the evolution of e-commerce in the airline industry agifors reservations and yield management study...
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The Evolution of e-Commerce in the Airline Industry
AGIFORS Reservations and Yield Management Study GroupNew York - March 24, 2000 by Richard Ratliff
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Overview
Introduction
The History of Distribution
The Internet: a New Distribution Channel
Impact of the Internet on Distribution
and Planning Systems
Future Outlook
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Introduction
The travel and transportation industry has a long history of electronic commerce and communications Developed internal communications infrastructures to
coordinate the activities of staff, aircraft and passengers In the 1950s, business-to-business systems (ARINC and SITA)
created to facilitate passenger service across airlines In the 1960s and 1970s, systems such as Galileo and Sabre
developed to consolidate airline product information (schedules, fares and availability) for travel agencies, creating a global electronic marketplace for the airline industry
Airlines have taken advantage of the information and control available in this environment to increase revenues and reduce costs (including development of OR applications)
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Introduction (cont’d)
Airline industry has a technical and cultural predisposition to e-commerce Explosive growth of internet and World Wide Web has
changed the volume and nature of electronic transactions Legacy systems have required retooling, new business
models have been created These factors have expanded the actual and potential use
of Operations Research within the travel and transportation industry
Review the evolution of e-commerce in the travel and transportation industry Challenges associated with the current environment Adapting existing models and new OR opportunities
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The History of Distribution
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Relevance
Growth of CRSs and the related use of Operations Research in the airline industry provide a strong foundation to build upon in the newly evolving and expanding world of Internet-based e-commerce
However, the infrastructure that exists today was built up over a 70 year period
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Early e-Commerce in Air Travel
The pioneering efforts for airline reservations began with the “request and reply” system used in the 1930s
Through the mid-1940s reservations were recorded manually with a pencil on different colored index cards, nicknamed “Tiffany” cards after the lamps with the colored glass shades
Overbooking used to account for misplaced or incorrectly filed reservations (“no recs”)
After World War II airlines began investing in technology
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How CRSs Originated
In the late 1950s, air travel was on the brink of two key transformations (jet aircraft and IT)
SITA and ARINC were one of the world’s first business-to-business (B-to-B) systems in the 1950s
In 1959, AA and IBM jointly announced plans to develop a Semi-Automated Business Research Environment – better know as the Sabre
CRSs were the first business application of real-time computer technology Moved from hand-written to electronic passenger information
records via automated systems accessible to any agent
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YM and the Increasing Importance of Airline OR
New,start-up carriers in the 1970’s (e.g. People’s Express and Texas International) Introduction of supersaver fares YM fare control began as a defensive measure by majors Major carriers could utilize the wealth of data available from
their reservations systems
Following deregulation, major US carriers were uncompetitive on cost Saddled with legacy pilot and flight attendant union
contractual agreements Without revenue-enhancing CRS and IT/OR technology,
majors would have been unable to respond to competition
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Connecting to Travel Agencies – Distribution
As passenger volumes increased, travel agents became increasingly concerned about their business Processes remained paper-intensive and time-consuming,
offering slower service than the airlines could Automation was needed to print itineraries, invoices, tickets
and accounting functions
JICRS (Joint CRS initiative) 1974 - Create one CRS for all airlines (participants included
American, Eastern, Trans World, United, Western) 1975 - Failure to reach agreement; United withdrew 1976 - Apollo and Sabre installed in travel agencies 1978 - The US airline industry is deregulated Actions spawned today’s multi-CRS and GDS environment
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CRSs are Regulated
Nov. 1984 - several key CRS functions were regulated by the U.S. Civil Aeronautics Board (now known as the US DOT) Display bias was their primary concern
Timing of fare releases and ATPCO
Competitive advance booking data (e.g. MIDT) made available
No differentiation allowed in booking fees by agent
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New Capabilities in the 1980’s and 1990’s
Additional functions become available CRS hosting Frequent Flyer programs Hotel, car rental and cruise line availability Bargain finder (search multiple fares and advise which
class is least expensive for flights booked) Automated yield management Direct connect availability E-ticketing Internet travel sites Best fare finders (go directly from low fare to flight)
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GDS - Relationship Changes
1. Basic Distribution 1976 - 1985 (10 years)
2. Advanced Distribution 1986 - 1999 (14 years)
Supplier
|
GDS
|
Agency
|
Traveler
Supplier
|
GDS
|
Agency
|
Traveler
1976-1993 1994 - 1999
In 1994, Easy Sabre on Prodigy and AOL
In 1997, the Internet arrived.
