each of the fare classes on each OD
market. This information is then passed back over the fence to the passengers to influence
the demand patterns.
3.1.1
Passenger Choice Model
The success of any given revenue management method is dependent on its ability to
maximize revenues based on the behavior of passengers. In PODS, these behaviors are
governed by the Passenger Choice Model, which consists of four sequential steps:
Demand Generation, Assignment of Passenger Characteristics, Definition of Passenger
Choice Set and finally a specific Passenger Decision (see Figure 3-1). This section
provides a general overview of the Passenger Choice Model. A more comprehensive
discussion of the PODS Passenger Choice Model, including its assumptions, logic, and
ultimate validation can be found in Carrier 2 .
Carrier, E. 2003. Modeling Airline Passenger Choice: Passenger Preference for Schedule in the
Passenger Origin-Destination Simulator (PODS). Master's Thesis, Massachusetts Institute of Technology,
Cambridge, MA.
62
-
39
-
3.1.1.1
Demand Generation
During demand generation, the Passenger Choice Model generates an average daily air
travel demand for each OD market in the user-defined network and this total passenger
demand is apportioned into leisure and business passengers. Variability is then randomly
generated around the average daily demand, giving us the daily demand curve for each
group of passengers. However, this does not include seasonal or day-of-week variability.
Finally passenger arrival patterns during the booking process are modeled for both leisure
and business segments according to user-defined booking curves. With both the demand
and arrival curves established, individual passengers are generated at each time frame and
specific passenger characteristics are then assigned to these individuals, bringing us to
Step Two of the process.
3.1.1.2
Assignment of Passenger Characteristics
Here, the Passenger Choice Model generates a decision window which defines the
earliest acceptable departure time and latest acceptable arrival time for each passenger.
Leisure passengers would thus tend to have wider decision windows than business
passengers who are more time sensitive. At this stage, all paths and fare classes which
fall within a passenger decision window are considered equally appealing while
infeasible paths are equally unappealing.
In addition, the maximum willingness-to-pay (WTP) of each passenger, which is the
maximum out of pocket fare a passenger is willing to pay for the air travel, is also
generated from a user-defined price-demand curve. Given that leisure passengers are
more price sensitive, the leisure passenger price-demand curve is typically steeper (more
elastic) than the business passenger curve. Any fare exceeding the passenger's WTP is
excluded from his or her choice set.
Furthermore, disutility costs are also assigned to each passenger. These costs represent
the passenger's sensitivity to schedule preference (whether the flight falls within his
decision window or whether he needs to re-plan the window), path quality (non-stop
versus connecting itineraries), and fare product restrictions (such as Saturday night stay,
itinerary change fee and non-refundability). These disutility costs are randomly generated
based on user defined probability distributions by passenger type (business vs. leisure). A
more detailed description of the disutilities assignment process in PODS can be found in
Lee'.
Lee, S. 2000. Modeling Passenger Disutilities in Airline Revenue Management Simulation. Master's
Thesis, Massachusetts Institute of Technology, Cambridge, MA.
63
-40-
3.1.1.3
Passenger Choice Set
After assigning all the above passenger characteristics, the Passenger Choice Model then
presents each passenger with a set of fare products and fight options (including the option
of not making a booking) to choose from. Some of these options might then be eliminated
immediately from the choice set if 1) the fare is higher than the passenger's WTP, 2) the
advance purchase requirements are not met or 3) the fare class and/or paths are not
available due to revenue management controls.
3.1.1.4
Passenger Decision
As long as there are options where the fare is less than a passenger's max WTP, a
passenger will make his or her decision by calculating the total generalized cost (sum of
the fare and relevant disutility costs) for each available option and the option with the
lowest generalized cost is selected. This information then flows to the respective airline
revenue management system where seat inventory is updated and the booking recorded in
the historical database for future reference.
3.1.2
PODS Revenue Management Systems
As mentioned in Section 3.2, the airline side of PODS consists of a revenue management
module which consists of three interacting components: the Historical Booking Database,
the Forecaster and the Seat Allocation Optimizer. This structure, as well as the
relationships between the components and the links to the Passenger Choice model is
shown in Figure 3-1.
3.1.2.1
Historical Booking Database
The Historical Booking Database contains each airline's historical booking data by fare
class and path. It is initially filled with user defined default vales at the start of each trial
and these data are gradually updated and replaced with actual data as booking occur (as
described in Section 3.2). For simulations performed in this thesis, the historical booking
database includes the previous 26 simulated departures of a particular flight.
3.1.2.2
Forecaster
Using data extracted from the Historical Booking Database, the forecaster generates an
estimate of future demand for each given path and fare class. For the purpose of this
thesis, pick-up forecasting (as described in Section 2.1.1) is used to estimate future
demand by adding the number of current bookings on hand (at a certain point prior to
departure) to the average number of the incremental historical bookings that was
-41-
observed (and thus expected) to be "picked-up" between that specific point and day of
departure. Details on pick-up forecasting methodology in PODS can found in Zickus 23
and Skwarek.
However, given that the raw data from the database are often "constrained" in that they
reflect only actual bookings made when a fare class is actually available (and not closed
down due to revenue management control), there is a need to "unconstraint" the booking
data to estimate the number of bookings that would have materialized had the fare class
remained open throughout the whole booking process. Detailed analyses of different
detruncation methods can be found in Wickham2 and Skwarek . In this study however,
the Booking Curve detruncation technique (a percentage-based multiplier which
extrapolates demand for closed flights and fare classes using trend data from open ones)
is used to overcome the bias towards underestimating demand.
3.1.2.3
Seat Allocation Optimizer
Since there is a fixed seating capacity for each leg within the network, the Seat Allocation
Optimizer helps to determine the manner in which this fixed inventory of seats are
assigned to passengers who want to travel. In PODS, the user can choose from a number
of seat inventory control optimizers with varying level of sophistication. The three main
optimizers to be used in this thesis are EMSRb, DAVN and HBP (as described in section
2.1.2).
3.2
SIMULATION ENVIORNMENT
This section provides an introduction to the two simulated competitive environments that
are used in this thesis - the simulated air transportation networks and the carriers that are
providing the flights throughout the networks.
3.2.1
Network D
Network D is a simplified representation of the US domestic environment with two
competing hub-and-spoke carriers: ALl and AL2 as shown in Figure 3-2. Centrally
located in Minneapolis-Saint Paul International Airport (HI) and Dallas-Fort Worth
International Airport (H2) respectively, the two carriers compete in a network consisting
of twenty Western spoke cities (Number 1 - 20) and twenty Eastern Spoke cities
(Number 21 - 40). ALl's route network is shown in Figure 3-3 while AL2's network is
shown in Figure 3-4.
-42
-
Figure 3-2: Network D
Il
Figure 3-4: AL2's Network
Figure 3-3: ALl's Network
As shown in the Figures 3-2 to 3-4, each of the airlines operates non-stop west-to-east
service between its hub and the 40 spoke cities. They also operate a non-stop service to
and from the other airline's hub. In addition, each airline operates three such banks of
connecting flight daily.
Thus, Network D consists of a total of 252 legs and each airline serves 482 OriginDestination markets in each of the banks, with 42 local markets and 440 connecting
markets.
-43
-
"0 MOMMonaM--.''..
- .;4__
"' . __
1. -
As Network D is modeled to be a relatively symmetric network, where both airlines
operate similar schedules and offer identical fares, Table 3-2 shows the fare structure that
is used by both Airline 1 and Airline 2 for the purpose of this thesis.
Table 3-2: Network D Fare Structure and Restrictions
Restrictions
Advance Purchase
R1
Fare Class
Average
Fares
FC1
$412.85
0 days
FC2
$293.34
FC3
R2
R3
No
No
No
3 days
No
Yes
No
$179.01
7 days
No
Yes
Yes
FC4
$153.03
14 days
No
Yes
Yes
FC5
$127.05
14 days
No
Yes
Yes
FC6
$101.06
21 days
No
Yes
Yes
A total of 6 fare classes are used in Network D with FCI being the highest fare class and
FC6 being the lowest. The fares charged by the airline for each fare class vary according
to the OD path and the average fare charged for each of the fare classes is shown in the
second column of Table 3-2.
Advance purchase requirements show the number of days before flight departure in
which the fare class will be closed (independent of the seat allocation optimizer) and
lower fare classes are closed earlier in order to force sell-up. As for the restrictions, R1
represents the traditional Saturday night stay restriction that was used previously by
airlines to separate leisure and business travelers (who usually want to make it home for
the weekend). However, with the growth of LCCs and the trend towards less restricted
fare structure, this restriction is not as commonly used now and it therefore not modeled
in this network. R2 refers to the existence of itinerary change fee while R3 refers to a
non-refundability restriction.
3.2.2
Network S
Although Network D has its advantage in that it is able to provide quick "proof-ofconcept" and "all else being equal" simulations, it does lack two important aspects of the
current competitive environment - asymmetry and mixed fare structures. Airlines do not
usually see symmetric head-to-head competition in all their markets and neither do they
have a single fare structure for all their markets. Rather, there exists a mixture of
traditional and less-differentiated fare structures in many networks. Thus, Network S is
used to investigate the usefulness of specific revenue management enhancements in more
complex and asymmetrical environments.
-44
-
Consisting of 4 airlines (ALL, AL2, AL3 and AL4) with different size and markets,
Network S is a 4-hub network. ALl, AL2 and AL4 are modeled as traditional legacy
airlines while AL3 is a low-cost carrier. Figure 3-5 to Figure 3-8 depicts the route
network of the 4 airlines.
YVR
BOS
LGA
RAP
SF0S
PHL
S
SIFO
DE
.rt
$1
Figure 3-5: AL's Route Network in Network S
-45
-
YVR
SN
SFO
Figure 3-6: AL2's Route Network in Network S
YVR
stv
SFO
Figure 3-7: AL3's Route Network in Network S
-46
-
YVR
Figure 3-8: AL4's Route Network in Network S
As we can see, all the airlines provide connecting flights through their hubs, and they also
offer non-stop services that bypass their hubs. The main airline, ALI is again based in
Minneapolis-Saint Paul International Airport and serves every OD market in the network.
It has a close competitor, AL2, who is based in Chicago and serves most of its OD
markets. AL3 is modeled as a low cost competitor that is based in MCI. It competes in
about half of ALl's OD markets and has the highest number of hub-bypasses. Finally,
AL4 is a smaller-sized traditional airline based in Dallas-Fort Worth International
Airport. Table 3-3 summaries each airline's OD markets and paths.
Table 3-3: Network S O-D Markets and Paths
# of Hub
Bypass
Airline
# of Origin
Cities
# of Destination
Cities
ALl (MSP)
24
24
AL2 (ORD)
24
23
6
AL3 (MCI)
15
20
AL4 (DFW)
18
24
-47
-
O-D Markets
(Local / Connect)
Total #
of Paths
572
(42/436)
1457
548
(45/503)
1458
19
296
(33/263)
907
4
428
(40/388)
1044
In terms of the fare structure, Network S is a "mixed-fare" network. Being a low cost
competitor, AL3 uses a fare structure with more compressed fares and fewer restrictions.
