Forecast Versus Actual Analysis (FVA) Josef Loew Senior Director Yield Management

1. Forecast Versus Actual Analysis(FVA)Josef LoewSenior Director Yield Management

2. America West Airlines • 9th largest US airline • 125 Aircraft • Daily Flights: 650 • Hubs: Phoenix / Las Vegas / Columbus, OH • Annual Revenue: $2.2 Billion

3. Route Map

4. Introduction • Yield Management at America West has traditionally consisted of analysts making small changes to seat allocations based on exception reporting and analyst “experience and know-how”. • Over the past year and a half we have introduced the idea of Yield Management as a production process – the YM Virtual Factory -- and implemented applicable Total Quality Management (TQM) concepts. • This presentation describes how Forecast Versus Actual (FVA) information was incorporated into the business process.

5. Data Forecast Process Optimization Process Allocations Why Focus on Forecast Quality? • The accuracy of the forecast is the principle driver of seat allocation decisions.

6. Creating a FVA Data Structure • AWA currently uses a leg forecasting leg control YM System that overwrites previous forecasts. • Extract forecasts from the YM System every night and store the data in an FVA database.

7. Improving Data Integrity with FVA • A weekly process identifies suspicious data (flights) in the inventory history. • Screen for excessively high unconstrained demand value • Identify forecast with very high error • Take corrective action. • Exclude or edit bad data points (flights) • Split history used in forecast

8. Example for using FVA – March 2000 Flights • March has some of AWA’s highest demand flights. • FVA showed that forecasting was not robust enough to adequately forecast peak travel periods in March. • Selected a group of 325 flights that were severely underforecast in ’99 with similar forecasts in January 2000 (60 days prior to departure). • Used FVA data to calculate applicable user influence (UI) forecast correction. The FVA derived UI was 5 to 10 times stronger than what the analysts had already applied using traditional analysis. Close monitoring and early results show very encouraging results.

9. Preliminary Results of March Experiment • Forecast was corrected 60 days prior to departure.

10. Summary • FVA based statistical process control helps analysts identify problems as well as show the correct level of user influence (UI) to apply. • Bias: Easy to correct • Trend: Analyst intervention reduces forecast model’s lag • High Variations: May have correctable cause, but is difficult to connect in general • Environmental Change: UI tables • Fare sales • Schedule changes