Computer-implemented systems and methods for determining future profitability

• US 8,473,331 B2
• Filed: 03/29/2012
• Issued: 06/25/2013
• Est. Priority Date: 05/08/2009
• Status: Active Grant

• Current Assignee: SAS Institute Incorporated
• Original Assignee: SAS Institute Incorporated
• Inventors: Pope, David Carter, Szczerba, Robert Leo, Gaiway, Liewellyn Arthur
• Primary Examiner(s): Hayes, John
• Assistant Examiner(s): Jung, Allen J
• Application Number:
  US13/433,319
• Publication Number:
  US 20120185289A1
• Time in Patent Office: 453 Days
• Field of Search: 705/7.35, 705/7.37
• US Class Current: 705/7.37
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/063 Operations research, analysis or management
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0206 Price or cost determination based on market factors

Abstract

Computer-implemented systems and methods are provided for determining future profitability for an entity. A present profitability model is generated for the entity based upon present values for a plurality of components of profitability. The present profitability model is used to create a future profitability forecast for the entity.

Claims

1. A computer-implemented method, comprising:

generating a present profitability model, wherein the present profitability model is associated with one or more components;

determining a set of multiple components;

determining a set of one or more profitability drivers from amongst the set of multiple components, wherein the profitability drivers are correlated with a profitability of an entity;

isolating components that are not in the set of profitability drivers, wherein isolated components are held constant;

forecasting one or more future values for each of the one or more profitability drivers; and

generating, by a computing device, a future probability forecast using the one or more future values, the isolated components, and the present profitability model.

2. The method of claim 1, wherein the future probability forecast indicates forecasted profit for the entity.

3. The method of claim 1, wherein generating the future probability forecast includes generating one or more future forecasts and a confidence interval for each of the one or more future forecasts.

4. The method of claim 1, wherein generating the future probability forecast includes using an activity based costing model.

5. The method of claim 1, wherein generating the present profitability model includes determining mathematical relationships in historical data between the components of the set and profitability of the entity.

6. The method of claim 1, wherein the components of the set are independent variables and the present profitability model includes relationships between independent variables and profitability of the entity within a set of historical data.

7. The method of claim 6, wherein the isolated components are independent variables which, within the historical data, are less correlated with profitability of the entity than profitability driver variables.

8. The method of claim 1, wherein the profitability drivers are independent variables, and wherein each profitability driver is determined based on a profit correlation with the entity.

9. The method of claim 1, further comprising:

using the present profitability model to determine an effect of a component of the set on profitability of the entity.

10. The method of claim 1, wherein isolating further comprises holding the isolated components constant in the present profitability model.

11. A system, comprising:

one or more data processors; and

one or more non transitory computer-readable storage media containing instructions configured to cause the one or more processors to perform operations including;

generating a present profitability model, wherein the present profitability model is associated with one or more components;

determining a set of multiple components;

determining a set of one or more profitability drivers from amongst the set of multiple components, wherein the profitability drivers are correlated with a profitability of an entity;

isolating components that are not in the set of profitability drivers, wherein isolated components are held constant;

forecasting one or more future values for each of the one or more profitability drivers; and

generating a future probability forecast using the one or more future values, the isolated components, and the present profitability model.

12. The system of claim 11, wherein the future probability forecast indicates forecasted profit for the entity.

13. The system of claim 11, wherein generating the future probability forecast includes generating one or more future forecasts and a confidence interval for each of the one or more future forecasts.

14. The system of claim 11, wherein generating the future probability forecast includes using an activity based costing model.

15. The system of claim 11, wherein generating the present profitability model includes determining mathematical relationships in historical data between the components of the set and profitability of the entity.

16. The system of claim 11, wherein the components of the set are independent variables and the present profitability model includes relationships between independent variables and profitability of the entity within a set of historical data.

17. The system of claim 16, wherein the isolated components are independent variables which, within the historical data, are less correlated with profitability of the entity than profitability driver variables.

18. The system of claim 11, wherein the profitability drivers are independent variables, and wherein each profitability driver is determined based on a profit correlation with the entity.

19. The system of claim 11, further comprising instructions configured to cause the one or more processors to perform operations including:

using the present profitability model to determine an effect of a component of the set on profitability of the entity.

20. The method of claim 11, wherein isolating further comprises holding the isolated components constant in the present profitability model.

21. A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause a data processing apparatus to:

generate a present profitability model, wherein the present profitability model is associated with one or more components;

determine a set of multiple components;

determine a set of one or more profitability drivers from amongst the set of multiple components, wherein the profitability drivers are correlated with a profitability of an entity;

isolate components that are not in the set of profitability drivers, wherein isolated components are held constant;

forecast one or more future values for each of the one or more profitability drivers; and

generate a future probability forecast using the one or more future values, the isolated components, and the present profitability model.

22. The computer-program product of claim 21, wherein the future probability forecast indicates forecasted profit for the entity.

23. The computer-program product of claim 21, wherein generating the future probability forecast includes generating one or more future forecasts and a confidence interval for each of the one or more future forecasts.

24. The computer-program product of claim 21, wherein generating the future probability forecast includes using an activity based costing model.

25. The computer-program product of claim 21, wherein generating the present profitability model includes determining mathematical relationships in historical data between the components of the set and profitability of the entity.

26. The computer-program product of claim 21, wherein the components of the set are independent variables and the present profitability model includes relationships between independent variables and profitability of the entity within a set of historical data.

27. The computer-program product of claim 26, wherein the isolated components are independent variables which, within the historical data, are less correlated with profitability of the entity than profitability driver variables.

28. The computer-program product of claim 21, wherein the profitability drivers are independent variables, and wherein each profitability driver is determined based on a profit correlation with the entity.

29. The computer-program product of claim 21, further comprising instructions configured to cause a data processing apparatus to:

use the present profitability model to determine an effect of a component of the set on profitability of the entity.

30. The computer-program product of claim 21, wherein isolating further comprises holding the isolated components constant in the present profitability model.