And due to head-on competition, all the traditional airlines match AL3's fares in all the
296 O-D markets it competes in. Thus, these markets have a less-restricted fare
environment (as shown in Table 3-4). In the remaining markets however, the traditional
airlines continues to use the more restricted fare structure (Table 3-5).
Table 3-4: Fare Structure and Restrictions - LCC Markets
Fare Class
Average
Fares
FC1
FC2
FC3
$324.14
Restrictions
Advance Purchase
R1
R2
R3
$188.21
0 days
0 days
7 days
No
No
No
No
Yes
No
No
No
Yes
FC4
$146.38
7 days
No
Yes
Yes
FC5
FC6
$125.47
$104.56
14 days
14 days
No
No
Yes
Yes
Yes
Yes
$250.95
Table 3-5: Fare Structure and Restrictions - Non LCC Markets
3.3
Restrictions
Advance Purchase
RI
Fare Class
Average
Fares
FC1
FC2
$674.96
$530.33
0 days
3 days
FC3
$385.69
FC4
FC5
FC6
R2
R3
No
No
No
Yes
No
No
7 days
No
Yes
Yes
$257.13
10 days
Yes
Yes
Yes
$208.92
$160.71
14 days
14 days
Yes
Yes
Yes
Yes
Yes
Yes
FARE ADJUSTMENT IN REVENUE MANAGEMENT SYSTEM
As discussed in Section 2.3.2, Fare Adjustment was originally developed by Fiig and
Isler6 to improve the revenues of airlines using virtual nesting-based seat allocation
optimizers in unrestricted fare structure. It was also later extended to be use in a semirestricted fare class (which has both differentiated and undifferentiated fare classes).
Instead of dealing with the lack of class differentiation problem through the demand
forecaster side, it approaches the issue from within the seat allocation optimizer itself.
-48
-
".
41,111,011,01-
111-1 1~- 1
1 1~
-
3.3.1
,,
-
-
-
_- - - - -
-,
__
- - I- I
FRAT5s
FRAT5 values are used by airlines in PODS as an estimate for passengers' willingnessto-pay. "FRAT5" is the fare ratio at which 50% of the demand for the lowest fare class
will sell-up to a higher class. A high FRAT5 value means that the passengers are less
price-sensitive while a lower FRAT5 value denotes higher price sensitivity among the
passengers. Thus it quantifies the probability that a passenger will sell-up from Q-class
(the lowest fare class) to a more expensive fare class.
In addition, we assume that passenger's willingness-to-pay increases as we move closer
to the date of departure, as business travelers (who are willing to pay more for their
tickets) tend to book later towards the end of the booking period. This behavior is
captured in PODS by assuming that FRAT5 values increase gradually following a "Sshape" curve as the date of departure draws near. From Figure 3-9, we can see that in
Time Frame 1 (FRAT5 = 1.2), the median willingness-to-pay is 120% of the Q-class fare.
As we progress through the time frames, the FRAT5 value increases and by Time Frame
16, the median willingness-to-pay has risen to 300% of the Q-class fare.
3.5
-
2,5
2.0
1.5
1
2
3
4
5
6
7
8
9
1,0
'1
1,2
1,3
14
15
16
Time Frame
Figure 3-9: FRAT5 Curves in PODS
Although these FRAT5s can be used to estimate passenger willingness-to-pay and
forecast demand for the various fare classes in the forecaster, there is also a need capture
this sell-up behavior in the seat allocation optimizer that Fare Adjustment works in. As
the passengers' willingness-to-pay increases, the price elasticity (PE) cost used in Fare
Adjustment (as described in 2.3.2) must also increase to close down the lower fare classes
more quickly.
As explained in Cl6az-Savoyen's thesis8 , the FRAT5 values used for fare adjustment
should be lower than those used in forecasting. To solve this problem, he made use of a
linear set of Fare Adjustment FRAT5s which are independent of the forecasting FRAT5
values. However, it does not make sense for an airline to assume two different
-49-
- __
11
=NQ
willingness-to-pay (and thus FRAT5 values) for its passengers. Therefore, we will be
using a scaling factor to relate the two FRAT5 values as shown in the equation below.
FA FRAT5f = 1+ f 5scl(FRAT5tf -1)
Where:
FA FRAT5tf
FRA T5 f
= Fare Adjustment FRAT5 value in a particular time frame;
= FRAT5 value used for forecasting in a particular time frame;
= A scaling factor (between 0 and 1) for the two sets of FRAT5s.
f5scl
With the implementation of the scaling factor, the airline now has to decide on the
FRAT5 that is to be used for forecasting and also the appropriate scaling factor that
would best describe its passengers' willingness-to-pay for use in its Fare Adjustment (see
Figure 3-10).
More
Aggressive FA
3-
FASC
22.5-.FATC
in
0.13
-0.2
I'
0.0.1
Less
12
34
56
7
89
;0;11;2
1314 15
16
Aggressive FA.
Time Frame
Figure 3-10: PODS FA FRAT5s Values with Different Scaling Factors 8
3.3.2
Sell up
In PODS, there are two ways to account for the sell-up behavior. One can either input the
probability of sell-up, psup, arbitrary or one can make use of the forecasting FRAT5
values to derive the psup. In the latter, sell up is assumed to follow an inverse exponential
shape (see Figure 3-11) and therefore, the psup from Q-class to some higher fare class, f,
is an inverse exponential function of the fare ratio between Q and f, and a sell-up
constant, supc, based on the FRAT5, as shown below.
-
50-
PSUPq-f = e-
faref
a
supc = -In (0.5) / (FRAT5 - 1)
where:
faref = Fare of higher fare class, f
fareq= Fare of lowest fare class fare, q
As we can see from Figure 3-11, the more aggressive the FRAT5s, the greater the
probability of sell-up to higher fare class. Therefore an airline using a high FRAT5 value
in PODS will assume that the passengers have a high willingness-to-pay and thus, protect
more high fare class seats to account for the higher expected sell-up.
1
-FRAT5=
08
2.0
-FRAT5=
-FRAT5
2.5
= 3.0
0.6
'A
o
0
-- -
-
0.2
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
Fare Ratio
Figure 3-11: Probability of Sell-up and FRAT5s
8
However, the use of FRAT5 to calculate sell-up means that we are not able to change the
psup of the higher fare classes without affecting the psup of the lower fare classes. As
seen from Table 3-6, in order to have some psup for the highest fare class (Class 1), there
is a need to make use of a very high FA FRAT5 input of 1.6. This pushes the psup of the
fare class 4 and 5 to over 50% and thus reduces the number of seats that are available for
these lower fare classes.
Although this may be the best solution for a very full flight, such an aggressive strategy
(sacrificing load for yield) could possibly result in lower revenue especially in markets
with less demand. In this thesis, we will be making use of and comparing the results of
both input psup and the use of FRAT5s to calculate psup.
-51-
Table 3-6: PSUP for different FA FRAT5s Input
FA FRAT5=1.2
Class
FA FRAT5=1.4
Ave Fare Fare Ratio PSUP
Class
101.06
1.00
100%
1.26
127.05
41%
153.03
1.51
17%
179.01
1.77
7%
2
293.34
2.90
0%
1
412.85
4.09
0%
Ave Fare
6
5
4
3
6
5
4
3
2
1
101.06
127.05
153.03
179.01
293.34
412.85
FA FRAT5=1.6
Class
FA FRAT5=1.8
Ave Fare Fare Ratio PSUP
101.06
1.00
100%
127.05
5
1.26
74%
4
153.03
1.51
55%
3
179.01
1.77
41%
2
293.34
2.90
11%
1
412.85
4.09
3%
Class
6
3.3.3
Fare Ratio PSUP
1.00
100%
1.26
64%
1.51
41%
1.77
26%
2.90
4%
4.09
0%
6
5
4
3
2
1
Ave Fare Fare Ratio PSUP
101.06
1.00
100%
127.05
1.26
80%
153.03
1.51
64%
179.01
1.77
51%
293.34
2.90
19%
412.85
4.09
7%
Formulation and Parameters in PODS
There are two methods of fare adjustment in PODS: a continuous marginal revenue
formulation (MR) and a discrete formulation (KI). The continuous FA method assumes
negative exponential sell-up function and its formulation for the adjusted fare is as shown
below.
MR Fare Adjustment
=Rii
f
1
fareQ (FA Frat5-1)
= fare
-l(O
- ln(O.5)
The KI formulation on the other hand is generalized for all sell-up functions and this is
the formulation that we will be using for our simulations in this thesis.
KI Fare Adjustment
[
MR,,Ier = fadj =
psupf
.
fares - psup_1 fare1
Psupf - Psup,
-
52 -
-N
The PE cost can then calculated using the following equation:
PE cost = OD Fare - MR
This relationship is as shown in Figure 3-12 where the PE cost is the difference between a
given fare and the marginal revenue the carrier can expect to receive after correcting for
any revenue lost due to buy-down behavior.
1500
-
---
PE Costs
1300._
7001100.-,
--
_
_
Prke P(Q)
Cont MR
-500_-_
-
-_-
-_
Figure 3-12: Relationship between Fare, Marginal Revenue and PE Cost 7
In DAVN, fare adjustment reduces the pseudo fare that is used for optimizing booking
limits in virtual buckets by the PE costs which reflects the risk of buy-down as shown in
the equation below. Thus, mapping to virtual buckets is based on the adjusted fares (or
MR) minus displacement cost.
Pseudo Fare
=
(OD Fare - PE cost) - Displacement Cost
=
MR - Displacement Cost
However, there can be no re-mapping of classes if fare adjustment is used with EMSRb
fare class control. Instead, the average fare for each fare bucket is the weighted average
of adjusted fares for all the path/classes in the bucket. EMSR calculations then are
applied to the aggregate leg/bucket demands and adjusted average fares as illustrated
below.
-
53
-
Given FA FRAT5 = 1.6 and supc = 1.1552,
Flight SFO-BOS (via MSP)
Flight SFO-MSP
FC
Fare
Mean Dd
FC
Fare
Mean Dd
1
$300.00
6
0.0417 $ 300.00
psup
fadj (KI)
1
$400.00
4
0.0313 $ 400.00
2
$200.00
7
0.1768
$ 169.12
2
$250.00
3
0.1768
$ 217.79
3
$180.00
9
0.2360 $ 120.27
3
$200.00
4
0.3150
$ 136.04
4
$150.00
11
0.3639
$ 94.67
4
$170.00
5
0.4454
$ 97.57
5
$100.00
14
0.7492
$ 52.77
5
$120.00
8
0.7937
$ 56.04
6
$80.00
18
1.0000
$ 20.271
6
$100.00
10
1.0000
$ 23.05
Flight SFO-JFK (via MSP)
psup
fadj (KI)
FC
Fare
Mean Dd
1
2
$350.00
$225.00
5
3
0.0355 $ 350.00
0.1768 $ 193.56
3
4
5
$190.00
$160.00
$110.00
3
4
8
0.2770
0.4072
0.7736
$ 128.29
$ 96.13
$ 54.44
6
$90.00
10
1.0000
$ 21.67
SFO-MSP Leq Capacity Allocation - 100 seats
Using EMSRb Path Based Forecast with Fare Adiustment
FC
1
2
3
4
5
6
Avg fadj
$350.00
$193.49
$128.20
$96.13
$54.42
$21.66
Mean Total Dd Std Dev Booking Limits
100
15
5
86
13
8
71
7
16
9
54
20
11
28
30
38
6
-13
IN
Avgfadj= IOD fadjoD *WOD
O
where
fadjOD = Adjusted Fare for a particular OD market
Weightage given to that particular OD
N = Total number OD associated with this particular leg
WOD =
-
54 -
psup
fadj (KI)
Comparing the booking limits obtained above with the results from EMSRb Path-based
forecast with no Fare Adjustment in Table 3-7, we can see that not only does Fare
Adjustment allocates fewer seats to the lower fare classes, it also closes down FC6 right
at the start. Thus, by taking into account the PE cost, fare adjustment helps to close down
lower fare classes earlier and forces passengers to buy-up.
