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Approaches to Availability Processing - The Increasing Problem of Shopping -. Richard Ratliff AGIFORS R&YM Study Group Honolulu – June 2003. Outline. e-Commerce Impacts on Availability Emergence of On-line Channels Assessing Competitiveness Methods CRS Upgrades / Multihosting AVS

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Approaches to availability processing the increasing problem of shopping

Approaches to Availability Processing- The Increasing Problem of Shopping -

Richard Ratliff

AGIFORS R&YM Study Group

Honolulu – June 2003


Outline
Outline

  • e-Commerce Impacts on Availability

    • Emergence of On-line Channels

    • Assessing Competitiveness

  • Methods

    • CRS Upgrades / Multihosting

    • AVS

    • Caching

    • Proxy-based Methods

  • Sabre’s experiences

    • Air

    • Hotel

  • Future Directions



Emergence of on line channels
Emergence of On-line Channels

  • “Look-to-Book” ratio defined (a.k.a. LTB)

    Look-to-book = shopping requests / net (actual) bookings

  • On-line factors driving increased LTB ratios

    • Consumers are comparison shopping across websites

    • Robotics to “mine” websites for competitive information

    • Low fare search engines growing more complex and returning more options to customers (necessitating more availability checks)

  • Systems challenges at Sabre

    • Availability requests have grown over 50% over the past two years, due to rising LTB ratio and increased low fare search activity

    • Necessitated movement of low fare search functions off mainframe TPF and onto massively scalable (MPP) computer systems

      • Announced a $100M, 10-yr deal with HP


Rise in on line shopping

Agency LTB

Website

LTB

Rise in On-line Shopping

  • Significant productivity differences exist between traditional vs. on-line channels

  • Travel agencies

    • More experienced users with a productivity-oriented focus

    • LTB is low; Sabre-connected agency is typically 12-to-1

  • On-line channels

    • Less experienced users who view Internet channels as a “free” resource

      • 15% of Travelocity sessions involve repeat requests for the exact same market and date combinations (in the same session)

    • LTB varies widely by website

      • Ranges from 100-to-1 to as high as 2000-to-1

      • Working assumption in this presentation is that 200-to-1 LTB is indicative of on-line channel productivity


Comparison shopping via robotics
Comparison Shopping via Robotics

  • Robotics are easy to build and increasingly utilized to obtain competitive information across websites

    • Predominant information source for rental car and hotel companies (since no comprehensive centralized information sharing exists)

    • Airlines - ATPCO data is useful but limited

      • Webfare specials (not necessary to file everything via ATPCO)

      • GDS and on-line sum-of-local fares; cited by Continental Airlines in previous AGIFORS *

    • Two webfare vendor examples (not an exhaustive list)

      • SideStep

      • FareChase

    • Some suppliers have taken legal action against webfare vendors to block them from mining website information

      • Indicative of worsening channel conflict and/or increased system processing costs

  • Sabre’s experience

    • Suppliers are a major source of travel-related robotics

    • Need to distinguish between “friendly” and “unfriendly”

* References: see Kinloch – 2001 & Brunger - 2002



Single image inventory seamless availability

“Single Image” is having a single, accurate picture of inventory in one place

Airlines = widely, but not always, utilized (e.g. block space agreements with tour operators or for alliance code-shares)

Hotels = more a goal than a reality

Seamless availability is accessing the single image inventory in real-time (for greatest accuracy)

Travel Agents

Seamless Availability

GDSs

Call Center

Own website

Travelocity, Priceline, Hotwire,

Travelweb

Have everyone use seamless availability!

Mark Travel

Liberty

6

7

Expedia Hotels

Jetset

11

9

The

Reality

The

Goal

Trip.com

Quikbook

2

5

100

55

CCV

Orbitz Hotels

3

2

CRS

CRS

Single Image Inventory & Seamless Availability


Crs upgrades

Inventory inventory in one place

CRS Upgrades

  • Impacts of increasing “look-to-book” ratios

    • Seamless availability necessitates responses from supplier CRS within only 1-3 seconds

    • Airline (and hotel) CRS’s are finding it difficult to handle the increased volumes

    • Results in timeouts

      • Fallback is to use leg availability status information (AVS)

