1. Defining Revenue Management as a Game Gert Hartmans Agifors - Berlin 2002

2. Objectives Better understanding revenue management for system development Breakdown processes in small sub-processes Represent each sub-process as a game Link all games to reflect total process and enable review of strategies Derive system implications

3. Conceptual view of RevMgt 2000 Forecast Display Availability Reservations & Departure Control Group Desk Fares & Rules Capacity Optimise FaresCapacity Competitor

4. But daily issues... 2002 Corporate account access/effects Loyalty Program redemption access Alliance partner access Interline/SPA (non-) access Product differentiation (Economy +) CRS costs Ticket Class / Booked Class mismatch Waivers by sales

5. Systems - ‘ modular design ’ Sales contracts Pricing Inventory Control Account- ing LoyaltySPA’s Reserva- tions Departure Control

6. Systems - links Pricing Inventory Control Account- ing LoyaltySPA’s Reserva- tions Sales contracts Departure Control

7. Systems in Alliance Pricing Inventory Control Account- ing LoyaltySPA’s Reserva- tions Sales contracts Departure Control Pricing Inventory Control Account- ing LoyaltySPA’s Reserva- tions Sales contracts Departure Control

8. Game Concept Players/Rules Rewards/Loss Choices/Strategy Enables modeling –Expectations/Guessing –Deception/Cheating –Cooperation/Retaliation

9. Inventory Control - Game Players Objectives –Maximize Revenue Rules –Players set availability access per round without prior knowledge of others steps –Two classes: H earns 1 points, L earns 0.5 points –L books first Actions –Open class or Close class

10. Inventory Control - Game 1.1:2 Capacity 2 per player, Demand H1 L1 per round Pay off matrix for Blue –Per round one H-pax books valued at 1 and one L-pax books valued at.5 Dominant strategy = All open Blue Pay-off Matrix

11. Inventory Control - Game 2.1:2 Capacity still 2, demand H2 L1, L books first Pay off matrix for Blue –Per round two H-pax books valued at 1 and one L-pax books valued at.5 Dominant strategy = H Open/ L Closed Blue Pay-off Matrix

12. Inventory Control - Game 1.4:2 Capacity still 2, demand H1 L4, L books first Pay off matrix for Blue –Per round one H-pax books valued at 1 and four L-pax books valued at.5 Dominant strategy = Keep L open * Reward for H needs to be > 1.14 to close L Blue Pay-off Matrix spill

13. Usage of concepts –Dominant strategy best for both, can be used for guessing expected competitor action (leads to Nash Equilibrium) –If competitor deviates from dominant strategy alternatives do not seem more profitable, avoid copy-cat marketing –When reality/rules differs for both players, different strategies may be advantageous (larger capacity, different fares, lower frequency etc.)

14. Sub-processes Inventory control game Pricing game Fare Rule game Sales game Sales - Agents game Agents - Customer game … products … loyalty ….

15. Pricing - Game 2.2:2 Capacity 2 per player, demand H2L2, L books first Pay off matrix for Blue –Per round two H-pax books valued at 1 and two L-pax books valued at.5 Dominant strategy = Offer H fares only Blue Pay-off Matrix

16. Fare Rule - Game 2.2:2 Capacity 2 per player, demand H2L2 L books first, H books L if no rule exists Pay off matrix for Blue –Per round two H-pax books valued at 1 and two L-pax books valued at.5 Dominant strategy = Add fare rules Blue Pay-off Matrix

17. Sales Game 4.4:4 Capacity 4 is shared per 2 players (sales office) based on bids H:L 3:1 or evenly 2:2 (gray area) Demand H2 L2 per round per player, 4 per market Dominant strategy = bid high and sell high, but potential for cheating Blue Pay-off Matrix

18. Sales - Agents Game Commission % differs, Demand 1 per round Pay off matrix for Blue (Sales office) Dominant strategy = Surpass competitor commission levels if possible Blue Pay-off Matrix

19. Agents - Customer Game Discount differs, Demand 1 per round Pay off matrix for Blue (agent) Dominant strategy = Match or surpass competitor agent Blue Pay-off Matrix

20. Value Chain Airlines - Inventory game Airlines - Pricing game Airlines - Fare Rules game Sales game Agents - Customers game Sales - Agents game

21. Conclusions Market position is chain of games Organization, systems do not reflect inter- relations which are reflected in processes Open to cheating and deception System alignment and interfaces should help in achieving more optimal results

22. Enforcing rules Currently: Booking =/= Sale =/= Fare Commission variation, rule waivers, or over stating fares, leads to incorrect information Objective: 1 product = 1 contract = 1 booking = 1 sale = 1 fare

23. Place Commercial Here