Table 3-7: Booking Limits without Fare Adjustment (Path-based EMSRb)
FC
1
2
3
4
5
6
Avg Fare
$350.00
$225.00
$190.00
$160.00
$110.00
$90.00
Mean Total Dd
15
13
16
20
30
38
Std Dev
5
8
7
9
11
6
Booking Limits
100
87
76
60
36
5
As for HBP, path/classes are mapped to the virtual buckets based on actual fares, even
though the virtual bucket average values are based on the adjusted fares as illustrated in
Table 3-8. This thus leads to adjusted EMSR value for each leg which is then compared
to the adjusted path/class fare.
FC
1
Fare
2$
SFO - MSP
SFO - BOS
SFO - JFK
3
-4
-5
-6
$80
fad' (KI)
$ 300.00
169.12
$ 120.27
$ 94.67
$ 52.77
$ 20.27
FC
1
2
3
4
Faefadj (KI)
$40$400.00
$ 217.79
$ 136.04
$ 97.57
$ 56.04
5
$ 23.05 J
6
FC
1
2
3
4
5
6
Fare
$350
$1 g
$90
fadj (KI)
$ 350.00
~$ 193.56
$ 128.29
$96.13
$54.44
$ 21.67
Table 3-8: HBP with Fare Adjustment
LZ~
#
Buckets
Path/Class
Bucket Value
1
2
3
4
5
6
7
8
$350 - $400
BOS/1; JFK/1
MSP/1; BOS/2
MSP/2; BOS/3; JFK/2
MSP/3; JFK/3
BOS/4;JFK/4
MSP/4; BOS/5
MSP/5; BOS/6; JFK/5
MSP/6; JFK/6
$375.00
$258.89
$166.24
$124.28
$96.85
$75.36
$43.42
$20.97
-55
$80 -1
$0-$99
-
3.4
FARE ADJUSTMENT IN RESERVATION SYSTEM
In addition to investigating fare adjustment in the revenue management system, this
thesis will also be looking at the other alternative - the use of fare adjustment in the
reservation (RES) system. Since an airline that uses DAVN can very easily incorporate
Fare Adjustment in their revenue management system (through virtual nesting), we will
be looking at introducing RES Fare Adjustment for airlines that are using EMSRb or
HBP. This is a post-RM process that occurs after forecasting and optimization and thus
no feedback is provided back to the RM system.
As per fare adjustment in RM, fares in the reservation system are adjusted using the Karl
Isler equation with either input FRAT5s with FA multiplier or user-defined direct sell-up
inputs for each fare class below Class 1. The decision to accept or reject a passenger
request is then made by comparing the adjusted decision fares to the critical EMSR
values (leg bid prices) obtained from the revenue management system at the completion
of an EMSR/HBP optimization. Therefore, the reservation system will close down a fare
class if
Adjusted Decision Fare < Max [EMSRJ, EMSR2]
where EMSR1 and EMSR2 are the critical EMSR values that are fed from the revenue
management system to the reservation system.
Given FA FRAT5 = 1.6, supc = 1.1552,
Leg MSP - BOS
FC
1
2
3
4
5
6
Booking Limits
100
90
80
62
36
4
Leg Bid Price: $80
(EMSR1)
Leq SFO - MSP
Booking Limits
100
87
FC
1
2
3
4
5
6
77
61
36
5
Obtained from Revenue
Management System
Leg Bid Price: $120
(EMSR2)
For single-leg paths, the adjusted decision fare is compared to the critical EMSR value of
the leg (EMSRl). If the adjusted fare is less than this critical value of EMSR, the
corresponding leg/class booking limit is set to zero (i.e. the fare class is closed).
Otherwise, the booking limit remains unchanged as illustrated below.
-
56 -
Single Leg Path
FC
1
2
3
4
5
6
+
RES Fare
$200.00
$170.00
$150.00
$120.00
$80.00
$60.00
MSP - BOS
psup
0.0675
0.1203
0.1768
0.3150
0.6804
1.0000
fad (KI)
$200.00
$131.63
$107.42
$ 81.63
$ 45.521
$ 17.42
Compared with
leg bid price
(EMSR1) of $80
FC
1
2
3
4
5
6
RES Bkg Limit
100
90
80
62
0
0
For two-leg paths, the adjusted decision fare is compare to the maximum of the two
critical EMSR values (obtained from the two legs: EMSR1 and EMSR2). If the adjusted
fare is less than the maximum, the availability for the path is reduced to zero. Otherwise,
no change is required and the minimum of the EMSRb booking limit on the two legs is
applied (see illustration below).
Two-Leg Path
FC
1
2
3
4
5
6
+
RES Fare
$400.00
$250.00
$200.00
$170.00
$120.00
$100.00
SFO - MSP - BOS
psup
0.0313
0.1768
0.3150
0.4454
0.7937
1.0000
fadj (KI)
$400.00
$217.79
$136.04
$97.57
$56.04
$23.05
Compared with
max leg bid price
{$120, $80)
FC
1
2
3
4
5
6
RES Bkg Limit
Min {100, 100
Min {87,90}
Min {77, 80}
0
0
0
Once a booking is accepted, it is recorded in the fare/class as per normal. However, no
modifications to the bookings, forecasts or revenue inputs to EMSRb logic are made for
future timeframes or departures, apart from the effects of rejected bookings and more seat
availability for some path/classes.
3.5
CHAPTER SUMMARY
In this chapter, we presented the Passenger Origin-Destination Simulator that is used to
test Fare Adjustment in this thesis. Specifically, we described the two components of
PODS: the Passenger Choice Model and the Airline Revenue Management system. We
also went on to describe both the simulated air transportation networks within PODS and
how Fare Adjustment is modeled in both the revenue management and reservation
system.
-57
-
The following two chapters provide the results of the simulations, describing the impact
Fare Adjustment has on an airline's revenue when it is used in the revenue management
system (Chapter 4) and also when it is used in the reservation system (Chapter 5). In
addition, we will also compare the results of using the Fare Adjustment in selected
paths/markets that offer a less restricted set of fare products with the use of Fare
Adjustment in all paths/markets in a mixed network.
-58-
4.
RESULTS OF FARE ADJUSTMENT IN RM SYSTEM
In this chapter, we will be looking at results of the implementation of fare adjustment
with pick-up forecasting in each of the three RM methods - DAVN, EMSRb and HBP.
The first half of the chapter presents the findings when FA is simulated in Network D
using both fixed and variable FRAT5s to calculate psup. The second half of the chapter
will then detail the benefits of fare adjustment when used in a larger, more competitive
network - Network S.
4.1
NETWORK D
In this section, we take a systematic look at the performance of fare adjustment with
standard forecasting in Network D. Given that leg-based revenue management is widely
used in the industry, we will be using a baseline environment where both of the
competitors employ leg-based EMSRb with standard forecasting. We will then use this
environment to look at the incremental benefits of fare adjustment when used with
EMSRb, DAVN and HBP.
Table 4-1 and Figure 4-1 show the revenue and fare class mix of the two airlines in the
base case scenario. Since both airlines are using leg-based EMSRb, it is not surprising
that there is not too much difference in revenue and fare class mix between the two
airlines.
Table 4-1: Results of Base Case Scenario
Airline
AL1
AL2
Revenue
Load Factor (%)
Yield ($/RPM)
$1,029,823
$1,025,096
83.61
83.2
0.1004
0.0967
35
DA1
30
. A2
25
20
i5
10
5E
1
2
4
3
5
Figure 4-1: Base Case Scenario - Fare Class Mix
-
59
-
6
4.1.1
DAVN
Previous studies such as Cl6az-Savoyen 8 and Reyes 9 have concentrated on applying fare
adjustment to DAVN in Network D. Therefore, before we present our results of fare
adjustment in other revenue management strategy, we will first take a look at the results
for DAVN. As shown in Table 4-2, by changing its seat allocation optimizer from the
leg-based EMSRb to DAVN, Airline 1 experiences a 1.01% increase in revenue.
Table 4-2: Results of DAVN in Network D
Airline
Revenue
Load Factor (%)
Yield ($/RPM)
AL
$1,040,271 (+1.01%)
82.24
0.1031
AL2
$1,028,321 (+0.31%)
81.24
0.0994
The introduction of fare adjustment with standard pick-up forecasting however, did not
help to further increase the revenue (Figure 4-2). Rather, it results in a fall in revenue and
as fare adjustment becomes more aggressive, Airline 1 loses the revenue gain it obtained
from the introduction of DAVN and starts to fare worse than the base case of leg-based
EMSRb.
FA Scaling Factor
.
E LU'
0 Cn
1.5%
1.0%-
*e 0.5%
-
0.0%
-0%C-0 -0.5%
0.0
0.4
0.2
n.o0.
OR.
-1.0%
-1.5%
r---------FRATS Scaling Factor
0
FRAT5 C
0.2
0.4
0.6
0.8
1.00%
0.86%
0.71%
-1.06%
Figure 4-2: DAVN with FA in Network D
The above results from DAVN are in-line with that of previous research 8 ' 9. However,
previous studies did not expand the use of fare adjustment with pick-up forecasting to
that of other seat allocation optimizers such as EMSRb and HBP. Therefore, in the
following sections, we will be presenting the results of such scenarios.
-
60-
4.1.2
EMSRb with Path Forecasting
As described in Section 3.3.3, in order to make use of Fare Adjustment, ALl has to make
use of EMSRb with path forecasting so that we are able to calculate the weighted average
adjusted fares for all the path/classes in each fare bucket. Thus by changing to path
forecasting, ALl experiences a 0.27% increase in revenue as compared to the base case
scenario with leg forecasting (see Table 4-3). Airline 2 also benefited from ALl's change
in strategy, experiencing an even greater revenue increase of 0.93% due to the increase in
FC6 loads (Figure 4-3). As ALl allocates more seat inventory to the higher fare classes
with the use of path forecasting, it rejects more passengers from the lower fare classes
and these passengers are then picked up by AL2.
