1. o & d Forecasting for O & D Control
Arjan Westerhof Decision Support

2. AGIFORS Yield Management June 2003
Outline
Introduction: O&D control and forecasting
Why O&D’s are usually o&d’s
3 alternatives for handling o&d’s in the forecast
Conclusions and discussion
April 14, 2017
AGIFORS Yield Management June 2003

3. AGIFORS Yield Management June 2003
O&D Control
KLM implemented O&D revenue management in 2000 (first sub-networks) / 2001 (entire network)
Systems based on
O&D demand forecasting
O&D fare forecasting
network optimization
Organization based on O&D / Point of Sale
April 14, 2017
AGIFORS Yield Management June 2003

4. AGIFORS Yield Management June 2003
Bottom Up vs Top Down
KLM uses bottom up demand forecasting
This seems more powerful to capture the various cultural differences in KLM’s home market than top down forecasting
April 14, 2017
AGIFORS Yield Management June 2003

5. AGIFORS Yield Management June 2003
From Agifors YM 2002
April 14, 2017
AGIFORS Yield Management June 2003

6. AGIFORS Yield Management June 2003
KLM versus LH LH KL
Are we almost the same ?
April 14, 2017
AGIFORS Yield Management June 2003

7. Every customer is different!
O&D is not the only dimension …
… other dimensions that might influence booking behavior and passenger revenue:
Customer or Point of sale (agent / country / corporate account / …)
Booking class / ticket restrictions
Departure Day of Week
Departure season
Special events
Etc.
Define ‘product’ at a more detailed level
April 14, 2017
AGIFORS Yield Management June 2003

8. AGIFORS Yield Management June 2003
The product curve
%O&D’s
%Pax
KL O&D’s
%Pax
%Products
LH Products (?)
KL Products
We are different!?
April 14, 2017
AGIFORS Yield Management June 2003

9. Why is KLM different from LH?
Though Amsterdam is a great place to visit
±70% of our passengers use Amsterdam only for connecting to other destinations
April 14, 2017
AGIFORS Yield Management June 2003

10. AGIFORS Yield Management June 2003
Introducing ...
The o&d World
‘Small’
%Pax
‘Medium’
‘BIG’
KL Products
%products
April 14, 2017
AGIFORS Yield Management June 2003

11. AGIFORS Yield Management June 2003
O&D or o&d?
> 70% of the products are ‘exotic’ o&d’s (not sold regularly)
If forecasts are created for these o&d’s:
The quality of these forecasts can hardly be measured
> 70% of computation time will be involving ‘meaningless’ numbers
The forecast will be confusing to the users
April 14, 2017
AGIFORS Yield Management June 2003

12. Solutions for o&d Forecasting
Do nothing special
Forecast aggregation
Forecast elimination
April 14, 2017
AGIFORS Yield Management June 2003

13. AGIFORS Yield Management June 2003
Do nothing special
Network optimization will ‘aggregate’ the o&d’s to the leg-level when determining bid prices and buckets
Advantages:
All detailed information is available in the forecast
Acceptance/rejection in the RES system aligned with forecast/optimization system
Disadvantages:
Forecast quality can not really be measured
Much data with little information (user/computing)
April 14, 2017
AGIFORS Yield Management June 2003

14. Solutions for o&d Forecasting
Do nothing special
Forecast aggregation
Forecast elimination
April 14, 2017
AGIFORS Yield Management June 2003

15. AGIFORS Yield Management June 2003
Forecast aggregation
Drop one or more of the dimensions in the product definition for products with insufficient volume
For example:
Drop O&D dimension by splitting o&d’s in legs
Drop point of sale dimension
Drop booking class dimension or aggregate to cabin level
April 14, 2017
AGIFORS Yield Management June 2003

16. AGIFORS Yield Management June 2003
Forecast Aggregation
Advantages
Quality of aggregated forecasts can be measured
Helps to focus on important flows
Disadvantages
Bookings are evaluated in the RES system with different values than used in optimization
Unconstraining uses different revenue values than the ones used during passenger acceptance
Products with different booking behavior might be aggregated
April 14, 2017
AGIFORS Yield Management June 2003

17. AGIFORS Yield Management June 2003
Forecast quality?
Hard to measure:
Many O&D’s/Flights are constrained during some time in the booking cycle
There are almost no stable reference periods anymore (Sep 11 / War / SARS / …)
Evaluating forecasts on the leg level might bias the evaluation to the benefit of the aggregated forecasts
April 14, 2017
AGIFORS Yield Management June 2003

18. AGIFORS Yield Management June 2003
Forecast quality
Leg/cabin level, open flights on two lines
Note: even on an open flight some products may not be for sale due to constraints on other flights
Not much difference in forecast
Which one is better?
Rem. demand
TIME
April 14, 2017
AGIFORS Yield Management June 2003

19. Aggregation vs. Do Nothing
Average bid price
Some differences but not too big with 50% fewer forecast products
TIME
April 14, 2017
AGIFORS Yield Management June 2003

20. Solutions for o&d Forecasting
Do nothing special
Forecast aggregation
Forecast elimination
April 14, 2017
AGIFORS Yield Management June 2003

21. AGIFORS Yield Management June 2003
Forecast elimination
Throw out the o&d’s
Re-map these o&d’s to products with significant demand (O&D’s)
Experimental results indicate that this does not result in a good forecast
April 14, 2017
AGIFORS Yield Management June 2003

22. AGIFORS Yield Management June 2003
Conclusion
Most of the products that are sold are ‘exotic’, there are much more o&d’s than O&D’s
If these exotic products are being forecast, they are polluting the system
Aggregation solves the small number problem but the quality of the forecast is not always better
As a result, the choice between aggregating or not aggregating seems mainly a matter of personal preference (?)
April 14, 2017
AGIFORS Yield Management June 2003

23. AGIFORS Yield Management June 2003
Questions ?
April 14, 2017
AGIFORS Yield Management June 2003