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Good Things Take Time – Building up an O&D-Forecaster. Stefan Poelt Presentation to the AGIFORS RES & YM Study Group Berlin, 16 - 19 April 2002. Agenda. How does a perfect project look like ?. What’s the scope of our O&D project ?. Some traps to watch. O&D Forecaster system overview.

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Good things take time building up an o d forecaster

Good Things Take Time –

Building up an O&D-Forecaster

Stefan Poelt

Presentation to the AGIFORS RES & YM Study Group

Berlin, 16 - 19 April 2002


  • How does a perfect project look like ?

  • What’s the scope of our O&D project ?

  • Some traps to watch

  • O&D Forecaster system overview

  • What’s the current status ?

  • Happy end !

How does a perfect project look like
How does a perfect project look like ?

A perfect project

  • Is in time

  • Is in budget

  • Covers the scope

  • Delivers quality

Our project

  • Is not quite in time

  • Is in budget

  • Scope has been adjusted

  • Delivers quality

What s the scope of our o d project



O&D Optimization

Married Segment Control




Detailed level

Individual pax / groups

Gross bookings / cancellations

Aggregation of ‘exotic’ O&Ds

Benchmark study

Project stopped after 11 Sep 2001

Re-activation in 2003

Prevents cheating

Implemented at Inventory Dept.

Coordination task

PNR attributes

Decision tree learning

Mixed with traditional no-show forecasts

Implemented by Lufthansa Systems Berlin

No decision about vendor

Implemented by CRSs

What’s the scope of our O&D project ?

Simple vs complex forecast method


Weather Forecast Station

Stone is wet

Stone is dry

Shadow at ground

White on top

Stone can’t be seen

Stone swings

Stone is hopping

Stone disappeared


No rain







Simple vs. complex forecast method

  • Separation of individual and group bookings

  • Separation of gross bookings and cancellations

  • Flexible aggregation of low demand traffic flows

  • Complex methods are more difficult to calibrate

  • Complex methods can better model reality

  • Trade-off between accuracy and stability

Better no show forecasts allow better overbooking
Better no-show forecasts allow better overbooking

  • Traditional no-show forecasts are based on booking class and routing

  • PNR is a richer data source (e.g. ticketing info, special service request etc.)

  • ‘There’s gold in the PNR data mine’ (Louis Busuttil, PROS Conference 1997)

  • ‘PNR-based no-show forecast’ (Klaus Weber, AGIFORS Conference 2000)

  • PNR-based methods can be applied to booked passengers only - they must be mixed with traditional no-show forecasting

  • PNR-based methods (e.g. decision trees) can also be applied to cancellation forecasting

O d forecasting without o d optimization
O&D forecasting without O&D optimization

  • Intermediate step: O&D forecasts aggregated to leg-level and used in leg-optimization

  • Better quality of leg demand forecasts

  • Better quality of (PNR-based) no-show forecasts  lower overbooking risk

  • O&D information, better understanding of travel flows

  • Better communication and co-operation with Pricing and Scheduling / Flight Planning

Reasons for delay
Reasons for delay

  • Too optimistic project plan

  • Internal staffing (6-7 project members, 4 users – part-time)

  • Under-estimation of noise in PNR data

  • Frequent change of project management

  • Scientific driven project – late involvement of users

  • Research and calibration effort for new topics (e.g. cancellations)

  • Re-organizations

  • 11. September 2001

Renaming the project does not help
Renaming the project does not help

  • There were plans to rename the project to ‘O&D-Forecaster’

  • This might help to confuse controllers but does not help to solve the problems

  • Project name: ‚Bid Price 2‘

  • Original scope was adjusted, O&D-optimization was stopped after 11. September 2001

Shit happens
Shit happens

  • There are many situations where something can go wrong

  • The business processes haven’t been addressed at the beginning

  • Later on we realized that important functionalities are missing

  • Noise and complexity of PNR data have been under-estimated

  • Aggregation of no-show information from O&D to leg is problematic

A precise specification avoids complaints later on

You‘ve built

it up reversed !

A precise specification avoidscomplaints later-on

  • Innovative IT-projects require a lot of research

  • Alternation of research and prototyping / implementation

  • We decided to refine the specification during the project

  • Problems keeping specification and documentation up to date

  • Testing with ‘fuzzy’ specification is challenging

A good documentation is important



A good documentation is important

  • Documentation often is prioritized down

  • Good documentation improves user’s confidence

  • 3 documents:

    • system administration

    • user manual / online help

    • process / workflow

In a perfect project project managers have the right perspective
In a perfect project: Project managers have the right perspective

  • Focusing on the most important topics

  • Understanding the subject (RM and Forecasting)

  • Good communication and motivation skills

  • Currently 2 project managers, one focusing on management, the other focusing on methods - learning from each other

In a perfect project motivation of the project team is not an issue
In a perfect project: Motivation of the project team is not an issue

  • Working for years on the same thing is boring

  • We are the 4. generation of project managers

  • The project survived 2 re-organizations

  • Increase motivation by changing responsibilities

  • Success is the best motivator !

Our new toy

History Builder an issueUnconstrainingOutlier Detection

Data Loading

Forecast Kernel






Event &SeasonalityManager










Forecaster Pool


Bad Data Processor






History Pool


Forecast DB

Flight Monitor

Forecast Building




Model Mixer


Leg Optimizer

Interface DB

Our new toy

Aggregation solves the small numbers problem
Aggregation solves the small numbers problem an issue

  • There are many low traffic O&Ds

  • 20:90 rule - 20% of all O&Ds cover 90% of the traffic

  • Flexible aggregation of low traffic O&Ds is a key factor

Effects of schedule changes are handled automatically

neighbored flights an issue

flight A

flight B



flight C

new flight

Effects of schedule changes are handled automatically

  • A major schedule change can affect thousands of O&Ds

  • Concept of Lufthansa Systems Berlin handles new, moved and cancelled flights

  • Demand is automatically adjusted based on ‘neighbored’ flights/paths

  • ‘Distance’ to neighbored flights mainly depends on departure and arrival times

  • Approximation of market (passenger choice) model

This year we will take the last hurdle
This year we will take the last hurdle an issue

  • Forecast quality (aggregated to leg-level) is better than for current system

  • Data loading performance is o.k.

  • Business processes have been re-designed and matched with the tool

  • Flight by flight migration of new forecast system (Jun - Nov 2002) minimizes risk

  • Business re-organization will go hand in hand with migration

  • Parallel project of O&D-reporting is started

  • Married Segments Control is implemented in Amadeus and Galileo

The forecast results are very promising

Booking period departure an issue

Booking period departure

The forecast results are very promising

  • Validation of O&D-forecaster at representative test flights

  • Distinction of open and closed flights to separate unconstraining effects

  • Open flights are less important but have reliable actual values

Happy end
Happy end ! an issue

  • The O&D forecaster will be completely in operation this year

  • Very good cooperation with Lufthansa Systems Berlin

  • O&D not always means Odd & Delayed !