Table 4-3: Results of EMSRb with Path Forecasting in Network D
Yield ($/RPM)
Load Factor (0/)
Revenue
Airline
ALI
$1,032,641 (+0.270/)
79.48
0.1059
AL2
$1,034,679 (+0.93/o)
85.69
0.0948
35 MAI
30 2520
1510
*A2
,
I
'
5
1
2
3
4
5
6
Figure 4-3: EMSRb with Path Forecasting in Network D - Fare Class Mix
4.1.3
EMSRb (Path) with Fare Adjustment
In the first series of fare adjustment of simulations, Airline 1 uses a fixed FA FRAT5
throughout the 16 successive time frames. As observed from Figure 4-4, ALl's revenue
increases with the use of slightly more aggressive FA FRAT5s. However, as the FRAT5s
get even more aggressive, ALl's revenue starts to decrease very rapidly.
-61-
FA FRAT5s
1.0%-
cc
(
0.0%
1.00
E -1.0%
0
=
v
-2.0%
1.2
1.50
2.0
.
Path-Based
(No FA)
-3.0%
-4.0%
I
I
FA FRAT5s
I 1.00 I
1.2
1.8
2.0
Rev Change from BASE
0.27%
0.40%
-0.45%
-2.92%
Figure 4-4: EMSRb with FA in Network D - Fixed FRAT5s
This increase and subsequent fall in revenue can be explained by taking a closer look at
the adjusted fares, load factor and yield of ALl. As presented in Table 4-4, the more
aggressive the FA FRAT5s, the greater the psup and PE cost, resulting in a lower
adjusted fare for each fare classes (other than Fare Class 1).
Table 4-4: Adjusted Fares with Fixed FRAT5s
Fare Class
1
2
3
4
5
6
Actual Fares
$412.9
$293.3
$179.0
$153.0
$127.1
$101.1
FA FRAT5 = 1.2
$412.9
$291.1
$176.4
$134.9
$108.9
$82.9
FA FRAT5 = 1.5
$412.9
$264.2
$148.6
$92.3
$66.3
$40.4
FA FRAT5 = 1.8
$412.9
$226.2
$110.1
$48.9
$22.9
-$3.1
Given the adjusted fares, ALl would therefore protect more seats for high-yield
passengers and rejecting the lower fare classes' demand. This thus results in an increase
in loads for the higher fare classes (FCl to FC3) and a fall in loads for the lower fare
classes (FC5 and FC6) as shown in Figure 4-5. In addition, we also see in Figure 4-6 that
as FA FRAT5s becomes more aggressive, the load factor for FC6 ALl's load factor starts
to fall while yield increases. However, with moderate FA FRATs, the increase in yield
compensates for the fall in load factor thus resulting in the increase in revenue for ALl.
Hence, ALl can increase its base case revenue by 0.61% with the use of a fixed FA
FRAT5 of 1.5.
-
62
-
Al Fare Class Mix
*2
E1.8
01.5
01.2
30
25.
20
10
5
0
6
5
4
3
2
1
Figure 4-5 Airline 1 Fare Class Mix - EMSRb with FA (Fixed FRAT5s)
80
0.140
-
85
-
-0.130
75 -
-0.120
0.110
65
- 0.100
60
-U1-
55
---
Load Factor
1.00
1.2
1.50
0.080
i
i
50 - --
0.090
Yield
1.8
2.0
Figure 4-6: Load Factor and Yield - EMSRb with FA (Fixed FRAT5s)
Although fixed FA FRAT5 is easier to administer and use, it would be more realistic if
one were to make use of a variable FRAT5 to account for the increase in willingness-topay as we get closer to the date of departure (see Section 3.3.1). Therefore, another series
of simulations was run to investigate the impact on revenues when variable FRAT5s are
used instead.
With the use of variable FRAT5s, the adjusted fares now vary across the timeframe. As
the scaling factor increases (from 0.2 to 0.8), the fares are adjusted lower with the
magnitude of fare decrease being greater as fare adjustment becomes more aggressive.
The lower fare classes also experiences a sharper drop in fares as compared to the higher
fare class as illustrated in Figure 4-7.
-
63 -
350
180
300
160
250
200
140
120
100
F
150O
80
100
50
20
0
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16
1
2
3
4
5
6
7
8
9
10 11 12 13 14 15 16
120
100
80
Figure 4-7:
Selected Adjusted Fares at FRAT5 C
60
4"FC 6
20
0
1
2
3
4
5
6 7
8
9 10 11 12 13 14 15 16
And from Figure 4-8, we can see the use of variable FRAT5s produces the same trend in
revenue increases for ALl. As we increase the scaling factor (which results in a more
aggressive variable FA FRAT5), ALl's revenue start to increase up to a certain point
before the increase starts to taper off and revenue starts to fall. With the use of FRAT5 C
and a scaling factor of 0.4, we are able to achieve a 0.77% increase in revenue for ALl as
compared to the base case. This is also slightly higher (+0.15%) than when fixed FA
FRAT5 is used.
FA Scaling Factor
2.0%
1.0%0.0%
-1.0% -
0.2
0.4
0..
-2.0%
-3.0%
-4.0%
-5.0%
-6.0%-
FRAT5
Scaling Factor
FRAT5 C
0
0.2
0.6
0.8
1.0
0.27%
0.44%
0.72%
-0.16%
-4.85%
Figure 4-8: EMSRb with FA in Network D - Variable FRAT5s
-64-
4.1.4
HBP
The use of fare adjustment with EMSRb (which uses booking limits as a mechanism for
inventory control) has shown positive results. This section presents the results when ALI
uses HBP instead. By just changing the optimizer to HBP, ALI is able to increase its load
factor and thus increase its revenue by 1.42% from the base case (Table 4-5).
Table 4-5: Results of HBP in Network D - Leg Forecasting
Load Factor (%)
Airline
AL
Revenue (vs. BASE)
$1,044,496 (+1.42%)
88.33
AL2
$1,022,558 (-0.25%)
80.55
Yield ($/RPM)
0.0964
0.0996
However, like EMSRb, we need to use path forecasting in order to implement fare
adjustment in HBP. Although the use of path forecasting still results in higher revenue for
ALl as compared to base case, the revenue increase is slightly lower than that of leg
forecasting as the higher yield is offset by the lower load factor (see Table 4-6).
Table 4-6: Results of HBP in Network D - Path Forecasting
Yield ($/RPM)
Airline
Revenue (vs. BASE)
Load Factor (%)
AL
$1,044,047 (+1.38%)
81.64
0.1042
AL2
$1,024,566 (-0.05%)
84.40
0.0953
The introduction of fare adjustment to HBP with path forecasting also did not produce
much better results. With the use of fixed FA FRAT5, fare adjustment resulted in a fall in
revenue as compared to just HBP with path forecasting as shown in Figure 4-9.
FA Scaling Factor
2.0% IL
1.0%
0.0%Path-Based
2.0
1.75
1.50
1.25
1.00
0
SF2.0%A)
-3.0%-4.0%-5.0% --
FA FRAT5s
Rev Change from EMSRb
1
1.00
1.25
1.50
1.75
2.0
1.30%
0.48%
-2.29%
-3.55%
Figure 4-9: HBP with FA in Network D - Fixed FRAT5s
-65
-
The use of a variable FRAT5 on the other hand produced slightly better result of a 1.40%
increase in revenue from the base case as compared to a 1.38% increase when only HBP
with path forecasting is used (Table 4-10). However, regardless of the FRAT5 used, HBP
with fare adjustment was not able to produce greater revenue than just HBP with leg
forecasting.
FA Scaling Factor
2.0%
1.5% 1.0%
0.5%
0.0%
0.5%
0
0.8
S-1.0%
1.5%
-2.0%
-2.5%
FRAT5 Scaling Factor
0
0.4
0.6
0.8
FRAT5 C
1.38%
0.29%
0.02%
-2.06%
Figure 4-10: HBP with FA in Network D - Variable FRAT5s
This is because with the switch from leg-based HBP to path-based HBP (FA FRAT5 =
1.00), the forecasts are systematically higher and the seat control system thus protects
more of the higher fare classes. This causes a fall in FC6 loads as shown in Figure 4-11.
The use of Fare Adjustment further aggravates the problem, thus resulting in further
revenue decrease as the increase in yield was unable to offset the fall in total load factor
(see Figure 4-12).
Al Fare Class Mix
50
45
40
35
30
25
20
15
10
5
0
1
2
3
4
5
Figure 4-11: Airline 1 Fare Class Mix - HBP
-
66 -
6
0.14
-- 0.13
90-
8580 -
-0.12
75 - _ _
-
70-
0.11
0.10
6560-
0.09
-4-Yield
1 -0- Loads
0.08
1
50 -
Leg
0.0
0.4
0.2
0.6
0.8
Figure 4-12: Airline l's Loads and Yield - HBP
4.2
NETWORK S
As presented in the previous section, in a head-to-head competitive environment, fare
Adjustment with standard forecasting has shown some positive results when used with
EMSRb. The introduction of RM system fare adjustment to DAVN and HBP however
did not produce any revenue gains in such a situation.
In this section, we will look at the incremental benefits of fare adjustment in a more
complex and non-symmetric network. Thus, the base case will not call for all of the
airlines to use the same revenue management system. The focus airline will still be
Airline 1 (MSP) with EMSRb being its baseline RM method. The "traditional"
competitors, Airline 2 (ORD) and Airline 4 (DFW), will use DAVN while Airline 3
(MCI) the low cost carrier will use a threshold revenue management method called
AT90 65.
With such an environment, MSP faces close competition in most of its connecting
markets from ORD which uses a more advance revenue management system and it also
sees low cost competition in just over half of its connecting markets and some of its local
markets from a smaller competitor (MCI) with a relatively simplistic revenue
management system. DFW then functions to provide more capacity in the market so as to
avoid feedback effects (which are prevalent in Network D).
AT90 is a adaptive threshold revenue management method that progressively closes down fare classes on
a leg as they reach the target load threshold, which in this case is a leg load factor of 90. More details on
threshold revenue management method can be found in Cleaz-Savoyen.
65
-
67 -
Base Case Revenues
2100000
1900000
1700000-1500000
1300000--
1100000-900000-700000
1,939,977
MSP
2,013,603
ORD
MCI
Airline
Load Factor
Yield
MSP
86.66%
0.1399
ORD
90.78%
0.1295
MCI
87.64%
0.1224
DFW
90.52%
0.1232
DFW
Figure 4-13: Base Case Results - Network S
The result of the base case is shown in Figure 4-13. Given its network and more
advanced revenue management system, ORD has the highest revenue amongst the 4
airlines. MSP comes in a close second in terms of revenue with its strong yields making
up for the low overall load factor.
4.2.1
EMSRb with Path Forecasting
As in Network D, in order to make use of Fare Adjustment, MSP has to make use of
EMSRb with path forecasting. With the revenue management system of the other airlines
remaining unchanged, MSP experiences a 1.04% increase in revenue as compared to the
base case scenario when leg-based EMSRb was used (see Figure 4-14). Despite a drop in
yield, ORD and DFW managed to increase their load factor - by capturing more spill-in
at the lower fare classes from MSP - and thus benefited from MSP's change in strategy,
with revenue increase of 0.31% and 0.14% respectively.
-68
-
1.20%.