      • Results in less accurate responses (and increased UCs)

    • Major threat to the travel distribution ecosystem

  • Managing increased demands via CRS upgrades and additional capacity

    • Expensive, real-time CPU resources are involved

    • Scalability constraints may prevent adding new capacity, necessitating redesign of core processes

    • Escalating costs for carriers

Supplier

Availability Request

Availability Request

Seamless Availability

Process Flows

(Traditional)

Travel agencies / consumers

Availability Response

Availability Response

CRS


Use of multihosting top 15 airlines crs
Use of Multihosting – Top 15 Airlines’ CRS* inventory in one place

Airline2001 Pax (millions)CRS (circa 2001)

Delta 104.9 Deltamatic — Managed by Worldspan

American 80.7 Sabre

United 75.4 Apollo (Galileo)

Southwest 64.6 SAAS (Sabre)

US Airways 56.1 Sabre

Northwest 54.1 Worldspan

Continental 44.2 EDS SHARES

All Nippon 43.2 In-House

British Airways 40.0 Amadeus

Lufthansa 39.7 Amadeus

Air France 38.6 Amadeus

Japan Airlines 32.2 In-House

Iberia 27.3 Amadeus

Alitalia 24.9 In-House

Air Canada 23.1 In-House — managed by IBM

* Reference: Giga Information Group and Airline Business magazine, September 2002

  • Observations

  • Only 4 carriers listed maintain their own CRS

  • Multihosting is a common approach to managing the complex system challenges

  • Doesn’t negate effective management usage (due to increased transaction fees)


Avs availability status messages
AVS (Availability Status Messages) inventory in one place

  • Leg and Segment AVS

    • Traditional method in widespread use today

    • Standards are established and universally adopted

    • Not timely; updates can sometimes lag by a full week or more

      • Could be improved via use of publish-subscribe technology (or SITA)

    • Can be inaccurate, especially close to departure

  • O&D AVS?

    • O&D AVS proposals

      • Worldspan

      • Lufthansa

      • No standards yet exist

    • Polynomial increase in size of controls being managed

      • Relies heavily on frequent exchange of status (e.g. via pub-sub)

      • YM control or sales changes on one ODF would create a flood of O&D AVS messsages

      • Statusing logic is complex; can‘t always identify other ODF impacts

    • If kept up-to-date, should provide greater accuracy that leg AVS


Caching defined
Caching - Defined inventory in one place

  • What is caching?

    • Rather than checking availability live (in real-time), use a previously stored (i.e. cached) availability result for the specific ODF and date in question

      • Actively used by Expedia and Orbitz

      • Worldspan uses this as their primary solution to rising LTB ratios

  • Types of caching

    • Passive – reuse results of any previous seamless availability checks that were made during sell process

    • Active – proactively poll the supplier CRS to obtain the current availability

  • Availability usage differences

    • Some on-line retailers and GDSs believe that small inaccuracies in availability are tolerable during the shopping process

      • e.g. a customer is shown a fare is available when in fact it’s not

    • When a fare is actually sold, almost everyone agrees that seamless availability is necessary

      • Creates risk, because errors result in agency debit memo exposure or risk of PNR cancellation by the carrier


Caching benefits
Caching – Benefits inventory in one place

  • More accurate than leg AVS

    • Data are more current and specific than leg AVS (e.g. by ODF)

    • Can be used in conjunction with leg AVS (to highlight ODFs and dates that have changed)

  • Simpler integration

    • Very easy to develop using robotics

    • Pub-sub (event-triggered) updates are more difficult

      • More accurate than scheduled polling

      • Requires greater integration effort and partnering with supplier

  • Uni-lateral decision making

    • Retailer and/or GDS doesn’t need agreement from supplier to begin to cache results (i.e. “Just Do It”)

    • No need to agree on an industry standard


Caching problems
Caching - Problems inventory in one place

  • Inaccuracy

    • The continual challenge is data freshness (or lifespan of the cached result)

    • To improve accuracy requires more frequent polling, which (paradoxically) drives up the LTB ratio!

