Application of discrete choice theory to forecasting aircraft retirements and fleet composition

• US 8,131,582 B2
• Filed: 01/04/2008
• Issued: 03/06/2012
• Est. Priority Date: 01/04/2008
• Status: Active Grant

• Current Assignee: The Boeing Co.
• Original Assignee: The Boeing Co.
• Inventors: Jiang, Helen
• Primary Examiner(s): DIXON, THOMAS A
• Application Number:
  US11/969,529
• Publication Number:
  US 20090177506A1
• Time in Patent Office: 1,523 Days
• Field of Search: 705/7.31, 705/7.13
• US Class Current: 705/7.31
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

An apparatus and method for forecasting aircraft fleet composition, aircraft retirements and fleet spares requirements, includes obtaining aircraft fleet data, obtaining data on independent variables, determining a cycle indicator, determining the operation type of the aircraft, estimating the model using certain estimation steps using the aircraft fleet data, examining and updating the model based on changes in the independent variables, and forecasting on a year-to-year basis by age and operation cohort.

Claims

1. A method for forecasting aircraft fleet composition, aircraft retirements and fleet spares requirements, comprising the steps of:

obtaining aircraft fleet data;

obtaining data on independent variables;

determining a cycle indicator;

determining the operation type of the aircraft;

estimating a fleet model according to discrete choice theory by using the aircraft fleet data;

examining and updating the fleet model based on changes in the independent variables;

forecasting the aircraft fleet composition on a year-to-year basis by age and operation cohort; and

at least one of the steps of forecasting being performed by a computer processor.

2. The method of claim 1, wherein the independent variables include at least one of fuel price, age of individual aircraft, and economic shocks.

3. The method of claim 1, wherein the cycle indicator is in a range from 2 to 20 years.

4. The method of claim 1, wherein the operation type includes any one of a cargo, passenger, and business.

5. The method of claim 1, wherein the step of updating the fleet model is based on aircraft fleet data.

6. The method of claim 1, wherein the independent variables include the fleet size and age of the aircraft.

7. An apparatus for forecasting aircraft fleet composition, aircraft retirements and fleet spares requirements, comprising the steps of:

a memory unit storing software for execution of instructions, the memory unit storing obtained aircraft fleet data, and data on independent variables; and

a processor connected to memory executing the instructions stored in the memory including;

determining a cycle indicator;

determining the operation type of the aircraft;

estimating a fleet model using discrete choice theory using the aircraft fleet data;

examining and updating the fleet model based on changes in the independent variables; and

forecasting the aircraft fleet composition on a year-to-year basis by age and operation cohort.

8. The apparatus of claim 7, wherein the independent variables include at least one of fuel price, age of individual aircraft, and economic shocks.

9. The apparatus of claim 7, wherein the cycle indicator is in a range from 2 to 20 years.

10. The apparatus of claim 7, wherein the operation type includes any one of a cargo, passenger, and business.

11. The apparatus of claim 7, wherein the step of updating the model is based on aircraft fleet data.

12. The apparatus of claim 7, wherein the step of forecasting is automatically performed in a software program accessing databases with the aircraft fleet data and the independent variables.

13. The apparatus of claim 7, wherein the independent variables include the fleet size and age of the aircraft.