Implementing an O&D System at KLM

1. Implementing an O&D System at KLM Agifors yield management study group March 23, 2000 Arjan Westerhof Arjan Westerhof

2. Outline • Project overview • Short description of all major phases. Main focus on: • Specification • Business tests • Current status and concluding remarks Arjan Westerhof

3. Project Overview Approach: entirely new core system • O&D based data • Completely new demand forecasting • Completely new fare forecasting • Network optimization • New hardware • New programming methods Arjan Westerhof

4. 1997 1998 1999 2000 ProjectOverview Detailed Specification and unit testing Integration testing and performance tuning Business testing & system improvement Specification Vendor choice First flight live Small network live Large network live Performance +/-OK Specs agreed Unit test complete Bus. test complete Arjan Westerhof

5. 1998 1997 1998 1999 2000 Project Overview Acc. test Integra-tion test Detailed specification and Unit test Detailed spec. and Unit test Integrationtesting and performance tuning Business testing & system improvement Arjan Westerhof

6. 1997 1998 Specification Complex, state of the art system (PNR based) Why? This is for an airline like KLM the only method that will give accurate short term demand forecasts Arjan Westerhof

7. 1997 1998 Specification: Real Data Example I Different types of passengers using the same flight and class have different booking curves Arjan Westerhof

8. 1997 1998 Specification: Real Data Example II Different types of passengers using the same flight and class have different booking curves Arjan Westerhof

9. Specification: Simplified Example Different types of passengers using the same flight and class have different booking curves When aggregated data is used, inaccurate forecast of the demand to come will result. Simplified example: Arjan Westerhof

10. 20 10 0 0 20 20 20 10 Specification: Simplified Example Arjan Westerhof

11. 20 10 0 0 20 20 20 10 Specification: Simplified Example Arjan Westerhof

12. Specification: Simplified Example Using the low level (PNR) data will give the correct forecast But ... complex system is much more work than simple system Arjan Westerhof

13. 1997 1998 Unit Testing • Unit testing with self constructed testcases • Limited data in order to be able to determine the expected result with manual or spreadsheet calculation • Constructed in such a way that ‘all’ logical cases are tested • Started with input data modules to start buildup of historical O&D data asap • Problems with data quality • Hard to get the tailor made software correct Arjan Westerhof

14. 1997 1998 1999 Integration Testing Modules worked quite good together, but… • Large amounts of real life data contain strange values of which some were not tested • Much more data than expected • Performance problems Redesign for performance (and again unit testing,...) Arjan Westerhof

15. Business Testing 1997 1998 1999 2000 Will the system generate extra revenue? Arjan Westerhof

16. Business Testing 1997 1998 1999 2000 What? Analyses of: • Fare forecasting • Demand forecasting • Optimization Arjan Westerhof

17. Business Testing 1997 1998 1999 2000 How? • Data analyses on the O&D data • Comparison with current systems leg data • Expert opinion on leg and O&D data Arjan Westerhof

18. Business Testing: Fare Forecast 1997 1998 1999 2000 Percentage of tickets that has certain forecast error. Forecast can be evaluated for: • Input data • New data %Tkts<=10% %tkts<=20% Overall x1 % y1 % Top 100 country – country x2 % y2 % Top 20 POS x3 % y3 % Top 100 city – city x4 % y4 % Top 20 city – city x5 % y5 % Arjan Westerhof

19. Business Testing: Fare Forecast 1997 1998 1999 2000 Consistency of forecasts (higher subclass should in general have higher fare). %Consistent ranking Overall X1 % Top 20 country – country X2 % Top 20 POS X3 % Top 20 city – city X4 % Arjan Westerhof

20. 1997 1998 1999 2000 Business Testing: Demand Forecast Do the forecasts match the input data? Arjan Westerhof

21. 1997 1998 1999 2000 Business Testing: Demand Forecast Comparison forecasts with reality (note: reality is constrained, forecast is unconstrained) Arjan Westerhof

22. 1997 1998 1999 2000 Business Testing: Optimization Look how forecasts and bidprices develop in time KL 1024 DEP=LHR ARR=AMS 20-Feb-2000 160 140 O&D FORECAST 120 100 Seats 80 CURRENT SYSTEMS FORECAST 60 40 SEATS SOLD BIDPRICE 20 0 0 20 40 60 80 Days to departure Arjan Westerhof

23. 1997 1998 1999 2000 Business Testing: Optimization Comparison of overbooking levels Arjan Westerhof

24. Current Status 1997 1998 1999 2000 • Small network live • Not yet completely happy with the results still working on system improvement • Expect to implement major improvements in April (currently in unit testing) Arjan Westerhof

25. 1997 1998 1999 2000 Lessons Learned • Doing everything at the same time has some advantages, but a more gradual approach might be better • Not recommended to implement new system on new hardware • Everything takes much longer than expected • The time needed to get from a running system to a system that generates business value is very long • Tough project with various parties • Complex system makes all the above things harder but is the only way generate the promised revenue. Arjan Westerhof

26. Questions ? Arjan Westerhof