1.04%
0.70%0.31%
C
0.14%
0.20%-
ORD
MSP
-0.30%
DFW
--
0.28%
Load Factor (%)
Yield ($/RPM)
Airline
Revenue
AL
$1,960,102
84.09
0.1456
AL2
$2,019,844
91.47
0.1289
AL3
$970,443
87.67
0.1220
AL4
$1,290,311
91.02
0.1227
Figure 4-14: EMSRb with Path Forecasting - Network S
4.2.2
Path-based EMSRb with Fare Adjustment
Since Network S is a mixed fare network with two fare structures (as described in Section
3.2.2), we will make use of this opportunity to expand our investigation and look not only
at the impact of fare adjustment when it is introduced to all the markets in MSP's
network, but also when it is used solely in the markets with LCC competition. In
addition, given that previous results from Network D had shown that variable FRAT5s
are producing better results than when fixed FRAT5s are used, the rest of the simulations
in this chapter will be run using variable FRAT5s.
From Figure 4-15, we see that when fare adjustment is introduced to all the markets in
MSP's network, it helps to further improve the revenue when used in moderation. By
using variable FRAT5s and a scaling factor of 0.4, MSP was able to obtain a 1.84%
increase in revenue as compared to the base case and a 0.8% increase from EMSRb with
path-forecasting alone. These results are similar to that observed in Network D where the
improvement in revenue from path-forecasting is 0.5%.
Similar results are obtained when fare adjustment is only applied to LCC markets (Figure
4-15). However, given that we are influencing only a portion of MSP's markets, we see
that a higher scaling factor of 0.6 is required to achieve the same magnitude of revenue
increase (+1.80%). In addition, the drop in revenue is less acute when fare adjustment
gets too aggressive.
-
69
-
FA Scaling Factor
3.0%
S2.0%-
U-
1.0%
S0.0%_
0.0
S-1.0%1.
S-2.0%
0.2
0.4
0.6
0.8
1.0
Path-Based
(No FA)
-3.0%
-4.0%
-0-A
Kftrkets
-
LCC N rkets
-5.0%
FRAT5 Scaling Factor
1
0
0.2
All Markets
1
1.04%
1.35%
1.04%
1.36%
LCC Markets Only
0.4
0.6
0.8
1.0
1.62%
-2.87%
-4.35%
0.15%
-1.43%
1.620%
Figure 4-15: EMSRb with FA in Network S - Variable FRAT5s
4.2.3
HBP with Fare Adjustment
Fare adjustment has once again proved to be useful in increasing revenue when used with
path-based EMSRb. This section presents the results when ALl uses HBP in a complex
and competitive environment. By changing the optimizer to HBP, ALl is able to increase
its load factor and thus increase its revenue by 0.87% from the base case (Table 4-7).
Table 4-7: Results of HBP in Network S - Leg Forecasting
Airline
Revenue
Load Factor (%)
Yield ($/RPM)
AL
$1,956,849 (+0.87/o)
88.92
0.1375
AL2
$1,994,114 (-0.97%)
89.72
0.1298
AL3
$972,337 (-0.09%)
87.44
0.1226
AL4
$1,283,191 (-0.42%)
90.49
0.1228
However, as per EMSRb, we need to make use of path forecasting in order to implement
fare adjustment in HBP. And again, as what was observed in Network D, we find that
although the use of path forecasting still results in higher revenue for ALl as compared to
the base case, the increase is slightly lower than when HBP with leg-forecasting is used
(Table 4-8).
-
70 -
Table 4-8: Results of HBP in Network S - Path Forecasting
Revenue
Load Factor (%)
Yield ($/RPM)
AL1
$1,942,718 (+0.14%)
79.06
0.1535
AL2
$2,038,391 (+1.23%)
92.60
0.1285
Airline
AL3
$977,093 (+0.40%)
88.27
0.1220
AL4
$1,302,864 (+1.110/o)
91.95
0.1227
However, unlike Network D, when fare adjustment is introduced to path-based HBP in
Network S, it produced much better results than both path and leg based forecasting.
With the use of variable FRAT5s and moderate scaling factors, MSP experiences a
1.40% increase in revenue when fare adjustment is applied to all the markets and a 1.46%
increase in revenue when it is used only in LCC markets (see Figure 4-16).
FA Scaling Factor
2.0%
j0.0%_E
0.1
0.0
-1.0%
Path-Based
-2.0%
(No FA)
-3.0%
0.2
-U-All Markets
0.4
8
.
-4-LCC Markets
-4.0%-
FRAT5 Scaling Factor
1
All Markets
LCC Markets
I
0
0.1
0.14%
1.33%
0.14%
1.37%
0.2
1. 4 3%
0.4
0.6
0.8
1.01%
-0.26%
-3.17%
146%
0.69%
-0.85%
Figure 4-16: HBP with FA in Network S - Variable FRAT5s
But why is there a difference in the performance of path-based HBP with fare adjustment
in Network D and Network S? When path-based forecasting is introduced in Network D,
it causes Al to reject the lower fare class demand and protect more seats for higher fare
classes. The rejected lower fare class passengers thus get "picked up" by Airline 2, given
a two-carrier environment and Al hence sees a lower spill-in of FC6 passengers as shown
in Figure 4-17.
-71-
Network D6: Al Spill-In Fare Class Mix Trend
70000
60000
50000
-4-FC 1
FC 2
FC 3
40000
30000
20000
-u-FC 4
-*-FC 5
10000
-FC
6
0
Leg
Path
0.2
0.4
0.6
Network S: Al Spill-in Fare Class Mix Trend
89000
79000
69000 -FC
1
FC 2
49000
-e-FC 4
39000
--
29000
--
FC 5
FC
6
19000
Leg
Path
0.1
0.2
0.4
Figure 4-17: Al Spill-in Fare Class Mix - Network D vs. Network S
As a result, Al experiences a greater spill-in of FC1 passengers as A2's load factor
increases and thus, is unable to accommodate more high-paying passengers closer to the
date of departure. The introduction of fare adjustment further encouraged this course of
action for Al. However, the increase in yield from the increase in FC1 spill-in was
unable to offset the fall in total load factor from the fall in FC6 spill-in. Thus path-based
fare adjustment was not able to produce better results than that of a leg-based HBP.
On the other hand, path-based forecasting (FA FRAT5 = 0.0) in Network S was so
aggressive in protecting the higher fare classes that it resulted in a huge increase in the
spill-in of FCl and FC2 as shown in Figure 4-17. However, the increase in yield could
not offset fall in overall load factor (mainly from FC6 as shown in Figure 4-18). This thus
resulted in a fall in revenue as compared to leg-based HBP.
As mentioned in Section 4.1.4, path forecasting results in higher forecasts as compared to
leg forecasting. These higher forecasts translate to higher bid prices when HBP is used
and thus more protection for the higher fare class seats. The introduction of fare
adjustment in Network S however, helped to reduce this over aggressiveness of pathbased HBP. Even though we still have the same higher forecasts with fare adjustment, by
taking into account the risk of buy-down, fare adjustment results in lower bid prices and
thus open up more seats at the lower fare classes.
-
72
-
By reducing the spill-in of FC1 (to a level that is still higher than that of a leg-based
HBP) and increasing the load of FC2 to FC4, fare adjustment was able to adjust the fare
class mix such that the drop in yield was more than compensated with a greater increase
in load factor (see Figure 4-19), thus resulting in a revenue increase.
Al Fare Class Mix
60
eg
50
E0.0
00.2
00.1
*0.4
40
30
20
10
0
6
5
4
3
2
1
Figure 4-18: MSP Fare Class Mix - Path based HBP with FA in Network S
0.19
0.18
0.17
0.16
0.15
0.14
9590 85 80
7570--
65
0.13
0.12
U
-0-Yield
-U--Load Factor
60
55
0.11
0.10
l
50
Leg
0.0
0.1
0.4
012
0.6
0.8
Figure 4-19: MSP Yield and Load Factor - Path based HBP with FA in Network S
4.2.4 DAVN with FareAdjustment
Having looked at the performance of fare adjustment when used with EMSRb and HBP,
we now move to DAVN. If MSP were to just switch to using a more advanced revenue
management method of DAVN instead of leg-based EMSRb, it will experience a 1.89%
increase in revenue as its load factor increases (Figure 4-20). The rest of the airlines on
the other hand will suffer a drop in revenues from 0.60% to 1.13% as they lose
passengers to MSP.
-
73
-
1.89%
1.80%
1.30%
0.80%0.30% -0.20% -
MSP
ORD
-0.70%-0.74%
-0.60%
-1.20%
Airline
-1.13%
Revenue
Load Factor (%)
Yield ($/RPM)
AL1
$1,976,586
89.58
0.1379
AL2
$1,990,814
89.28
0.1302
AL3
$965,955
87.04
0.1224
AL4
$1,280,778
89.26
0.1242
Figure 4-20: DAVN Results - Network S
With fare adjustment, MSP's revenue is further increased by about another 1%, with the
introduction of fare adjustment to all markets producing slightly better results (+3.05%)
than when fare adjustment is applied to the LCC markets only (+2.81%). And as
illustrated in Figure 4-21, we see the same trend in that the change in revenue when fare
adjustment is applied to LCC markets is less acute than when it is applied to all markets.
FA Scaling Factor
E LU
0)
V42<
.>
M U
T--
3.5%
3.0%
2.5% 2.0% -
I
-
S 1.5%
1.0%
0.5%
0.0%
UJ
-050/6
-(N o FA)II
-. 0.0
FRAT5 Scaling Factor
All Markets
LCC Markets
All Markets -*-LCC Markets
0.2
0
1 1.89%
|
1.89%
0.4
0.2
0.6
0.4
0.8
0.6
0.8
1 2.45%
2.42%
-0.09%
I
2.47%
1.86%
2.36%
Figure 4-21: DAVN with Fare Adjustment in Network S
-74-
4.3
CHAPTER SUMMARY
In this chapter we have presented the results of simulations focusing on the impact Fare
Adjustment with standard forecasting has on the revenue of an airline using EMSRb,
HBP and DAVN. And based on the results of our simulations, it appears that even
without the use of more sophisticated forecasting techniques, such as Q- or hybrid
forecasting, the performance of an airline can be improved through the application of
Fare Adjustment in the RM system.
In Section 4.1, we conducted simulations in a simplified head-to-head environment Network D. A summary of the results obtained in Network D is presented in Table 4-9
and Table 4-10. Our experiments indicated that in such a network, the gain from the
introduction of fare adjustment with standard forecasting to a revenue management
system can approach 0.5%.
Table 4-9: Al Revenue Improvement over Leg-Based EMSRb in Network D
Al RM System
Leg-Based EMSRb (BC)
Path EMSRb with FA
Path HBP with FA
DAVN with FA
Al Revenue
$1,029,823
$1,037,763
$1,044,266
$1,040,117
A Revenue From BC
--+0.77%
+1.40%
+1.00%
Table 4-10: Al Revenue Improvement from Fare Adjustment in Network D
Al RM System
Path EMSRb
Path HBP
DAVN
Al Revenue
without FA
Al Revenue
with FA
A Revenue From
No FA
$1,032,641
$1,044,047
$1,040,271
$1,037,763
$1,044,266
$1,040,117
+0.50%
+0.02%
-0.01%
In Section 4.2, we applied the fare adjustment technique in a more realistic and thus more
complex and asymmetric environment - Network S. The results here were even more
encouraging as Fare Adjustment is showed to be able to improve an airline's revenues by
approximately 1% over the standard revenue management methods of path-based
EMSRb, path-based HBP and DAVN.
