1. Adaptive Mechanisms in an O&D Demand Forecasting System Silvia Riedel Operations Research Analyst Agifors June 2nd-5th, 2003 Honolulu

2. Chart 2 June 2003 Agifors 2003 Honolulu Lufthansa Systems Berlin GmbH Agenda Motivation Models of Adaptation – The General Model Models of Adaptation – Special Cases Experimental Results

3. Chart 3 June 2003 Agifors 2003 Honolulu Lufthansa Systems Berlin GmbH Motivation Management – A Motivation Revenue Management – A Motivation fchistorical data February football world cup new competitor special price offer in... morning flight has been canceled political instability instable connection time need for adaptation IThe world changes -> influences to the demand change historical data does not represent the future behaviour IForecasts are based on historical data -> it is expected that historical data represents future behaviour

4. Chart 4 June 2003 Agifors 2003 Honolulu Lufthansa Systems Berlin GmbH Agenda Motivation Models of Adaptation – The General Model Models of Adaptation – Special Cases Experimental Results

5. Chart 5 June 2003 Agifors 2003 Honolulu Lufthansa Systems Berlin GmbH The model of Attractiveness and Attractiveness Changes - Assumptions Revenue Management – A Motivation Revenue Management – A Motivation Iadaptations can always be defined as a relative change or a transfer of demand Iadaptations need a description of Itime: concerned departures Ilevel: the level of transfer (transfer from-> to) Ideviation: a description of how the demand changes Iother adaptation details Forecasting system general need for adaptation forecasting system:general model of adaptation February football world cup new competitor special price offer in... morning flight has been canceled political instability instable connection time

6. Chart 6 June 2003 Agifors 2003 Honolulu Lufthansa Systems Berlin GmbH The model of Attractiveness and Attractiveness Changes - Terms Revenue Management – A Motivation Revenue Management – A Motivation IAttractiveness: the stable world behaviour –no random noise –no seasonal influences –no events or bad data –no other changes that influence only few departures IShort Term Influence: –changes influencing a small number of departures IAttractiveness Change: –change of the attractiveness compared to the previous departures Demand = Attractiveness + Short Term Influences + Random Noise

7. Chart 7 June 2003 Agifors 2003 Honolulu Lufthansa Systems Berlin GmbH The model of Attractiveness and Attractiveness Changes - History Adaptation versus Forecast Adaptation IModel1: Adapt historical data (or the smoothed or filtered data) to the situation of the departure to forecast IOur approach: IAdapt the historical data to today’s attractiveness –remove short term influences –adapt to attractiveness changes IAdapt the forecast to the future situation –insert attractiveness changes + short term influences IModel2: Use the pure historical data and adapt the forecast

8. Chart 8 June 2003 Agifors 2003 Honolulu Lufthansa Systems Berlin GmbH The model of Attractiveness and Attractiveness Changes - When to adapt what Management – A Motivation Revenue Management – A Motivation historical demand data forecast demand data today forecast attractiveness change forecast short term influences forecast attractiveness adapt attractiveness changes, adapt short term influences, forecast attractiveness changes forecast short term influences adapt attractiveness to today‘s situation, learn attractiveness learn attractiveness changes learn short term influences

9. Chart 9 June 2003 Agifors 2003 Honolulu Lufthansa Systems Berlin GmbH The model of Attractiveness and Attractiveness Changes - How to adapt Revenue Management – A Motivation Revenue Management – A Motivation IAdaptation of the demand IAdapt not only the demand at departure IAdapt also the development of the demand IDescription of the attractiveness change of the departure date n Irepresented as a sum of changes related to the attractiveness caused by different real world situations like events, schedule changes etc. Ican be represented as absolute changes or relative changes (with n indicating the departure and j,k the number of the change) Ithe complete attractiveness change is calculated by

10. Chart 10 June 2003 Agifors 2003 Honolulu Lufthansa Systems Berlin GmbH The model of Attractiveness and Attractiveness Changes - The security aspect Management – A Motivation Revenue Management – A Motivation IAdaptation is risky because Iusing factors is risky in combination with a ‘problem of small numbers’ Iassumptions of the deviation are often wrong for the fine level of forecasting IProtection of the used data Iadaptations of the attractivness changes to the future behaviour based on demand information of future departures Idata specific evaluation concerning the fine level Iconfidence information Iextended analysis of adapted values’ ranges Ibad data and outlier analysis

