Lufthansa

Looking for FeedbackPerformance Measurement in Revenue Management

Stefan Pölt

Lufthansa German Airlines

AGIFORS Reservations & Yield Management Study Group

Bangkok, 8.-11. May 2001

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Why to Measure RM Performance ?

• To track performance over time

• To identify weaknesses in RM systems

• To quantify and objectify the impacts of RM decisions

• To isolate contribution of RM to the overall performance (influenced by pricing, scheduling, sales, economy, ....)

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• Forecasts (demand, no-shows)

• Passenger mix (yield)

• Overbooking quality (spoiled seats vs. oversales)

• Others (quality of pricing decisions, ...)

• At different aggregation levels - from single flight events to monthly statistics on whole regions

What to Measure ?

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• By hard facts:– total revenue– market share– average seat load factor (SLF)– yield (revenue per passenger)– unit revenue (revenue per capacity)– number of denied boardings per 1000 passengers– closed flight SLF– ...

• By simulation (revenue opportunity model, ROM): How much of the difference between perfect and no control has been captured ?

How to Measure ?

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hard facts ROM

input data easy to get more complex

reasonable quality quality problems,

uncertainty ofunconstraining

isolation of RM impossible reasonablecontribution

target group upper management RM departmentand RM department

Comparison of Both Methods

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• Combine different measures to increase reliability (cross-checks)

• Relative performance (compared to last month or last year) is more stable and meaningful than absolute numbers

• Performance measurement is some kind of post analysis – it can’t replace early warnings (e.g. booked SLF)

General Aspects

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General idea of ROM: Calculate opportunity as difference between maximum (perfect control) and minimum (no control) and measure how much has been captured

Revenue Opportunity Model

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• There are several variants of ROM– look at departure only vs. simulation over booking period

– calculate minimum by filling up from low to high vs. filling up in realistic booking order

– an overall performance number vs. separation of fare mix, overbooking, upgrading and ...

• Simulation over time allows detailed analysis – rejected bookings that should have been accepted

– accepted bookings that should have been rejected

• Measures of fare-mix and overbooking are not independent

Revenue Opportunity Model

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• Handling of specific cases

– Forced bookings– Group bookings– Capacity changes– pax out > capacity– max = min > actual

• Data quality (e.g. check-in numbers)

• Estimation of denied boarding costs

• O&D control

• Assumption of independence of legs

• ...

Challenges

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Motivation for a Simulation Study

• Are hard facts sufficient or is there additional value by ROM ?

• How much is ROM measure correlated with forecast errors ?

• How does the unconstraining error influence the results ?

• What are the most stable and reliable measures ?

• Additional insights in RM trade-offs (yield vs. SLF, spoilage vs. denied boardings)

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Simulation Layout

• Generate demand curves based on realistic booking patterns

• Simulate booking process, for every snapshot– forecast demand to come (adjustable forecast errors)– calculate EMSR booking limits– generate booking requests based on demand figures– accept / reject booking requests based on booking limits

• Simulate no-shows

• Unconstrain historical booking curves

• Calculate forecast errors and performance measurement statistics

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First Results

Actual revenue varies a lot over departure dates

Which flights have been controlled well and which not ?

Departures

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Separation of RM Contribution

ROM gives a clear picture which half of the departures have been controlled better

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Influence of Unconstraining

Uncertainty in unconstraining does not distort results too much

Unconstraining error is 50% of forecast error (MAPE = 25%)

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ROM True Demand

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Correlation with Forecast Error

ROM realization is not too much correlated with forecast error

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Forecast Demand Error

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Spoilage Analysis

Spoilage (empty seats despite excess demand) is caused by over-estimating show-up rates and/or (high fare) demand

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Forecast Demand Error

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Denied Boarding Analysis

Denied boardings (oversales) are caused by under-estimating show-up rates

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Forecast Demand Error

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Summary

• ROM does a good job in isolating the RM contribution

• Due to the uncertainty in ROM it should be cross-checked with other measures (SLF, yield, ...)

• Relative performance measures (compared to last month or last year) are more meaningful than absolute numbers

• Fare-mix and overbooking contribution are interdependent and can’t be separated easily