The document discusses revenue management concepts and components used in the airline industry. It describes how airlines use revenue management to optimize pricing and inventory availability across different fare classes. The key components are forecasting demand, optimization of pricing and inventory, transaction processing in real-time, and decision support tools to adjust availability and pricing dynamically. The goal is to maximize profits by selling the right mix of inventory to customers willing to pay higher prices.

1. Revenue Management and Dynamic Pricing: Part I E. Andrew Boyd Chief Scientist and Senior VP, Science and Research PROS Revenue Management [email_address]

3. Revenue Management and Dynamic Pricing Revenue Management in Concept

15. Revenue Management and Dynamic Pricing Managing Airline Inventory

22. Revenue Management and Dynamic Pricing Components

23. The Real-Time Transaction Processor Real Time Transaction Processor (RES System) Requests for Inventory

24. The Revenue Management System Revenue Management System Forecasting Optimization Extract, Transform, and Load Transaction Data Real Time Transaction Processor (RES System) Requests for Inventory

25. Analysts Revenue Management System Forecasting Optimization Extract, Transform, and Load Transaction Data Real Time Transaction Processor (RES System) Requests for Inventory Analyst Decision Support

26. The Revenue Management Process Revenue Management System Forecasting Optimization Extract, Transform, and Load Transaction Data Real Time Transaction Processor (RES System) Requests for Inventory Analyst Decision Support

28. Real-Time Transaction Processor DFW EWR Y Avail M Avail B Avail Q Avail 110 60 20 0 DFW-EWR: $1000 Y $650 M $450 B $300 Q

32. Extract, Transform, and Load Transaction Data PHG 01 E 08800005 010710 010710 225300 XXXXXXXX 000000 I 01 1V XXXXXXXX SNA US XXX 05664901 00000000 XXXXXXXXX XXX I R 0 0 PSG 01 OA 3210 LAX IAH K 010824 1500 010824 2227 010824 2200 010825 0227 HK OA 0 0 PSG 01 OA 9312 IAH MYR K 010824 2330 010825 0037 010825 0330 010825 0437 HK OA 0 0 PHG 01 E 08800005 010710 010711 125400 XXXXXXXX 000000 I 01 1V XXXXXXXX SNA US XXX 05664901 00000000 XXXXXXXXX XXX I R 0 0 PSO 01 EV 0409 K PSG 01 OA 1221 LAX IAH K 010825 0600 010825 1325 010825 1300 010825 1725 HK OA 0 0 PSG 01 OA 0409 IAH MYR K 010825 1455 010825 1636 010825 1855 010825 2036 HK OA 0 0 PSO 01 EV 4281 Y PSG 01 OA 4281 MYR IAH Y 010902 0600 010902 0714 010902 1000 010902 1114 HK OA 0 0 PSG 01 OA 5932 IAH LAX K 010902 0800 010902 0940 010902 1200 010902 1640 HK OA 0 0 PHG 01 E 08800005 010710 010712 142000 XXXXXXXX 000000 I 01 1V XXXXXXXX SNA US XXX 05664901 00000000 XXXXXXXXX XXX I R 0 0 PSO 01 EV 0409 K PSG 01 OA 1221 LAX IAH K 010825 0600 010825 1325 010825 1300 010825 1725 HK OA 0 0 PSG 01 OA 0409 IAH MYR K 010825 1455 010825 1636 010825 1855 010825 2036 HK OA 0 0 PSO 01 EV 4281 Y PSG 01 OA 4281 MYR IAH L 010903 0600 010903 0714 010903 1000 010903 1114 HK OA 0 0 PSG 01 OA 5932 IAH LAX K 010902 0800 010902 0940 010902 1200 010902 1640 HK OA 0 0 PHG 01 E 08800005 010710 010716 104500 XXXXXXXX 000000 I 01 1V XXXXXXXX SNA US XXX 05664901 00000000 XXXXXXXXX XXX I R 0 0 PSO 01 EV 0409 K PSG 01 OA 1221 LAX IAH K 010825 0600 010825 1325 010825 1305 010825 1725 HK OA 0 0 PSG 01 OA 0409 IAH MYR K 010825 1455 010825 1636 010825 1855 010825 2036 HK OA 0 0 PSO 01 EV 2297 L PSG 01 OA 5932 IAH LAX K 010903 0800 010903 0940 010903 1200 010903 1640 HK OA 0 0 PSG 01 OA 2297 MYR IAH Q 010903 1140 010903 1255 010903 1540 010903 1655 HK OA 0 0 PHG 01 E 08800005 010710 010717 111500 XXXXXXXX 000000 I 01 1V XXXXXXXX SNA US XXX 05664901 00000000 XXXXXXXXX XXX I R 0 0 PSO 01 EV 0409 K PSG 01 OA 1221 LAX IAH K 010825 0600 010825 1325 010825 1300 010825 1725 HK OA 0 0 PSG 01 OA 0409 IAH MYR K 010825 1455 010825 1636 010825 1855 010825 2036 HK OA 0 0 PSO 01 EV 2297 Q PSG 01 OA 0981 IAH LAX Q 010903 1420 010903 1608 010903 1820 010903 2308 HK OA 0 0 PSG 01 OA 2297 MYR IAH Q 010903 1140 010903 1255 010903 1540 010903 1655 HK OA 0 0 1 2 3 4 5