-
75
-
INAN MM", '=--
-
-
__ -.
, -_
--
.- - -__ -,
I
___
In particular, by taking into account the risk of buy-down and thus lowering the bid
prices, Fare Adjustment was able to reduce the over aggressiveness of path-based HBP
(caused by the higher forecasts) as compared to leg-based HBP. This helped to open up
more seats at the lower fare classes and increased the load factor, thus resulting in a
1.31% increase in revenue for Al when it introduces FA to path-based HBP. A summary
of the results obtained in Network S are presented in Table 4-11 and Table 4-12.
Table 4-11: Al Revenue Improvement over Leg-Based EMSRb in Network S
Al Revenue
Al RM System
Leg-Based EMSRb (BC)
Path EMSRb with FA
Path HBP with FA
DAVN with FA
A Revenue From BC
$1,939,977
$1,975,591
$1,968,206
$1,999,053
--+1.84%
+1.46%
+3.05%
Table 4-12: Al Revenue Improvement from Fare Adjustment in Network S
Al RM System
Path EMSRb
Path HBP
DAVN
Al Revenue
with FA
$1,975,591
$1,968,206
$1,999,053
Al Revenue
without FA
$1,960,102
$1,942,718
$1,976,586
-
76 -
A Revenue From
No FA
+0.80%
+1.31%
+1.14%
RESULTS OF FARE ADJUSTMENT IN RESERVATION SYSTEM
5.
In Chapter 4, we showed that Fare Adjustment with standard forecasting is capable of
improving the revenue performance of an airline when it is used in the revenue
management system. In this chapter, we will be looking at the alternative of using fare
adjustment in the reservation (RES) system for an airline that is using EMSRb or HBP.
The first half of the chapter presents the findings when FA is simulated in Network D
using both input psup and FRAT5s to calculate psup. The second half of the chapter will
then detail the benefits of fare adjustment in the RES system when used in a larger, more
competitive network - Network S.
NETWORK D
5.1
In this section, we take a systematic look at the performance of RES fare adjustment with
pick-up forecasting in Network D. Again, as with our study of RM fare adjustment, we
will use a baseline environment where both of the competitors employ leg-based EMSRb
with pick-up forecasting (See Table 4-1 for baseline revenues, load factors and yields).
We then make use of this environment to look at the incremental benefits of RES fare
adjustment when used with EMSRb and HBP.
Introduction of RES Bid Price to Leg-Based EMSRb
5.1.1
In order to make use of fare adjustment in the reservation system, we first need to be able
to make use of bid prices in the reservation system. As shown in Figure 5-1, the
introduction of RES bid price to Airline 1 (using leg-based EMSRb) causes an increase in
its overall load factor which in turn resulted in a 2.01% increase in revenue.
Revenue
Load Factor (%)
Yield ($/RPM)
AL1
$1,050,531(+2.01%)
86.59
0.0989
AL2
$1,017,556 (-0.74%)
81.55
0.0979
Airline
Airline 1 Fare Class Mix
Airline 2 Fare Class Mix
40
40.
a Base
35 -
35 -
ORRs
30-
30
25-
25
20
20
0 Base
ERes
2
3
15
4n -
10.
1
2
1
3
Figure 5-1: RES Bid Price in Network D
-
77 -
4
5
6
With a leg-based seat inventory control such as EMSRb, the closing or opening of a fare
class affects both the local and connecting paths within that particular fare class.
However, with EMSRb bid price, there is the ability to differentiate between the
connecting paths that have higher total fares and the local paths that have lower total
fares within a fare class. Thus, one is able to close down a local path that is from a higher
fare class (but has a lower total fare) while keeping a connecting path of a lower fare
class (but higher total fare) open. As illustrated in Figure 5-2, when RES bid price is
introduced to leg-based EMSRb, Airline 1 is able to capture more spill-in for FC5 and
FC6. In addition, there is a 3% to 4% increase in connecting passengers (Figure 5-3).
Horizontal Spill in Observed
6000050000
40000
30000
20000
10000
0
Base Al
Res Al
Base A2
Res A2
Vertical Spill In Observed
7000
-A-F1-N
C
C
-a
C
-e
o
C
C
6000
5000
4000
3000
2000
1000
0
Base Al
Res Al
Base A2
Res A2
Figure 5-2: Airline 1 Spill in Fare Class Mix in Network D - RES Bid Price
EMSRb (BASE)
With RES FA
c V
:ZK4
Figure 5-3: Local vs. Connect Passengers in Network D - RES Bid Price
-78-
5.1.2 Leg-based EMSRb with RES FareAdjustment
In the first series of RES FA of simulations, Airline 1 uses a fixed FA FRAT5 throughout
the 16 successive time frames to calculate the psup (as described in Section 3.3.2). As
observed from Figure 5-4, ALl's revenue increases with the use of more aggressive FA
FRAT5s. However, there is a threshold in terms of the aggressiveness and once we
exceed that threshold (in this case FA FRAT5 = 1.7), the revenue starts to drop very
rapidly.
FA Scaling Factor
o
4.0%
3.5%
U
3.0%
4) 4
o
.C
-
2.5%
W
2.0%1.5% 1.0%
0.5%
() .
2
0.0%
No FA
FA FRAT5
1
Rev Change from BASE
1.4
1.5
1.6
2.07%
2.53%
3.26%
1.2
1.0
1 2.10%
2.01%
1.8
1.7
1.6
1.5
1.4
1.2
1.0
1.8
1.7
0.81%
Figure 5-4: EMSRb with RES FA in Network D- FRAT5psup
This improvement in revenue can be attributed to the increase in FC1 and FC2 loads
which increases the yield of Airline 1. However, as seen from Figure 5-5, the major
improvement in revenue comes when fare adjustment becomes aggressive enough to
causes the RES system to protect more FC5 seats than FC6 (reversing the greedy effects
of RES bid price). This further helped to improve the yield such that the increase in yield
more than offset the fall in overall load factor and thus results in an additional increase in
revenue of 1.47% (from RES bid price alone) when a FA FRAT5 of 1.7 is used.
0.130
90
85
-- 0.125
-0.120
--0.115
- 0.110
--
80
6
65
-
60
|
1.0
1.2
Load Factor -4-Yield_
I
I
1.4
1.5
1.6
Ii
1.7
91.0
4m
m 1.2 c3 1.4 a 1.5 m 1.6 m 1.7
=1.8
40
35
30
0.105
-- 0.100
- 0.095
--0.090
-- 0.085
0.080
75
70
50
15
10
5
1
1.8
2
3
4
5
6
Figure 5-5: LF, Yield & Fare Class Mix - EMSRb with RES FA (FRAT5 psup) in D
-
79
-
However, as explained in Section 3.3.2, the use of FRAT5s to calculate sell-up means
that we are not able to increase the psup of the higher fare classes without affecting the
psup of the lower fare classes. Therefore, another series of RES FA simulations were run
with input psup, which are user-defined to be uniform across the different fare classes
and timeframes.
The change to input psup in this case however, did not result in significant increase in
revenue as compared to FRAT5 psup. With a change to input psup, Airline 1 now
experiences a 3.55% increase in revenue as compared to the base case (Figure 5-6),
which is only 0.07% higher than FRAT5 psup. In addition, a similar trend is observed for
the trend in revenue as one gets more aggressive in protecting the higher fare classes.
Input psup
4.0%
3.0%
I0
z
2.0%
E
0
1.0%
0)
0.0%
0
-1.0%
>
-20%
0.1
0.2
0.3
0.4
0.5
0.6
0.
0.8
No FA
-3.0%
0
Input psup
Rev Change from
BASE
.
2.
0.1
0.2
0.3
0.5
0.6
0.7
0.8
2.38%
3.21%
3.29%
3.50%
0.41%
-0.34%
-2.10%
Figure 5-6: EMSRb with RES FA in Network D - Input psup
Yet, a look at the loads and yields shows the slight difference in how the input psup
approach affects the seat allocation problem. Whereas FRAT5 psup improves the overall
revenue by aggressively increasing yield and sacrificing load factor, input psup on the
hand maintains the load factor and takes advantage of just a slight increase in yield as
illustrated in Figure 5-7.
This is because when FRAT5s are used, the lower fare classes end up with very large
psups (see Table 3-6) and as FA gets more aggressive, more of the lower fare classes are
closed down (in anticipation of more high yield passengers), resulting in a fall in load
factor. With input psup however, the same psups are used across all the fare classes.
Therefore, any increase in the aggressiveness of FA affects all the fare classes and the
impact on the load factor is thus less than that of FRAT5 psups.
-
80
-
0.130
90
- --
85
0.125
-0.120
-0.115
80
.110
-
0.105
75
--
-
70
0.100
-- 0.095
65 --
- n-Loads
f
-0.090
Yield
-6-
--
0.085
60
0.2
0.1
0
0.4
0.3
0.5
0.6
Figure 5-7: Al's LF & Yield - EMSRb with RES FA (Inputpsup) in D
5.1.3
Leg-Based HBP with RES FareAdjustment
In Table 4-5, we have seen that if Airline 1 were to switch from leg-based EMSRb to legbased HBP in Network D (Section 4.1.4), its revenue will increase by 1.42%. And as was
observed with EMSRb, the introduction of the RES bid price (FA FRAT5 = 1.0) to legbased HBP results in an additional 0.93% increase in revenue, bringing the total revenue
improvement from the base case to a total of 2.35% as shown in Figure 5-8. Given that
the HBP local paths are controlled by the booking limits, the use of RES bid price allows
the system to close off local paths with lower fares thus leaving more seats for the
connecting paths with higher fares.
3.0%
2.5%
I
-
4
2.0%
1.5%
1.0%
----....- - ...-----.- - -- -- - -- -- -- - --
HBP without
RES Bid Price
(See Table 4-4)
0.5%
N o FA
0.0%
i1o
1.1
-0.5%
FA FRAT5
Rev Change from
BASE
1.2
1.o
23%23%
IL
1.3
1.1
1.4
1.2
1.5
1.6
1.7
1.3
1.4
1.5
1.6
1.7
1.8
2.21%
1.88%
1.89%
1.95%
1.80%
-0.35%
Figure 5-8: HBP with RES FA in Network D - FRAT5 psup
81
-
However, the implementation of RES Fare Adjustment using FRAT5 psup in this case
does not produce as great an increase as when EMSRb is used. Rather, at the best
scenario of FA FRAT5 = 1.2, Airline 1 will only see an additional benefit of 0.05% from
the introduction of fare adjustment to the RES bid price system (Figure 5-8).
The use of RES Fare Adjustment with input psup on the other hand, produces more
positive results. As illustrated in Figure 5-9, with an input psup of 0.3, Airline 1
experiences a 3.54% increase in revenue over the base case, with fare adjustment
accounting for 1.19% of the increase.