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The Internet: a New Distribution Channel
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Introduction
Today CRSs and GDSs are the main ticket outlet for most airlines
The internet allows airlines and ticket brokers to bypass the travel agent Customer needs drive the e-design Legacy systems limit the e-design
Different outlets specialize on different customer groups Reverse Auctions Virtual Travel Agents Airlines Sites Global Distribution Systems
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Reverse Auctions
Customer View Web sites like PriceLine.com allow the customer to name a
price for a travel product Customer has to accept any product that matches the
price
Infrastructure The broker contacts airlines directly and shops for the best
available fare
OR Models Reverse auction models are useful to determine inventory
controls in this business model Help give information on underlying consumer demand
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The Virtual Travel Agent
Customer View Sites like Sabre’s Travelocity.com and Preview Travel or
Microsoft’s Expedia allow customers to pick and choose among different offers online
Infrastructure The sites work on top of existing CRSs and emulate the
work of travel agents
Data Needs vs. Data Sources Fares include published, off-tariff and dynamically created OR methods can be used to build an efficient link between
the GDS and customer sites
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Finding the Best Fares using OR Techniques
OR Problem Optimize among a broad number of flight and fare
alternatives and also rank secondary choices
Problem Characteristics Problem space is very large and computational time limited Side constraints are on the leg and on the path level
Special Considerations Algorithm performance depends on efficient fare
enumeration and rule checking Different types of data have different access times
Useful By-Products Intermediate search results provide the customer with
additional information
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Airline Sites
Many airlines sell tickets directly through their own web sites
Customer Pros and Cons Customers are rewarded by special discounts and offers But they don't have the opportunity to shop for other
airlines
Use of OR Methods Airlines use statistical methods to set up promotional
schemes that target special consumer groups Provide availability processing and best fare search
capabilities such as those available in the GDSs
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Global Distribution Systems
Internet GDSs use the internet to extend their reach
What's new? Travel agents and GDSs provide value added services to
compete with new distribution channels (e.g. Virtually There)
Bundling of services and cross-selling
OR Applications Statistical models are used to find cross-selling
opportunities New YM opportunities for more detailed availability control
based on customer-specific behavior (creates both real-time and profiling challenges)
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Impact of the Internet on Distribution and Planning Systems
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Introduction
Airlines use market analysis and OR based systems to maximize expected revenue
Much of the data that feeds the OR systems are collected by CRSs and GDSs
The advent of a new distribution channel has a major impact on the validity and availability of the data In some cases the OR models themselves have to be
re-engineered to fit the new business problem Example OR applications follow in the next few slides
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CRS Simulation
CRSs use a set of rules to determine which flights are presented upon a given request
Screen presence has an extraordinary impact on customer preferences
Simulation models can be used to determine the effects of different strategies on screen presence and market share Recent innovations such as web outlets and dynamic
display rules also need to be considered Useful for developing e-mail promotions or those via
an airline’s web site
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Passenger Preference Modeling
Passenger preference models became prevalent after industry de-regulation Schedule design became a very complex problem
due to a growing number of airports and increasing demand
Models developed to support schedule design by evaluating schedule profitability
These model take account of market size forecasts, passenger preference parameters, flight schedules, fares and business rules
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Passenger Preference Modeling (cont’d)
The internet results in a large number of distribution channels with low volume Preference models have to capture passenger behavior
with respect to all types of distribution channels Smaller booking volumes per outlet increase data
variability used to calibrate the customer preference model
Many internet travel sites store customer profiles May also be used to calibrate passenger preference models
Potential use of “clickstream” data Captures transactions made by customers on web sites Similar attempts were made in Sabre by recording agent
key strokes during randomly selected sales sessions
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Passenger Yield Management
Demand and passenger behavior data is necessary to set controls, and CRSs serve as data sources
Advancements in the OR and processing are moving us from separate time-series forecasting and leg-based optimization to econometric models and ODYM Still mostly batch processes today Real-time re-forecasting and re-optimization in next five years Incorporation of still more detailed controls with e-channels
(customer-specific availability via on-line access to historical information and rapid profiling of characteristics)
Competitive closures will be less obvious due to reduced use of traditional distribution channels
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Passenger YM (other impacts)
Internet sales change size and characteristics of demand Changes in passenger behavior due to internet
specific restrictions May necessitate re-calibration of overbooking and
demand forecasts Hidden shifts in competitive bookings market share
due to direct airline web sites
Internet forces a change in pricing strategy (from oligopoly to retail)
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Cargo YM (B-to-B types)
Medium-term yield management Various forwarders (bulk customers) submit bids for
shipping capacity on airline’s flight network The airline optimizes the allocation of available capacity to
various bids by maximizing the expected revenue over a planning period such as quarter
Cargo routing is useful in determining feasible and profitable routes for satisfying a shipment request Being extended to the Internet to efficiently integrate the
business processes involved with the shipper-forwarder interaction
Can provide dynamic pricing and capacity allocation
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Cargo YM (B-to-C types)
Short-term yield management to satisfy the ad-hoc shipment demand
Bid prices Determined by considering the ad-hoc demand,
medium-term demand, and available capacity Used to accept/reject shipment requests over the
booking horizon
Improved consumer cargo search engines via the Internet may stimulate additional demand for last-minute shipments and drive large changes from historical booking behavior
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Future Outlook
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Regulation of e-Travel Sites?