    • Not real-time and can be hard to troubleshoot, so it should be combined with other real-time diagnostics (such as # of DCS failures)

    • Combinatorial explosion

      • Maintaining cache at a low level of detail (i.e. by ODF) results in a larger data space than at a higher level (e.g. by leg class)

      • Careful analysis is needed to maximize cache accuracy while minimizing volume of cached results

  • Which ODF and date ranges work best with caching?

    • Best: Off-peak periods (where sales activity is low)

    • Maybe: Uni-directional sales (i.e. once a class is closed, it stays closed without reopening)

    • Poor: ODFs and date ranges with frequent re-booking and cancellation activity are more problematic (i.e. fractional closures)


Proxy based availability
Proxy-based Availability inventory in one place

  • Proxy-based methods offload the expensive, real-time CRS processing onto open systems devices (run locally at a remote location)

    • Keep the inventory business logic and raw information synchronized with airline host

    • As inventory changes in the airline host environment, proxies are modified and updated

  • Benefits

    • Accuracy comparable to seamless (and faster since run locally)

    • Should be less expensive than CRS upgrades; can use commodity processors rather than mainframes

    • Platforms can be made more scalable

    • Can utilize pub-sub technology with reliable messaging delivery for robust, fault-tolerant synchronization

    • One server farm could be the supplier “availability” hub for all distribution channels

  • Problems

    • Since inventory processing logic (or a facsimile) must be replicated, requires high integration effort compared to other methods

    • Partnership approach means decision to use must be bi-lateral


Proxy based availability1

Inventory inventory in one place

Proxy-based Availability

Seamless Availability

Process Flows

(Proxy-based)

Avail.

Proxy

Supplier

Availability Request

Availability Request

Travel agencies / consumers

Inventory Updates

Availability Response

Availability Response

CRS

  • Why does this approach work?

    • Viewed against the actual CRS workload, the LTB ratio drops to 1-to-1 (due to functional offload of shopping – only sells remain)

    • Since shopping requests outnumber bookings (by a large integer number), the inventory update and synchronization volume is comparatively low

Note: the process depicted above is currently patent pending by Sabre


Sabre’s Experiences inventory in one place


Air availability
Air - Availability inventory in one place

  • AVS at Sabre

    • As of 5/28/03, Sabre manages more than 142 million separate, active AVS items

      • Across all carriers, markets, and future dates

      • These messages need to be handled consecutively, in the exact order received, to be properly applied (otherwise it’s based on the old status)

    • Re-application of AVS status is one of the major components involved in schedule change processing

      • Current AVS standard assumes that airline and GDS schedules are 100% in sync, which is problematic because of OAG delays

      • E.g. BA sends close “cc” on LHR-BOM but the flight is LHR-DXB-BOM, we have to figure it out and close all 3 segments

  • Can O&D AVS work?

    • O&D controls to manage connecting markets

    • Point-of-Sale controls to manage discount selling channels

    • O&D and POS controls will pose severe difficulties due to a large increase in the existing number of AVS items

      • Feasibility is still unclear

  • Sabre’s strategy

    • Have proposed to CRS Harmonization working group and CASMA the consideration of proxy-based availability processing to address escalating LTB ratios

      • Can effectively deal with low levels of control (e.g. by ODF and POS)


Hotel caching
Hotel - Caching inventory in one place

  • A leading hotel chain cited to Sabre that their LTB ratio is approaching 500-to-1

    • “…most of the lowest hotel rates are being provided through the unregulated medium of the Internet…” *

    • Shopping activity is expected to comprise 50% of their total CRS processing capacity by year-end 2003

  • Hotel merchant inventory by major on-line retailers

    • Expedia, Orbitz, Travelocity, etc. are increasingly taking a merchant position

    • Growth in merchant inventory requires “free sell” and seamless availability (rather than block allocations)

    • In the absence of seamless (since only a few chains elect to use this functionality), caching is required

  • Each of these “N” retail entities requires similar volume and quality of information to enable reasonable heuristics

    • The cached data are independently replicated “N” separate times!