36. Decision Support

37. Revenue Management and Dynamic Pricing Non-Traditional Applications

40. Revenue Management and Dynamic Pricing Further Reading and Special Interest Groups

43. Revenue Management and Dynamic Pricing: Part II E. Andrew Boyd Chief Scientist and Senior VP, Science and Research PROS Revenue Management [email_address]

45. Revenue Management and Dynamic Pricing Single Flight Leg

53. … T T-1 T-2 T-3 1 0 n n+1 n+2 n+3 … Seats Remaining Time to Departure Cancellation No Event / Rejected Arrival Accepted Arrival … … … … … …

56. 8825 9163 9492 8478 8476 8473 20 0 … … 8823 9161 9490 8820 9158 9187 8817 20 0 … … … n n+1 n+2 n+3 Seats Remaining T T-1 T-2 T-3 1 0 Time to Departure … … … … … … 8480 V(t,n) = Expected Revenue

57. 8825 9163 9492 … n n+1 n+2 n+3 Seats Remaining T … 8480 V(t,n) = Expected Revenue V(t,n+1) – V(t,n) = Marginal Expected Revenue 345 338 330 … T … 352

58. n n+1 n+2 n+3 Seats Remaining Bid Price Control: With n+1 seats remaining, accept only arrivals with fares in excess of 345 345 338 330 … T … 352

61. Revenue Management and Dynamic Pricing Network (O&D) Control Control Mechanisms

79. Revenue Management and Dynamic Pricing Network (O&D) Control Models

81. Demand Allocation Model Max  i  I r i x i s.t.  i  I(e) x i  c e e  E (  e ) x i  d i i  I (  i ) x i  0 i  I I = set of ODIFs E = set of flight legs c e = capacity of flight e d i = demand for ODIF i r i = ODIF i revenue I (e) = ODIFs using flight e x i = demand allocated to ODIF i

82. Leg/Class Control Max  i  I r i x i s.t.  i  I(e) x i  c e e  E (  e ) x i  d i i  I (  i ) x i  0 i  I The variables x i can be rolled up to generate leg/class availability

83. Virtual Nesting Max  i  I r i x i s.t.  i  I(e) x i  c e e  E (  e ) x i  d i i  I (  i ) x i  0 i  I Once ODIFs have been assigned to leg buckets, the variables x i can be rolled up to generate leg/class availability

84. Bid Price Control Max  i  I r i x i s.t.  i  I(e) x i  c e e  E (  e ) x i  d i i  I (  i ) x i  0 i  I The dual variables  e associated with the capacity constraints can be used as bid prices

89. Resource Allocation Model Max  i  I r i x i s.t.  i  I(e) x i  c e e  E (  e ) x i  d i i  I (  i ) x i  0 i  I Many small numbers

92. Revenue Management and Dynamic Pricing Network (O&D) Control Optimization Challenges

99. Revenue Management and Dynamic Pricing E. Andrew Boyd Chief Scientist and Senior VP, Science and Research PROS Revenue Management [email_address]