Will Internet travel websites be regulated? Neutral, semi-neutral and aligned sites exist Up-front disclosure of “alignment” is important in semi-
neutral sites (e.g. T2 consortium or sites with airline equity investment)
Customers could be misled into thinking that a complete and unbiased range of alternatives will be presented
But even “neutral” infomediaries may be biased Any system will require an algorithm that determines what to
display and the ordering (airlines, mortgages, insurance…) Volume-based commissions create incentives for bias Suppliers are paying for essentially two things: 1) to be listed
on the website and 2) better presence
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Regulation of e-Travel Sites? (cont’d)
Bias in e-commerce travel sites is similar to what exists through “brick and mortar” establishments Booking direct with airlines is biased Everything equal, agents favor airlines with best
commissions But governments have avoided e-Commerce regulation
Secondary market-driven forces may come to the rescue Studies of best fare comparisons by consumer advocacy
groups (e.g. Consumer Reports) Authentic “neutrality” may even become a strong selling
point among the informediaries
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Search Robots
Currently, e-commerce on the web is free to the user Search robots can abuse other sites to shop for free
information and re-sell it to the customer Impacts both the virtual travel agent and airline sites Increasing sophistication makes robots harder to detect
How can the industry protect itself against this abuse? Design websites to make it difficult for meta-search engines “Drilling down” for information several screens deep More frequent use of member i.d. logins to distinguish
genuine users from robots Usage-based fees?
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Airline B-to-B Will Grow
Successful alliance implementation requires seamless integration of various business processes and systems Internet and related technologies provide the communications
infrastructure required for the business to business integration Alliances have a profound impact on the airline OR systems Need to expanded current models to reflect the collaborative
planning, marketing, and operating efforts among the constituent airlines of the alliances
B-to-B vendors will provide central repositories for the data required for alliance related OR systems Will provide better tools to allow carriers to implementing the policies
obtained from the alliance-based OR models e.g. Sabre / Ariba deal to create Sabre e-Marketplace
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Impact on the Airline OR Profession
Effective implementation of new e-Commerce business practices requires investigation using OR
Rapid proliferation of e-Commerce practices is putting a strain on the airline OR profession The OR model life cycles are decreasing The rewards associated with rapid OR modeling are
becoming high but create greater risk of negative impact The data are more noisy and the business environment is
more unstructured than ever before
Ethical and legal ramifications such as “what level of detail data can be used from click-stream data?” Confidentiality and privacy issues
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Thanks
Other colleagues at Sabre who assisted in material presented here
Dan Delph
Dirk Guenther
Beju Rao
Barry Smith
Pat Trapp
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Selected References
Gilbert Burck, “’On Line’ in ‘Real Time’”, FORTUNE magazine, April 1964.
Copeland, Mason, and McKenney, “Sabre: The Development of Information-Based Competence and Execution of Information-Based Competition” IEEE Annals of the History of Computing, Vol. 17, No. 3, 1995, pg. 30
Lee Davis - UNISYS, “Real Time- The Ultimate O&D”, AGIFORS R&YM, Melbourne, May 1998
Geraghty, Govil, Guarnieri, & Lancaster - Delta Technology, ““Securities Trading Paradigm for Revenue Management”, AGIFORS R&YM, Melbourne, May 1998
Guenther, Rao, Ratliff, and Smith - Sabre, “A Review of the Evolution of e-Commerce and Operations Research in Travel and Transportation”, working paper, March 2000
Max D. Hopper, “Rattling SABRE – New Ways to Compete on Information”, HARVARD BUSINESS REVIEW, No. 90307 May-June 1990.
“Startup Muse”, FORBES magazine website (www.forbes.com), Digital Tool feature, August 18, 1999 issue
“That’s the Ticket”, WALL STREET JOURNAL, Monday, July 12, 1999, e-Commerce Section, pg. R45
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Questions?