Lufthansa Looking for Feedback Performance Measurement in Revenue Management Stefan Pölt Lufthansa German Airlines AGIFORS Reservations & Yield Management Study Group Bangkok, May 2001

Lufthansa 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,....)

Lufthansa 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 ?

Lufthansa 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 ?

Lufthansa hard factsROM input dataeasy to getmore complex reasonable qualityquality problems, uncertainty of unconstraining isolation of RMimpossiblereasonable contribution target groupupper managementRM department and RM department Comparison of Both Methods

Lufthansa 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

Lufthansa 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 100% 65% 0% maximum minimum actual opportunity realization revenue

Lufthansa 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

Lufthansa 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

Lufthansa 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)

Lufthansa 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

Lufthansa First Results Actual revenue varies a lot over departure dates Which flights have been controlled well and which not ?

Lufthansa Separation of RM Contribution ROM gives a clear picture which half of the departures have been controlled better

Lufthansa Influence of Unconstraining Uncertainty in unconstraining does not distort results too much Unconstraining error is 50% of forecast error (MAPE = 25%)

Lufthansa Correlation with Forecast Error ROM realization is not too much correlated with forecast error

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

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

Lufthansa 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