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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. PODS RM Research at MIT

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

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  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. 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. 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. 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

  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. 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

  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. RM System Alternatives

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

  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. 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. “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

  14. Requested Itinerary SEA-(HUB)-BOS Q=$200 SEA • SEAMLESS O-D AVAILABILITY • SEA-BOS YBM (connecting flights) • SEA-HUB YBMQ (local flight) • HUB-BOS YBMQ (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

  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. 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. 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. 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

  19. 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

  20. 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

  21. 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

  22. 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.

  23. 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?

  24. 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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