1. RM Coordination and Bid Price Sharing in Airline Alliances: PODS Simulation Results Peter Belobaba Jeremy Darot Massachusetts Institute of Technology AGIFORS YM Study Group Bangkok, May 9-11, 2001

2. 2 Outline PODS Alliance Network Characteristics  Airline A vs. Airline B+C Alliance Revenue Impacts Without RM Coordination  Unequal Revenue Benefits to Alliance Partners  Differential Revenue Gains for DAVN vs. Probabilistic Bid Price Valuation of Code Share Passengers in RM Models  Local Fare, Total Fare or Y-Fare Prorated Inputs Bid Price Sharing Between Alliance Partners  Revenue Benefits of Dynamic Information Exchange

3. 3 PODS Network Geography 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

4. 4 A vs. B/C Alliance in PODS Airline A remains unchanged, and still operates from the MSP (northern) hub:  Complete service from 20 west cities to 20 east cities Former Airline B is split into two unequal alliance partners, which both operate from the DFW (southern) hub:  Airline B operates longer haul flights from/to 10 northern cities on each side of hub, and also operates interhub flights.  Airline C operates shorter haul flights from/to 10 southern cities on each side of hub.  Code-share flights provided for all B+C “interline” connections

5. 5 Airline A Airline C Airline B

6. 6 Alliance Network Characteristics Asymmetric alliance partners:  Airline B has longer-haul flights to southern hub, lower load factors, but carries more passengers and RPMs  Airline C has shorter-haul flights and higher average load factors Implications for code-share paths:  Airline B benefits from increased code-share traffic, given lower load factor and greater proportion of distance flown (and revenue allocated) on own operated aircraft  For Airline C, code-share paths have higher alliance revenue value than own connections, but load factors are higher and revenue allocation is smaller (based on Y-fare proration revenue sharing) Valuation of code-share paths is critical to RM performance

7. 7 Base Case: EMSRb Fare Class YM 2 airlines A vs. B A vs. B/C Alliance

8. 8 Simulation Parameters RM methods  Airline A uses EMSRb Fare Class Yield Management (FCYM)  Airline B and C using BASE CASE: EMSRb Fare Class Yield Management (FCYM) Displacement Adjusted Virtual Nesting (DAVN) Probabilistic Network Bid Price Control (ProBP) RM model input fares for code share paths in connecting markets served by Airlines B+C  Local fare valuation (use local fare of same fare class) Alliance revenue sharing based on Y-fare proration of fares

9. 9 Use of DAVN by Alliance Partner(s) Standard DAVN in PODS defined as follows:  8 leg-specific virtual buckets based on total fare minus network displacement on own legs – applies only to own-connect paths  No displacement for code-share paths, which are treated as “local” paths, and valued at local fare value of same fare class Implications for code-share revenue management:  Own-connects can be nested higher or lower than locals/code- shares, depending on down-line displacement  Code-share paths are subject to same virtual bucket EMSRb booking limits as local paths

10. 10 Alliance DAVN vs. FCYM Base Case RM Method Used by B/C

11. 11 Change in Traffic Mix: DAVN/FCYM (compared to Base Case) B gains (0.78%) revenue, C gains (0.20%), Alliance gains (0.52%) DAVN allows B to increase own- connects, while locals and code- shares both decrease:  Use of displacement costs means B takes good connects  Leg-specific virtual buckets means less preference to locals and code-shares on long legs Reduced code-share flow in turn benefits C

12. 12 Change in Traffic Mix: FCYM/DAVN B gains (0.30%) revenue, C gains (0.48%), Alliance gains (0.38%) DAVN reduces own-connects for C, due to higher ALF  Mostly low class spill B still gains revenue from increased code-shares, given lower ALF and favorable revenue sharing

13. 13 Results – Alliance DAVN vs. FCYM Alliance partners benefit from one or both using DAVN:  Revenue gains even for airline with FCYM when one partner moves to DAVN O+D control  Better control of connecting traffic by DAVN partner can lead to increased revenue for FCYM partner (from either more or fewer code-share passengers)  Revenue gains are greatest for both partners and the alliance when both use DAVN.  Because of its higher market share and lower load factors, B benefits more from switching to DAVN.

14. 14 Use of ProBP by Alliance Partner(s) Standard ProBP in PODS defined as follows:  Nested Probabilistic Convergence Algorithm (Bratu, 1998)  Iterative “proration” of all ODFs on each leg in network to find convergent probabilistic leg bid prices  Additive bid price control for all local and connecting paths Implications for code-share revenue management:  Own-connects subject to sum of bid price values over both legs operated by single partner  Code-share paths are valued at local fare levels for optimization, and controlled only by bid price of own (operated) leg

15. 15 Alliance ProBP vs. FCYM Base Case RM Method Used by B/C

16. 16 Alliance ProBP vs. FCYM ProBP does not perform as well as DAVN for the alliance carriers using separate RM systems  Major difference is performance of PROBP for Airline C Revenue gains, in percent over FCYM baseline case:

