O d control abuse by distribution systems pods simulation results
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O-D Control Abuse by Distribution Systems: PODS Simulation Results. Dr. Peter P. Belobaba International Center for Air Transportation Massachusetts Institute of Technology AGIFORS Reservations and YM Study Group Meeting Berlin, Germany April 16-19, 2002. Outline. PODS RM Research at MIT

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O-D Control Abuse by Distribution Systems: PODS Simulation Results

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O d control abuse by distribution systems pods simulation results

O-D Control Abuse by Distribution Systems: PODS Simulation Results

Dr. Peter P. Belobaba

International Center for Air Transportation

Massachusetts Institute of Technology

AGIFORS Reservations and YM Study Group Meeting

Berlin, Germany

April 16-19, 2002


Outline

Outline

  • PODS RM Research at MIT

    • Simulated Revenue Benefits of Network RM

  • O-D Control “Abuse” by Distribution Systems

    • Example of Fare Search Abuse

  • Simulated Revenue Impacts of Abuse

    • Methodology for PODS Simulations

    • Proportion of Passengers Committing Abuse

  • Potential Threat to O-D Control Revenue Gains

    • Options for Dealing with Abuse


Pods rm research at mit

PODS RM Research at MIT

  • Passenger Origin Destination Simulator simulates impacts of RM in competitive airline networks

    • Airlines must forecast demand and optimize RM controls

    • Assumes passengers choose among fare types and airlines, based on schedules, prices and seat availability

  • Recognized as “state of the art” in RM simulation

    • Realistic environment for testing RM methodologies, impacts on traffic and revenues in competitive markets

    • Research funded by consortium of seven large airlines

    • Findings used to help guide RM system development


Pods simulation flow

PATH/CLASS AVAILABILITY

REVENUE

MANAGEMENT

OPTIMIZER

PATH/CLASS

BOOKINGS/

CANCELLATIONS

CURRENT

BOOKINGS

FUTURE

BOOKINGS

FORECASTER

UPDATE

HISTORICAL

BOOKINGS

HISTORICAL

BOOKING

DATA BASE

PODS Simulation Flow

  • PASSENGER

  • DECISION

  • MODEL


Pods network d description

PODS Network D Description

  • Two airlines competing in realistic network:

    • 40 spoke cities with 2 hubs, one for each airline

    • 20 spoke cities on each side located at actual US cities

    • Unidirectional : West to east flow of traffic

    • Each airline operates 3 connecting banks per day at its own hub

    • Connecting markets have choice of 6 scheduled paths per day

    • O-D fares based on actual city-pair published fare structures

    • 252 flight legs, 482 O-D markets

  • Airlines use same or different RM methods to manage seat availability and traffic flows.


Geographical layout

Geographical Layout

1

H1(41)

2

21

3

4

5

25

6

23

24

27

26

7

31

28

30

8

29

32

33

22

9

11

34

35

38

10

12

14

15

13

16

H2(42)

36

17

18

37

19

39

20

40


Revenue management schemes

Revenue Management Schemes

  • BASE: Fare Class Yield Management (FCYM)

    • Demand forecasting by flight leg and fare class

    • EMSRb booking limits by leg/fare class

  • “Vanilla” O-D Control Schemes: Representative of most commonly used approaches

    • Heuristic Bid Price (HBP)

    • Displacement Adjusted Virtual Nesting (DAVN)

    • Nested Probabilistic Network Bid Price (PROBP)


Rm system alternatives

RM System Alternatives


Revenue gains of o d control

Revenue Gains of O-D Control

  • Airlines are moving toward O-D control after having mastered basic leg/class RM fundamentals

    • Effective fare class control and overbooking alone can increase total system revenues by 4 to 6%

  • Effective O-D control can further increase total network revenues by 1 to 2%

    • Range of incremental revenue gains simulated in PODS

    • Depends on network structure and connecting flows

    • O-D control gains increase with average load factor

    • But implementation is more difficult than leg-based RM


Network d revenue gain comparison airline a o d control vs fcym

Network D Revenue Gain ComparisonAirline A, O-D Control vs. FCYM


Benefits of o d control

Benefits of O-D Control

  • Simulation research and actual airline experience clearly demonstrate revenue gains of O-D control

    • Return on investment huge; payback period short

    • Even 1% in additional revenue goes directly to bottom line

  • O-D control provides strategic and competitive benefits beyond network revenue gains

    • Real possibility of revenue loss without O-D control

    • Improved protection against low-fare competitors

    • Enhanced capabilities for e-commerce and distribution

    • Ability to better coordinate RM with alliance partners


O d control system development

O-D Control System Development

  • Based on estimates of network revenue gains, airlines have pursued development of O-D controls:

    • Up-front investments of millions, even tens of millions of dollars in hardware, software and business process changes

    • Potential revenue benefits of tens or even hundreds of millions of dollars per year

  • At the same time, GDS and website technology has evolved to provide “improved” fare searches:

    • Objective is to consistently deliver lowest possible fare to passengers and/or travel agents in a complicated and competitive pricing environment


Abuse of o d controls

“Abuse” of O-D Controls

  • Example 1: Booking connecting flights to secure availability, then canceling 2nd leg and keeping low fare seat on 1st leg.

    • Most airlines with O-D control are well aware of this practice, usually done manually by travel agents

    • Can be addressed with “Married Segment” logic in CRS

  • Example 2: Booking two local flights when connecting flights not available, then pricing at the through O-D fare in the same booking class.

