1. 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

2. 2 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

3. 3 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

4. 4 PODS Simulation Flow PASSENGER DECISION MODEL REVENUE MANAGEMENT OPTIMIZER FORECASTER HISTORICAL BOOKING DATA BASE CURRENT BOOKINGS HISTORICAL BOOKINGS FUTURE BOOKINGS PATH/CLASS AVAILABILITY PATH/CLASS BOOKINGS/ CANCELLATIONS UPDATE

5. 5 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.

6. 6 Geographical Layout H1(41) H2(42) 4 3 2 1 10 9 8 7 6 5 15 17 16 14 12 11 22 21 20 19 18 28 2726 25 2423 33 32 31 30 29 39 38 37 36 3534 40 13

7. 7 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)

8. 8 RM System Alternatives

9. 9 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

10. 10 Network D Revenue Gain Comparison Airline A, O-D Control vs. FCYM

11. 11 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

12. 12 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

13. 13 “Abuse” of O-D Controls Example 1: Booking connecting flights to secure availability, then canceling 2 nd leg and keeping low fare seat on 1 st 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

14. 14 Requested Itinerary SEA-(HUB)-BOS 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 HUB BOS SEA Q=$100 Q=$150 Q=$200

15. 15 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

16. 16 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)

17. 17 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

18. 18 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

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20. 20 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

21. 21 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

22. 22 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

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26. 26 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.

27. 27 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?

28. 28 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