1 / 27

O&D Forecasting Issues, Challenges, and Forecasting Results

O&D Forecasting Issues, Challenges, and Forecasting Results. John D. Salch PROS Revenue Management, Inc. jsalch@prosrm.com. Forecasting Issues / Challenges. data processing time modeling dynamics. Data …There Is More Than We Know What to Do With. Data Collection.

acacia
Download Presentation

O&D Forecasting Issues, Challenges, and Forecasting Results

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. O&D ForecastingIssues, Challenges, andForecasting Results John D. Salch PROS Revenue Management, Inc. jsalch@prosrm.com

  2. Forecasting Issues / Challenges • data • processing time • modeling • dynamics

  3. Data…There Is More Than We Know What to Do With

  4. Data Collection • Data Sources (Assume 1000 flights per day) • PNR (Touched and Flown) ~ 250,000 per day • Flight level inventory ~ 150,000 per day • Schedule ~ 20,000 per day • Agent, Customer, etc… ~ ? (your mileage may vary…)

  5. Data To Collect: Some Examples • Ticketing Information • Currency (Type/Exchange Rate) • Fare Basis Code • Special Service • Passenger Address • OAL Booked By • OAL segment(s) • Tour Segment • Hotel Segment • Car Segment • Group Name • Number of Passengers in PNR • Ticket Type • Denied Boardings Code • Form Of Payment Info • Agent Iata# • Tel # • Other Supp. Info Messages • Protected History (all legs bkd) • Received From (PNR modifying person) • Arrival Times all legs • OAL segment • PNR Record Locator • Passenger Name • Creation Time • Creation Date • Creation DOW • Holiday • Special Events • Airline Code(s) • Origin Airport • Origin City • Origin Country • Origin Continent • Destination Airport • Destination City • Destination Country • Destination Continent • Path Airport • Path City • Departure Date(s) all legs • Departure Time(s) all legs • Point of Sale City • Point of Sale Country • Point of Sale Continent • Booking Office • Group Identifier • Passenger Type (Freq. Flier Type?) • Frequent Flier Number • Fare Classes all legs • Number of Passengers • Number Protected • No Show Identifier • No Show Reason • Go Show Identifier • Go Show Booking Time before Departure • Connection from Airline • Connection to Airline • Original Point of Departure • Final Destination • Cancellation Identifier • Cancellation Date • Cancellation Time • Cancellation Reason • Flight Numbers all legs • Confirmation Codes all legs • Fare (Base, Airport Chg, Tax)

  6. Data Challenges • Rich source of data • It will take many years to find all of the gems • Large volumes of data • Processing time is the binding constraint • Cleaning / Massaging • Lots of cleaning required

  7. Forecast ModelingIt Must Be Fast, Fast, Fast….

  8. Forecast Updating • Unconstrain Actuals • Update Models

  9. Unconstraining • Methods for adjustment • Projection Methods • Iterative Methods • Inputs • Constraint Probability • Bookings / Cancels / Waitlist

  10. Forecast Modeling • Bayesian forecasting paradigm • Correlation adjustments • Seasonality Adjustments • Hierarchical Correlation • Component Relationship

  11. Bayesian Forecasting • Simple updating • Minimal data history required • Uses all history, but minimize database • Dynamic to changing data • exponential smoothing

  12. Bayesian Forecasting • components: • reservations (arrivals model) • cancellations (rate model) • go-shows • no-shows • booking curve • Each component poses new challenges!

  13. Correlation Adjustment • remove model assumptions of independence across time slices • adjust based on correlation model • early surge in bookings/cancels may result in lower or higher bookings later in cycle • significant reduction in errors

  14. Seasonality Adjustments • Model cyclical patterns • day of week patterns • monthly patterns • year over year patterns • significant reduction in errors

  15. Hierarchical Adjustments • remove model assumptions of independence between entities • relate entities through hierarchy • reduce “small numbers” problem • high demand in one itinerary may imply high/low demand in another (spill) • significant reduction in errors

  16. Component Relationship • “Blend”: • blend different models to form “out” passenger forecasts, demand to come • relate forecasts, e.g. cancels and no-shows

  17. Accuracy: “The Forest and the Trees” • Small numbers accurate, but... • aggregations need to be accurate, as well • Feedback mechanism • proper model tuning • bad aggregate forecasts can bias bid prices

  18. Holidays / Special Events • Accounted for in models • Discount from “non-holiday” forecasts • Incorporate user knowledge

  19. DynamicsEverything Is Always Changing…

  20. Dynamics • Schedule changes • Reduce impact of frequent changes in the flight network • Maintain “relevant” history • Create a “schedule-free” network • Accounting for new markets • sponsorship

  21. Hard Work Pays off... Forecasting Results

  22. A Forecast "Tonight's forecast: dark. Continuing dark throughout the night and turning to widely scattered light in the morning." - George Carlin

More Related