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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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