Constrained Forecast Evaluation (CFE)

1. Constrained Forecast Evaluation (CFE) Ronald P. Menich AGIFORS Res & YM 2-5 June 2003 HNL

2. Outline • Define what constrained forecasts are • Illustrate concepts with a sequence of examples • Discuss constrained forecast evaluation (CFE) simulation process • Relate CFE to revenue opportunity measures

3. Definition • A constrained forecast is a forecast of bookings, as we expect them to be when constrained by inventory controls (usually, by recommended inventory controls from an RMS). • A constrained forecast is not a forecast constructed solely from historical constrained demand observations.

4. Value of Constrained Forecasts Constrained forecasts can be used to estimate realistic projected load factors, revenues, and seating mixes. • If capacity is 100 and the unconstrained show up forecast is 135, then the constrained onboard forecast will be no greater than 100. The 135 is the potential that could fly on a stretchable aircraft, but the 135 cannot fly on the 100 seat aircraft. Constrained forecasts also help validate RMS recommended control values.

5. Typical RMS Process Flow Unconstrain Forecast Optimize / Recommend Controls Evaluate Constrained Forecasts

6. Example Constrained Forecast • Assume • Deterministic demand. • No cancellations. Ignore no shows and day of departure activity. • Single cabin, single class/bucket within that cabin • Zero current bookings • Unconstrained final demand 135, recommended cabin authorization 110 • Example constrained final demand forecast = min( 135, 110 ) = 110

7. Future 135 final Today is days left 15

8. Authorization = 110

9. 110 final

10. Simulation To produce a constrained forecast, an RMS performs a simulation that takes as input the unconstrained forecasts, the recommended controls, and the seats available logic of the target inventory control system. The RMS simulates the acceptance and rejection of bookings requests, and the cancellations of bookings on hand. The RMS simulates forward in time from the current days left through to departure.

11. More Complex Example • Assume • Deterministic demand • Bookings at rate 30/day, days left 14-8, no cancellations. • Bookings at rate 0/day, days left 7-1, cancellation rate 6%/day • Single cabin, single class/bucket within that cabin • Zero current bookings • Unconstrained final demand 135, constant recommended cabin authorization 110 (no profiling)

12. All bookings, no cancellations 135 final All cancellations, no bookings Future Today is days left 15

13. Authorization = 110

14. 71 final

15. … And More Complex • Same unconstrained demand of 135 and recommended authorization of 110 in both examples. • When no cancellations were possible, the constrained final demand forecast was 110 • With the cancellation model, the constrained final demand forecast was only 71. Can we increase authorization sufficiently so as to hit the final target of 110 ?

16. Future 135 final Today is days left 15

17. Authorization = 171

18. 110 final Open, open, closed, closed, open, …, open, depart

19. … And More Complex The constant authorization just evaluated results in positive seats available, followed by zero seats available, followed by positive seats available close to departure. What if instead we evaluated a recommended authorization that profiles down as we get close to departure?

20. 135 final

21. Authorization = max 171, min 110

22. 110 final Open, open, closed, closed, …, closed, depart

23. … And Even More Complex Constrained forecast evaluation must not only consider cabin/compartment-level controls, but also class/bucket-level seat mix controls as well: • Booking limits • Protection levels • Parallel or serial nesting • Class/bucket-level profiling • Complicated seats available logic

24. Simulation Calculate Seats Available Accept/Reject Incremental Booking Requests Assess Cancellations Compute Current Bookings Re-Profile Recommended Controls Advance Time Clock

25. The Ideal • Ideally, an RMS would perform a detailed stochastic (involves probability) discrete event simulation to evaluate constrained forecasts. • [Or, if the inventory control system were simple enough, the RMS might be able to evaluate a closed-form constrained forecast formula.] • Such a simulation would be executed hundreds of thousands of times in order to estimate expected behavior and distributions.

26. Today’s Reality • An RMS must execute its forecasting, recommendation, and evaluation steps very quickly in order to handle the massive data processing volumes. • This processing time requirement makes discrete event simulation non-desirable at present, because it is computationally intensive.

27. Engineering Choices for Today • Use deterministic simulation • Simulate fractional booking requests and cancellations each period • Use multi-day time intervals far from departure • Assume cheap-to-high ordering of demands within one period … but Moore’s Law makes the ideal ever more viable.

28. Revenue Opportunity Measures • At any particular point in time, there are controls operative in the inventory control system. • The RMS produces recommended controls. • Evaluate constrained forecasts subject to recommended controls. • Evaluate constrained forecasts subject to current controls. • The difference in constrained forecasts between recommended and current controls is the basis for a revenue opportunity estimate.

29. Current Authorization = 110 Constrained Forecast = 71 71 final

30. Recommended Authorization = 171 Constrained Forecast = 110 110 final

31. Revenue Opportunity Measures • Constrained forecast difference = 110 (recommended) - 71 (current) = 39 seats • If the average fare were $200, then the revenue opportunity would be 39 * $200 = $7800. If the recommended controls were rejected in favor of keeping the current controls, then $7800 would be lost.

32. Summary • Constrained forecast evaluation (CFE) simulates the acceptance/rejection of unconstrained demand by the inventory control system. • Constrained forecasts can be used to produce revenue opportunity metrics and realistic revenue forecasts and projected load factors. • CFE is computationally intensive.