17. 17 Change in Traffic Mix: PROBP/FCYM (compared to Base Case) B gains (0.70%) revenue, C loses (-0.21%), Alliance gains (0.29%) B carries fewer own-connects than with DAVN (slide 11):  Suggests PROBP bid prices are affected by code-shares, now treated as local paths  Code-shares and locals both decrease C’s losses come from change in mix, as Q locals and own-connects replace previous B and M pax

18. 18 Change in Traffic Mix: FCYM/PROBP B gains (0.12%) revenue, C gains (0.09%), Alliance gains (0.11%) Use of PROBP by Airline C leads to large decrease in own-connects:  Loss in all classes except Y  Distortion of PROBP bid prices clearly more substantial  ALF is notably lower than under DAVN Small gains for B from increased code-shares  Spread across all classes

19. 19 Summary: No RM Coordination Differences in O-D benefits to alliance partners:  Weaker airline can actually see its revenues decrease when larger partner moves to O-D control  Unequal impacts even when both partners use same O-D method  Depends on network characteristics and valuation of code-share passengers in RM optimization Differences between DAVN and ProBP revenue gains:  DAVN shows robust revenue gains in alliance combinations  In contrast, PROBP falls short in revenue performance  Bid price values are being distorted by treatment of code-share passengers as locals – impact is greater on probabilistic bid prices

20. 20 Valuation of Code Share Passengers Previous results assume connecting B+C code-share passengers valued at local fare for separate RM optimization. We also compared alternative approaches to valuation of code-share passengers in connecting (code-share) markets served by Airlines B+C: Local fare discount (use local fare of same fare class) Total path fare (no discount) Y-fare proration of connecting paths (same as revenue-sharing agreement assumed in all PODS results)

21. 21 Code Share Valuation: DAVN/DAVN

22. 22 Summary – Code Share Valuation Valuation of code share passengers for RM optimization and control has a significant effect on DAVN revenue gains:  Using total fares leads to the highest revenue gains for Airline B and total alliance, as it increases code-share traffic  Using local fares leads to slightly lower, but more evenly shared revenue benefits  Using Y-ratio fare inputs leads to the smallest alliance gains, but favors airline C At higher demand factors, relative revenue gains change:  In this network, using Y ratio fare inputs leads to higher revenue gains than either total fares or local fares as RM inputs

23. 23 Information Sharing between Partners Each alliance partner still performs RM optimization for own network separately:  Calculate leg displacement costs if using DAVN O-D controls  Calculate leg bid prices if using ProBP O-D controls  Separate network optimization assuming either local fare or total fare valuation of code-share connecting passengers “Bid price sharing” involves exchange of leg bid prices or displacement costs between alliance partners  Modeled in PODS as dynamic sharing of values for each leg in network, by booking period with a one period lag

24. 24 Alliance Information Sharing in PODS Separate Optimization Bid Price Computation Seat Inventory Control Bid Prices Airline B: Booking Request Decision Bid Price Computation Bid Prices Airline C: Seat Inventory Control Booking Request Decision Bid Price Sharing At the end of each time frame

25. 25 Alliance DAVN with Info Sharing When both alliance partners use DAVN, displacement cost sharing improves the results of DAVN compared to FCYM.  Network revenue value for code-share paths is total fare minus displacement costs on own and partner’s connecting legs  Both Airlines B and C achieve greatest revenue gains over base FCYM, as does the total alliance Revenue gains, in percent over baseline case:

26. 26 Displacement Cost Sharing: DAVN/DAVN

27. 27 Alliance ProBP with Info Sharing Bid price sharing improves the results of alliance ProBP dramatically compared to FCYM base:  With bid price sharing, ProBP performs better than DAVN (in contrast to previous results without bid price sharing).  The gains are higher when total fares are used as inputs to RM optimization model. Revenue gains, in percent over baseline case:

28. 28 Bid Price Sharing: ProBP/ProBP

29. 29 Even if partners use different RM methods and separate optimization, benefits of information sharing are substantial:  Benefits of RM coordination exceed differences between network optimization methods Revenue gains, in percent over baseline case: Alliance with DAVN/ProBP

30. 30 Bid Price Sharing: DAVN/ProBP RM Method Used by B/C

31. 31 Summary of Findings Revenue gains of O-D control can be affected by alliances:  With separate and uncoordinated RM, one partner can benefit more than the other, even causing other partner’s revenues to decrease  O-D methods perform differently (DAVN vs. ProBP), depending on network structure and valuation of code-share passengers Valuation of code share passengers for RM optimization affects both total revenue gains and partner revenue shares. Information sharing significantly improves the performance of OD control, even if partners use different OD methods.  In most cases, sharing of bid prices or displacement costs reduces the discrepancy between the revenue results of the partners.  Currently limited by technical and possibly legal constraints.

32. 32 PODS Future Work: Alliance RM Impacts of uncoordinated RM in alliances:  Additional RM methods (e.g., Heuristic EMSR Bid Price)  Code-share passenger valuation for optimization vs. control  Valuation of “own” code-share passengers vs. partner’s Information exchange between alliance partners:  Less frequent exchange of bid prices/displacement costs  “Bid price inference” by one airline of partner’s bid prices from CRS leg/class availability Larger, less symmetrical alliance network:  Longer-haul international flights; smaller code-share partners