I
Input psup
4.0%
3.0%
2.0%
No FA --
1.0%
a)E
0
- - --
HBP without
RES Bid Price
(See Table 4-4)
0.0%
-1.0%
2.0%
a)
No FA
-3.0%
4.0%
5.0%
Input psup
0
0.1
0.2
0.4
0.5
0.6
0.7
0.8
Rev Change from
BASE
2.35%
2.72%
3.25%
3.51%
3.03%
0.42%
-0.92%
-4.82%
Figure 5-9: HBP with RES FA in Network D - Input psup
This difference in outcome can once again be explained by how the two methods (of
obtaining psup) approach the seat allocation problem. With FRAT5 psup, there is
overprotection of the higher fare classes (See Figure 5-10) as the psup for FC6 increases
with the aggressiveness of FRAT5. This problem however, is averted with input psup.
From the fare class mix, we can see that the load factor for FC6 is maintained while there
is an increase in FC1 and 2.
Therefore, it is not surprising that with input psup, Airline l's load factor and yield are
more stable although there is a similar downward and upward trend respectively (see
Figure 5-11). Thus, there is a higher revenue increase when input psup is used as the
increase in yield is not offset by too great a fall in load factor.
-
82
-
Flat FRAT0
50
O 1.1
a Bid Price
45
O1.3 MO
o 1.2
1.5
1.4
35
30
25
20
15
10
40
5
0
6
5
4
3
2
1
Input psup
50
=
Bid Price
45
0.1
0
c3O.2
0.3
w
O.4
mO .5
0.6
0
35300
25200
1510
5
5
7
5
4
3
2
1
0.1
6
Figure 5-10: Fare Class Comparison - HBP with RES FA in Network D
FRAT5 psup
90
0.14
0.13
0.12
0.09
70-
w--Load
65
80 -
1
1.2
1.1
--
Yield
1
I
I
I
60
Factor
1.4
1.3
1.6
1.5
0.07
0.06
I
1.7
0.11
1.8 -
Input psup
90-
0.14
e~--0.13
85
80
-
--
75--
-0.09
-- 0.08
- -0.07
70-
65-0 -
-Las
0.0
0.1
-5-il
0.4
0.3
0.2
0.11
0.10
0.5
1
0.6
0.06
Figure 5-11: LF & Yield Comparison - HBP with RES FA in Network D
-
83
-
5.2
NETWORK S
As presented in the previous section, in a head-to-head competitive environment, RES
Fare Adjustment with standard forecasting has shown positive results when used with
both leg-based EMSRb and HBP. In addition, the use of input psup produces better
results than the use of FRAT5 to calculate psup. In this section, we move to a more
complex and non-symmetric network and we will be using the same baseline
environment as described in Section 4.2 and Figure 4-13.
5.2.1
Leg-based EMSRb with RES Fare Adjustment
With the introduction of the RES bid price, MSP sees a 1.49% increase in revenue when
RES bid price is applied to all its markets and 1.34% increase in revenue when it is used
only in markets with LCC competition (Figure 5-12). The use of RES fare adjustment
helps to further improve the revenue, bringing about an additional revenue increase of
0.43% to 0.57%. In addition, as was observed in Network D, there is a threshold in terms
of the aggressiveness of RES fare adjustment used. Once that threshold is exceeded, the
revenue starts to drop very rapidly, with the drop being more acute when RES fare
adjustment is applied in all the markets.
3.00%
00%
-
0.00%
-1.00%1 D
-2.00%
-
S
-3.00%
-4.00%
-5.00%
-
-4-All Mkts
---
LCC Only
-6.00%-
-7.00%
-8.00%
FA FRAT5
All Mkts
1.49%
LCC Only
1 1.34%
1
1.125
1.25
1.72%
1.89%
1.59%
1.77%
1.375
1.87%
1.5
1.75
2
1.83%
-0.02%
-7.01%
0.89%
-1.84%
Figure 5-12: EMSRb with RES FA in Network S - FRAT5 psup
Looking at the load factor and yield of Airline 1 when RES fare adjustment is applied to
all markets (Figure 5-13), we see the usual trend of falling load factors and rising yields
with more aggressive FA FRAT5s. In addition, we see the increase in FC6 loads when
RES bid price is introduced (FA FRAT5 = 1).
-84-
However, unlike Network D, RES bid price here also increases the loads for FC1 and
FC5 while decreasing the loads for FC2, FC3 and FC4. This thus causes an increase in
both the overall load factor and yield when RES bid price is used. The use of fare
adjustment helps to further increase the load of FC1 and FC5 while FC6 loads start to
fall. Thus the additional 0.43% revenue benefit of RES fare adjustment is achieved by its
strategy of sacrificing some of the loads for higher yields.
60
90
0.170
85
0.160
0 Base Ml 01.125 01.25 01.375
50
40
-- 0.150
80-
7
0.140
Base
1 -5-Load Factor -- Yield
11
1.25
Base
1
70
1.125
1.5
1.25 1.375
0.130
1
0.120
0
1.75
3
2
1
5
4
6
Figure 5-13: Al's LF & Yield in S - EMSRb w RES FA in All Mkts (FRAT5 psup)
The use of input psup also produced positive results although the increase in revenue is
less than the use of FRAT5 psup. When applied to all the markets, RES FA with input
psup results in a 1.62% increase in Al's revenue over the base case while its use in LCC
markets resulted in a smaller increase of 1.52% (Figure 5-14).
1.80%
1.60%
S1.40%/
"
1.20%
S1.00% -
-
0.80% -
-
-4-All Mkts
0.60%
LCC Only
---
0.40%
0.4
0.3
0.2
0.1
0
- -r-----------l
psup
0
0.05
0.1
0.2
0.3
0.4
All Mkts
1.49%
1.59%
1,62%
1.56%
1.18%
0.56%
LCC Only
1.34%
1.52%
1.47%
1.48%
1.31%
0.96%
Figure 5-14: EMSRb with RES FA in Network S - Input psup
-
85
-
This finding is different from that of Network D where input psup had performed equally
if not better than FRAT5 psup. In Network D, the impact of Al's RES FA (with FRAT5
psup) causes A2 to pick up more of the FC6 spill and thus unable to capture more of the
higher fare class passengers closer to departure (Figure 5-15). Hence, Al was able to take
advantage of this and increase its yield which helped to offset the substantial drop in load
factor. Input psup on the other performed slightly better as it allowed for a more uniform
psup across the different fare classes and this is reflected in Figure 5-15 where one see a
more stable spill-in for the different classes. Thus, Al is able to maintain its load factor
while taking advantage of the increase in yields.
FRAT5: Al Spill-In Fare Class Mix
Input psup: Al Spill-in Fare Class Mix
80000
70000
60000
50000
-O-FC 1
FC 2
FC 3
40000
30000
FC 4
-)*- FC 5
-FC 6
2
10000
0
RES
1.2
1.5
1.6
80000
70000
20000
10000
-0-FC 5
0
RES
0.1
-0.FC
02
0.3
0
Input psup: A2 Spill-in Fare Class Mix
*FC 1
FC 2
FC3
--- FC4
-0-FC2
FC3
40000
-4- FC 4
-6-F04
E
FC3
0--FC6
20000
.FC6
RES
0.1
0.2
0.3
0.4
Figure 5-15: Spill-In Trend - EMSRb with RES FA (All Markets) in Network D
Network S on the other hand consists of more competitors with each competing airline
serving slightly different markets. Thus the impact of Al's RES FA (with FRAT5 psup)
on the spill-in of other airlines is less than that of Network D due to the network effect
(Figure 5-16). The use of FRAT5 psup in Network S hence produces better results than
input psup as it was able to capture more FC1 spill-in without losing the spill-in from
other fare classes.
-
6
0.4
--- FC1
20W(0
FC4
FC3
40000
80000-
40000
---
50000
1.7
--
FC1
60000
FRAT5: A2 Spill-In Fare Class Mix
6M00
--
86
-
Input psup: Al Spill-in Fare Class Mix
FRAT5: Al Spill-In Fare Class Mix
109000
--
89000
109000
FC1
-4-FC 1
89000
69000
69000
WFC3
49000
FC3
--I
29000
-l-FC5
4
9000
9000
RES
1.125
125
--fFO 4
49000
-4--FC4
29000
RES
1.375
2
SFC
FC 5
-- FC
0.2
0.1
0.3
0.4
Input psup: Other Airlines Spill-In
FRAT5: Other Airlines Spill-in
300000
300000
-4-FO 1
@-FC2
250000
200000
FO 3
150000
.FIC 4
50000
.-
RES
1.125
1.25
250000
-$-FC 1
200000
-
FC 2
FC3
150000
FC 6
-e-FC
RES
1.375
0.1
0.2
0.3
Figure 5-16: Spill-In Trend - EMSRb with RES FA (All Markets) in Network S
5.2.2
6
Leg-based HBP with RES Fare Adjustment
As previously shown in Section 4.2.3 and Table 4-7, by changing the optimizer to legbased HBP, Airline 1 experiences a 0.87% increase in revenue from the base case of legbased EMSRb. The addition of the RES bid price capability (FA FRAT5 = 1.0) further
boosts the revenue increase to 1.88% when RES bid price is applied to all its markets and
1.87% when it is used in markets with LCC competition (Figure 5-17).
And from Figure 5-17 and Figure 5-18, we see that RES FA with FRAT5 psup once
again outperforms input psup in terms of revenue increase. With FRAT5 psup, Al
obtains an additional 0.2% revenue increase from RES FA when it is applied to all the
markets and an additional of 0.69% revenue increase when RES FA is used in markets
with LCC competition (as compared to the 0.32% revenue increase achieved when input
psup is used).
-
87
-
6
FA FRAT5
4.00%
E
0
C
2.00%
@1
0.00%
-2.00%
.
W
>
-4.00%
-6.00%
~
-8.00%
a
--
-10.00%
All Mkts
-u--LCC Only
-12.00%
-14.00%
FA FRAT5
1
All Mkts
1.8 8%
LCC Only
1.8 7%
1.25
1.375
1.5
1.75
2
1.93%
-0.08%
-1.70%
-5.77%
-12.10%
2.48%
2.22%
0.42%
-2.89%
1.125
2.16%
Figure 5-17: HBP with RES FA in Network S - FRAT5psup
Input psup
2.50%
E
2.00%
0
C
1.50%
U
wg
1.00%
0.50%
All Mkts
-
-U--LCC Only
0.00%
0.50%
0.4
0.3
0.2
0.1
)
.5
-1.00%
FA FRATS
1
All Mkts
LCC Only
0
0.05
1.88%
i
1.87%
|
2.14%
0.1
0.2
0.3
0.4
0.5
1.99%
1.89%
1.81%
1.20%
-0.66%
2.11%
2.19%
2.01%
2.04%
1.37%
Figure 5-18: HBP with RES FA in Network S - Input psup
-
88
-
5.3
CHAPTER SUMMARY
In this chapter we have presented the results of simulations focusing on the impact
Reservation Fare Adjustment with standard forecasting has on the revenue of an airline
using EMSRb and HBP. Our results suggested that revenue improvements can be
achieved with this alternative application of fare adjustment in the reservation system.