    • Drives huge increases in LTB ratios

* References: “Booking Hotels Online: An In-Depth Examination of Leading Hotel Web Sites”, William J. McGee, Consumer WebWatch, Apr. 24, 2003


Future Directions inventory in one place


On line vs total market
On-line vs. Total Market inventory in one place

  • US and Canada total travel spending analysis *

    • Assumes total travel growth is 3% from 2002-2006

    • Assumes CAGR of +20% online and -2% offline

  • Factors driving increased on-line usage by consumers

    • 18% of US households have broadband (est. 5X increase in DSL by 2005) **

    • Wireless Internet access growing at “hot spots” (e.g. Starbuck’s & airports)

    • Supplier’s pushing e-technology (e.g. e-ticketing, online check-in)

    • Reduction in agency locations (ARC decreases = 16% from 9/00 – 8/02) ***

* References: Various incl. Forrester, Jupiter, and PhoCus Wright estimates

** References: Forrester – 3/03

*** References: ARC website


Trends in availability requests
Trends in Availability Requests inventory in one place

  • Approximate impacts of on-line channel shift

    Typical Today (2002)

    (Agency Share * Agency productivity) + (on-line share * on-line productivity) =

    (82.4% * 12 LTB) + (17.6% * 200 LTB) = 45.1 LTB ratio (2002)

    Typical in Future (2006)

    (Agency Share * Agency productivity) + (on-line share * on-line productivity) =

    (68.0% * 12 LTB) + (32.0% * 200 LTB) = 72.2 LTB ratio (2006)

    Est. 60% increase in availability requests over next 4 years

  • Availability-related problems are going to grow worse over time

    • Above calculations don’t consider other impacts such as:

      • Widening use of robotics, increased dynamic packaging by on-line retailers (e.g. Expedia and Travelocity) & and new web service offerings by suppliers

      • CRM-related impacts (detailed on next page)


Increased adoption of crm
Increased Adoption of CRM inventory in one place

  • Customer Relationship Management

    • Customer-centric availability

    • Personalized pricing

      • “…industry consensus that the current US fare structure is dysfunctional”

        • From Joan Feldman, Air Transport World

      • Dynamic pricing & increased customer segmentation approaches are likely to emerge

        • From Brady and Cunningham - 2001


Customer centric availability processing
Customer-centric Availability Processing inventory in one place

  • Future integration of Customer Relationship and Yield Management (using bid price controls in this example)

Real-time Rate ODF or LOS

- S BP’s (bid prices across all legs or room nights)

+/- POS and Distribution Channel Bias

+/- Customer Marketing Value Adjustment

+/- Specific Overbooking Risk Adjustment

= Net Value ODF or LOS(considering multiple attributes)

Today

Future

  • Overbooking risk and customer value have clear business benefits

    • Will compound the limitations inherent in O&D AVS or caching approaches due to exponential explosion in controls to manage


References inventory in one place


Selected references
Selected References inventory in one place

  • “Exploring Predatory Pricing in the Airline Industry”, Brady and Cunningham, Transportation Journal, pgs. 10-11, Fall 2001

  • “RM from the eCommerce Point of View”, Bill Brunger - Continental Airlines, AGIFORS R&YM Study Group, Berlin – 2002

  • “Managing Your Look to Book Ratios”, Madeleine Gray – Sabre, CASMA conference (Computerized Airline Sales and Marketing Association), Oct. 2002

  • “Net Gains, Net Losses?”, Feldman, ATW, pg. 37, Feb. 2002

  • “Why O&D Doesn't Work“, Leon Kinloch - Continental Airlines, AGIFORS R&YM Study Group, Bangkok - 2001

  • “Booking Hotels Online: An In-Depth Examination of Leading Hotel Web Sites”, William J. McGee, Consumer WebWatch, Apr. 24, 2003


Questions? inventory in one place


Appendix inventory in one place


Avs more information
AVS – More Information inventory in one place

  • Controls: Carrier Flight Number, Date, Class/All Classes, Leg or Segment City Pair and Open, Numeric or Restrictive Status currently in effect

  • Enforces: Segment selling restrictions, Waitlist accumulation restrictions, Polling activation, and provides support for Leg Overrides to fully restrict ALL passenger flow over multi-leg flight routings

  • Effects: Manages “Sum-of-Locals” and “Through Passenger” revenues on a single flight-by-flight basis

  • Uses: Can be relatively accurate when used correctly. It’s a vital fail-over mechanism for sell and report processing when direct system access is off-line. It functions between automated and non-automated environments


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