11. Chart 11 June 2003 Agifors 2003 Honolulu Lufthansa Systems Berlin GmbH Agenda Motivation Models of Adaptation – The General Model Models of Adaptation – Special Cases Experimental Results

12. Chart 12 June 2003 Agifors 2003 Honolulu Lufthansa Systems Berlin GmbH Special Cases - Adaptation to the flight specific demand (random noise) Revenue Management – A Motivation Revenue Management – A Motivation The flight specific demand in early DCP’s is highly correlated to the flight specific demand at the departure. basic forecast now bookings days prior to departure bookings current flight basis forecast result of forecast adapted forecast

13. Chart 13 June 2003 Agifors 2003 Honolulu Lufthansa Systems Berlin GmbH Special Cases - Adaptation to the season and special events Revenue Management – A Motivation Revenue Management – A Motivation ISeasonal behaviour and special events Iinfluence the demand at the date of departure Ibut also determine the point of time when the demand sets in in times of strong demand the demand will set in early

14. Chart 14 June 2003 Agifors 2003 Honolulu Lufthansa Systems Berlin GmbH additive adaptation multiplicative adaptation basis forecast demand days to departure Special Cases - Adaptation to the season and special events Management – A Motivation Revenue Management – A Motivation additive adaptation multiplicative adaptation basis forecast adapted forecast high season adapted forecast low season demand days to departure IOur approach to an adaptation of the basic forecasts acts on the assumption of two models: Imultiplicative adaptation: the additive/missing demand sets in like the normal demand Iadditive adaptation: all additive/missing demand sets in/ is missing at the beginning of the booking period I(nonlinearly) weighted sum of the two adaptations gives the adapted forecast

15. Chart 15 June 2003 Agifors 2003 Honolulu Lufthansa Systems Berlin GmbH Special Cases - Adaptation to Schedule Changes Revenue Management – A Motivation Revenue Management – A Motivation 50 % 30 % 0 00 23 59 LH 3856LH 3790 (new) LH 3988 IThe adaptations to schedule changes as special cases of attractiveness changes: Ithe initialization/adaptation is done virtually until the first departure Iafter the first departure the history is initialized/changed permanently

16. Chart 16 June 2003 Agifors 2003 Honolulu Lufthansa Systems Berlin GmbH Special Cases - Adaptations to Market/Price Changes Revenue Management – A Motivation Revenue Management – A Motivation today 1. adaptation of the 3. adaptation of the forecasts 2. basic forecasts adaptation period departure date demand historical data IMarket/Price Changes are often characterized by an adaptation period Ithe input for the attractiveness changes can be generated from market/ pricing models and by expert knowledge

17. Chart 17 June 2003 Agifors 2003 Honolulu Lufthansa Systems Berlin GmbH Special Cases - Other Potential Needs for Adaptation Management – A Motivation Revenue Management – A Motivation Iavailability situation Isell up (demand displacement between fareclasses) Idemand displacement between points of sale Idemand displacement between departures (weekend-> middle of the week) Ivalidity situation Iadaptation to the booking behaviour if flights are introduced after the beginning of the booking period Ifareclass structure Iadaptation to new/ changed/ removed fareclasses

18. Chart 18 June 2003 Agifors 2003 Honolulu Lufthansa Systems Berlin GmbH Agenda Motivation Models of Adaptation – The General Model Models of Adaptation – Special Cases Experimental Results

19. Chart 19 June 2003 Agifors 2003 Honolulu Lufthansa Systems Berlin GmbH Experimental Results Management – A Motivation Revenue Management – A Motivation Iforecast quality can be increased significantly by the use of adaptation techniques Ia general model helps to answer the methodical questions for all needs of adaptation Iafter implementation in Profitline the following results have been observed:

20. Chart 20 June 2003 Agifors 2003 Honolulu Lufthansa Systems Berlin GmbH Thank you for your Attention !