In Section 5.1, we conducted simulations in a simplified head-to-head environment Network D. A summary of the results obtained in Network D is presented in Table 5-1
and Table 5-2. Our experiments indicated that in such a network, the introduction of RES
bid price alone produces revenue increase of 0.57% when HBP is used and 2.01% when
the airline is using EMSRb.
Further revenue gains of 1.16% to 1.51% can be achieved with the introduction of fare
adjustment with standard forecasting, bringing the total revenue benefit of RES FA to
approximately 3.5% as compared to the base case.
Table 5-1: Al Revenue Improvement over Leg-Based EMSRb in Network D
Al Revenue
Al RM System
$1,029,823
$1,050,531
$1,066,379
$1,048,031
$1,054,033
$1,066,288
Leg-Based EMSRb (BC)
EMSRb with RES Bid Price Only
EMSRb with RES FA
Leg-Based HBP
HBP with RES Bid Price Only
HBP with RES FA
A Revenue From BC
--+2.01%
+3.55%
+1.77%
+2.35%
+3.54%
Table 5-2: Al Revenue Improvement from RES Fare Adjustment in Network D
Al RM System
EMSRb with RES
HBP with RES
Al Revenue
without FA
Al Revenue
with FA
A Revenue From
No FA
$1,050,531
$1,054,033
$1,066,379
$1,066,288
+1.51%
+1.16%
In Section 5.2, we applied the fare adjustment technique in a more realistic and thus more
complex and asymmetric environment - Network S. Although the revenue improvements
here were slightly lower than in Network D due to the network effects, RES Fare
Adjustment still showed positive results.
-
89-
The introduction of RES bid price alone produces a revenue increase of 1.49% when the
airline is using EMSRb and 1% when HBP is used. Further revenue gains of 0.42% to
0.68% can be achieved with the introduction of fare adjustment with standard forecasting.
A summary of results obtained in Network S are presented in Table 5-3 and Table 5-4.
Table 5-3: Al Revenue Improvement over Leg-Based EMSRb in Network S
Al RM System
Leg-Based EMSRb (BC)
EMSRb with RES Bid Price Only
EMSRb with RES FA
Leg-Based HBP
HBP with RES Bid Price Only
HBP with RES FA
Al Revenue
$1,939,977
$1,968,882
$1,977,224
$1,956,849
$1,976,254
$1,989,640
A Revenue From BC
--+1.49%
+1.92%
+0.87%
+1.87%
+2.56%
Table 5-4: Al Revenue Improvement from RES Fare Adjustment in Network S
Al Revenue
without FA
Al Revenue
with FA
A Revenue From
No FA
EMSRb with RES
$1,968,882
$1,977,224
+0.42%
HBP with RES
$1,976,254
$1,989,640
+0.68%
Al RM System
-90
-
6.
CONCLUSIONS
In the beginning of this thesis, we looked at how traditional revenue management
methods relied heavily on the ability of booking restrictions (Saturday night stay
requirements, non-refundability, etc.) to fence potential demand into well-segmented fare
classes. However, with the growth of LCC and consequent fare compression and removal
of restrictions on many fare products, the use of traditional Revenue Management
systems (which were developed based on the assumption of independence of demand of
fare class) tends to lead to a spiral down effect.
Airlines now have to deal with customers who now systematically buy the lowest fare
available in the absence of distinctions between the fare classes. With fewer high-fare
products being purchased by passengers, airlines' historical booking data contains fewer
records of these products being sold and the revenue management system forecasts less
demand for these products. This in turn results in less seats being protected for higher fare
classes and the surplus of lower fare class seats causes revenues to become more diluted
as there is now even less inducement to buy high-priced products. Therefore, there is a
pressing need to develop new RM techniques for use in such less-restricted
environments. One such technique examined in this thesis is Fare Adjustment, which acts
at the booking limit optimizer level and takes into account the sell-up potential of each
passenger.
As presented in Chapter 1, the objective of this thesis was to provide a more
comprehensive investigation into the effectiveness of Fare Adjustment as a tool to
improve airline revenues in this new environment by 1) extending the investigation of the
effectiveness of FA with standard forecasting to other RM systems (namely EMSRb and
HBP) and also a mixed fare structure where different fare structures are used for different
markets, and 2) looking at the alternative use of fare adjustment in the reservation system.
We first provided a review of the literature on revenue management and its three
component models in Chapter 2, with our discussion focusing mainly on forecasting and
seat inventory control. We then went on to take a look at the entry of Low Cost Carriers
and how their impact on the fare structures of legacy airlines diminished the performance
of traditional RM systems, leading to a spiral down effect. We continued by introducing
the reader to two new revenue management tools that were designed for use in such
simplified fare structures: Q/hybrid forecasting and Fare Adjustment.
We then introduced the Passenger Origin-Destination Simulator (PODs) in Chapter 3,
with which the performance measures and analyses were performed, describing its
passenger choice model and its revenue management systems. We went on to describe
both the simulated air transportation networks within PODS and how Fare Adjustment is
modeled in both the revenue management and reservation system. Finally, we presented
the results of the simulations, describing the impact Fare Adjustment has on an airline's
revenue when it is used in the revenue management system and also when it is used in the
reservation system.
-
91
-
6.1
SUMMARY OF FINDINGS
Our first few series of experiments on RM Fare Adjustment was done in Network D with
two competing airlines. In such a network, we showed that RM Fare Adjustment with
standard forecasting produces positive results when Airline 1 is using EMSRb. We also
demonstrated that although fixed FA FRAT5s are easier to administer and use, variable
FRAT5s which accounts for the increase in willingness-to-pay as we get closer to the
date of departure produces better results. The introduction of fare adjustment to DAVN or
HBP on the other hand did not produce much revenue gains (if any). A summary of the
findings for Network D is illustrated in Figure 6-1.
Figure 6-1: Results Summary - RM Fare Adjustment in Network D
1.6%-
a
1.4%
o Without
-
FA
1.38%
m With FA
1.40%
-I
1.2%
UJW
)
1.01%
1.0%
1.00%
0.80/
0.6%
0.
0.4%
-
E
0
U
0Pt-B
0.2%0
0.0 /
-- yPath-Based EMSRb
DAVN
Path-Based HBP
Figure 6-2: Results Summary - RM Fare Adjustment in Network S
3.5%-
-I
.0
o
Without FA
- All FA
* LCC FA
3.0%-
3.05%/
2.5%-
LU
'U
0 U)
1.890/
2.0%
1.5 0/
~0
1.40% 1.46%
1.04Bo
1.00/
-
0.50/
-
(U
0.0/Path-Based EMSRb
DAVN
-
92
-
Path-Based HBP
We then continued in Section 4.2 by experimenting RM Fare Adjustment with standard
forecasting in a bigger and more complex Network S. Here, we found that RM Fare
Adjustment produces higher revenue for all 3 RM methods used and with the exception
of HBP, the use of fare adjustment in LCC markets alone is not as effective as when RM
fare adjustment is used in all markets (Figure 6-2). In addition, we demonstrated the
ability of RM Fare Adjustment to reduce the over aggressiveness of path-based HBP
(cause by the higher forecasts from path-forecasting) through the lowering of bid prices
as it takes into account the risk of buying-down.
Having showed that Fare Adjustment with standard forecasting is capable of improving
the performance of an airline when used in the revenue management system, we went on
to explore the alternative of using Fare Adjustment in the reservation (RES) system for an
airline that is using EMSRb or HBP in Chapter 5. In Section 5.1.1, we found that by
simply introducing a RES bid price system, Airline 1 would experience a revenue
increase as RES bid price allows the reservation system to capture more spill-in at the
lower classes by closing down local paths that are from higher fare classes (but have
lower total fares) while keeping connecting paths of lower fare classes (but higher total
fares) open.
And in Section 5.1.2, we combined FA with the RES bid price to show that Airline 1 can
obtain an additional 1.0% to 1.5% revenue increase with the introduction of RES Fare
Adjustment in a head-to-head environment. In addition, it was also demonstrated that in
such a network, the use of input psup (which allowed us to change the psup of the higher
fare classes without affecting the psup of the lower fare classes) produces better results
than when psup are derived using forecasting FRAT5 values. A summary of the findings
for Network D is illustrated in Figure 6-3.
Figure 6-3: Results Summary - RES Fare Adjustment in Network D
4.0%
3.8% ---
O Calculated psup
0 RES Bid Price
UInput psup
.J 3.4%3.2%-
LU
3.00%oW
0 4C
2.8%
2.6/a
.
0
2.4 /40
L
M
2.2%
E
2.00/.
.2.01%
0
1.8%1.6 0/
HBP
EMSRb
-
93
-
The use of RES Fare Adjustment in a more complex and competitive environment also
showed revenue gains of approximately 0.5% for both EMSRb and HBP (as summarized
in Figure 6-4). However, unlike the head-to-head environment, the FRAT5 psup gave
better results than input psup in this scenario as it was able to capture more spill-in from
the higher fare classes without losing the spill-in from other fare classes.
Figure 6-4: Results Summary - RES Fare Adjustment in Network S
2.8% --
M RES Bid Price O Calculated psup * Input psup
0
2.6 %
2.56%
2.4%
2.20/
2.190/
2.08% 2.08%
W LIl
2.0%
1.92/
1.80/
1
C0
EiS
E
1.910
1.88.
-
1.40/a
6%
1.62%
1.52%
1.4*
1.20/a-
1.2%
-
1.00/a
EMSR All
6.2
EMSRLCC
HBP All
HBP LCC
FUTURE RESEARCH DIRECTIONS
As a demonstration of the concept of fare adjustment in both revenue management and
reservation system, we have limited our simulations to the use of standard forecasting
techniques. One related area of interest for future investigation lies in the segmentation of
demand between product oriented and price-oriented demand - the use of Hybrid
forecasting over standard forecasting. Hybrid forecasting allows one to forecast each of
these demand independently so as to capture the maximum revenue from the productoriented demand (by managing them with traditional revenue management methods) and
then apply Q-forecasting techniques to the price-oriented method for which the
traditional method do not perform very well.
In his thesis, Reyes 9 had shown the positive impact of using hybrid forecasting with Fare
Adjustment in a two carrier network for DAVN. However, in such a network, a loss of
passengers or revenue by one airline usually leads to a respective gain for the other due to
the feedback effects. To better simulate the performance of Fare Adjustment, one could
implement Fare Adjustment with hybrid forecasting in a more competitive and complex
network such as Network S, with the two airlines using different combinations of seat
inventory optimizers.
-94-
In addition, for all the experiments in this thesis, we have assumed various different
levels of passengers' willingness-to-pay", through the use of different FRAT5 values in
PODs, so as to manage passengers' sell-up behavior. As a result, there is a need to run
simulations at various levels of FRAT5s or input psup, complicating both the
experimental and analyzing process. In addition, the absence of an estimate of passenger
WTP hinders the applicability of fare adjustment in the airline industry as it would be
difficult for the airlines to know which level of FRAT5 or which input psup they should
be using to maximize the returns from fare adjustment. Therefore, methods of estimating
passenger willingness-to-pay and the probabilities of passenger sell-up based on
historical data are also critically important avenues of research.
These are airlines' estimates of passengers' willingness-to-pay. It is important to note that the underlying
willingness-to-pay of the simulated passengers did not vary.
6
-
95
-