    • Appears to be occurring more frequently, as web site and GDS pricing search engines look for lowest fare itineraries


Requested itinerary sea hub bos

Requested Itinerary SEA-(HUB)-BOS

Q=$200

SEA

  • SEAMLESS O-D AVAILABILITY

  • SEA-BOSYBM (connecting flights)

  • SEA-HUBYBMQ (local flight)

  • HUB-BOSYBMQ (local flight)

  • O-D control optimizer wishes to reject connecting path and accept 2 locals with higher total revenue

BOS

Q=$100

HUB

Q=$150


O d abuse by fare search engines

O-D Abuse by Fare Search Engines

  • In our example, a passenger wishes to travel from SEA to BOS (via HUB):

    • Airline’s O-D control system has determined that $200 Q fare SEA-BOS should be rejected

    • However, Q fare remains open on SEA-HUB and HUB-BOS legs, with expectation of ($100+$150) $250 in total revenue

  • Travel agent or search engine finds that two local legs are still available in Q-class:

    • PNR created by booking two local legs separately

    • But, GDS then prices the complete BOS-SEA itinerary at $200, leading to $50 network revenue loss for airline


Revenue impacts of o d abuse

Revenue Impacts of O-D Abuse

  • This type of abuse affects only O-D RM methods:

    • Fare class control with EMSR does not distinguish between different O-D itineraries in same booking class

    • No revenue impact on EMSR control

  • How big is the revenue impact on O-D methods?

    • Clearly, abuse bookings can reduce the incremental revenue gains of O-D methods over EMSR leg fare class methods

    • Depends on how widespread abuse booking practices are (i.e., proportion of eligible booking requests that actually commit abuse)


Simulation of abuse in pods

Simulation of Abuse in PODS

  • For every O-D/fare in the network, we generated two path alternatives:

    • The connecting path priced at the published O-D fare

    • A path comprised of the two local legs, also priced at the connecting (through) O-D fare

  • When the connecting path is closed by the O-D RM system, passengers look for the “local” alternative:

    • Only the passenger choice process is affected

    • Airlines still perform RM assuming that sale of two local seats will generate revenue equal to sum of two local fares


Simulation set up

Simulation Set-Up

  • PODS Domestic Network D

  • Average Network Load Factors = 77%, 83%, 88%

  • Probability of Abuse from 0% to 50%, in 10% increments for both leisure and business travelers.

    • We assume initially that probability of abuse is the same for each passenger type

  • RM database records abuse bookings as path bookings accepted after path/fare was closed:

    • Historical abuse bookings added to detruncated estimates of path/fare booking demand, distorting future forecasts


Simulated revenue impacts

Simulated Revenue Impacts

  • Revenue gains of O-D methods drop from 1.4% with no abuse to almost zero at 50% abuse:

    • DAVN revenue gains are least affected, dropping to 0.55% over EMSRb base case at 50% probability of abuse

    • ProBP and HBP are affected more substantially, dropping to almost zero and even small negative revenue impacts

  • Several factors contribute to revenue losses:

    • Direct losses from taking lower connecting fare than expected two local fares

    • Distortion of demand forecasts leads to subsequent errors in estimation of network displacement costs and bid prices


Impacts depend on abuse probability

Impacts Depend on Abuse Probability

  • Simulations show that more than 50% probability of abuse required to wipe out O-D revenue gains:

    • Of all opportunities where two local legs are open and the connecting path is closed in the same fare class, more than half of passengers would have to abuse the O-D controls

  • Actual probability of abuse appears to be low:

    • Anecdotal evidence suggests 10-20% or less

    • But evolution of web site and GDS search engines raises concerns that this probability will continue to grow

    • For time being, more likely that leisure travelers paying lower fares are involved in O-D abuse, not business travelers


Impacts differ by network alf

Impacts Differ by Network ALF

  • Negative impacts on revenue gains are more dramatic at higher network load factors:

    • Because revenue gains of “perfect” O-D control are higher at higher demand levels, more to lose with O-D abuse

    • 25% probability of abuse reduces revenue gains by about 1/3 at 83% network load factor, and by 1/2 at 88%

    • At lower 77% network load factor, 25% probability of abuse actually leads to slightly higher O-D revenue gains (relative to EMSRb control), since additional traffic is accommodated with less displacement


Summary of findings

Summary of Findings

  • Simulated negative revenue impacts due to “O-D” abuse by availability search and pricing engines:

    • Even at 10-20% probability of abuse, revenue gains of O-D methods are reduced by up to 1/3

    • Means actual revenue gain of ProBP is closer to 0.8% than estimates of 1.4% under perfect O-D control conditions

  • Practical O-D control issues have an unexpected and substantial impact on network RM models:

    • Bid price methods appear to be more affected than DAVN, because forecasting distortions affect probabilistic bid prices more consistently than deterministic LP shadow prices.


Unanswered questions

Unanswered Questions

  • How widespread is this type of O-D abuse?

    • Certainly possible with manual action by travel agents

    • Evidence of systematic abuse by some website and GDS search engines

  • Can these simulation results be generalized?

    • Intuitively clear that overriding the optimized results of the network RM system will lead to reduced revenues.

    • Order of magnitude seems to be reasonable, dependent on probability of abuse and network load factors

  • Can airlines stop this abuse and revenue loss?


Possible solutions

Possible Solutions

  • Some airlines have considered (and implemented) “Journey Control” for PNR booking:

    • Recognize that first local leg has been booked and refuse second local leg availability if connection is not available

    • Also known as “shotgun wedding” controls in CRS

  • Alternative is a “ticket as booked” policy:

    • If two legs were booked based on local availability, then they must be ticketed as two local fares

    • Requires changes to GDS processing and/or travel agency enforcement, which might be more difficult to implement


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