The Evolution of e-Commerce in the Airline Industry AGIFORS Reservations and Yield Management Study...

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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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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