HEADCOUNT FORECASTING SYSTEM

• US 20110119109A1
• Filed: 11/13/2009
• Published: 05/19/2011
• Est. Priority Date: 11/13/2009
• Status: Abandoned Application

• Current Assignee: Bank of America Corp.
• Original Assignee: Bank of America Corp.
• Inventors: Vasudevannair, Unnikrishnan P., Teal, Benjamin T., Prentice, Timothy J., Yang, Dan
• Application Number:
  US12/618,017
• Publication Number:
  US 20110119109A1
• Time in Patent Office: Days
• US Class Current: 705/7.31
• CPC Class Codes: G06F 16/288 Entity relationship models
• 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 10/06312 Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

Embodiments of the present invention provide systems, apparatuses, methods, and computer program products for forecasting the future headcount of an organization by generating, validating and displaying models of the headcount of an organization or division thereof over time. In some embodiments, at least three different models are generated using stored historical headcount information, including a linear regression model, a multivariate model using macroeconomic variables, and an autoregressive moving average model. In some embodiments, for each of the foregoing types, multiple models are generated and the best model of each type is selected for use in forecasting headcount according to predetermined evaluation criteria.

Claims

1. A system for forecasting the future headcount of a group of individuals comprising:

a user interface;

a memory device comprising computer-readable program code, historical headcount data for the group and macroeconomic data; and

a processor operatively coupled to the user interface and the memory device and configured to execute the computer-readable program code to;

receive, via the user interface, a request for a forecast of the future headcount of the group of individuals;

locate in the memory device, in response to the request, the historical headcount data for the group and the macroeconomic data;

utilize the historical headcount data to generate at least one linear regression model and at least one autoregressive moving average model;

utilize the historical headcount data and the macroeconomic data to generate at least one multivariate macroeconomic model; and

display one of the at least one linear regression models, one of the at least one autoregressive moving average models, and one of the at least one multivariate macroeconomic models via the user interface.

2. The system of claim 1, wherein the processor is configured to display one of the at least one linear regression models, one of the at least one autoregressive moving average models, and one of the at least one multivariate macroeconomic models in combination on a graph having time on the x-axis and headcount on the y-axis.

3. The system of claim 1, further comprising a network interface, wherein the processor is configured to further execute the computer-readable program code to:

obtain at least a portion of the historical headcount data via the network interface from a database comprising information about the individuals.

4. The system of claim 1, further comprising a network interface, wherein the processor is configured to further execute the computer-readable program code to:

obtain at least a portion of the macroeconomic data via the network interface from an online service provider.

5. The system of claim 1, wherein the historical headcount data comprises a time series of the headcount of the group over a period of time prior to utilization of the system.

6. The system of claim 1, wherein the macroeconomic data comprises historical and forecasted values for a plurality of macroeconomic variables.

7. The system of claim 6, wherein the processor is configured to further execute the computer-readable program code to:

generate at least one time-lagged variable for each macroeconomic variable in the plurality of macroeconomic variables.

8. The system of claim 7, wherein the processor is configured to further execute the computer-readable program code to:

perform a stepwise analysis using the historical headcount data and the macroeconomic data to determine which of the plurality of macroeconomic variables and the time-lagged variables are correlated with the historical headcount data.

9. The system of claim 8, wherein the at least one macroeconomic model is generated using the macroeconomic variables and time-lagged variables that are correlated with the historical headcount data as the sole independent variables.

10. The system of claim 1, wherein the processor is further configured to execute the computer-readable program code to:

smooth the historical headcount data.

11. The system of claim 1, wherein the processor is further configured to execute the computer-readable program code to:

receive, via the user interface, a selection of minimum R-squared value and confidence level.

12. The system of claim 1, wherein the processor is further configured to execute the computer-readable program code to:

receive, via the user interface, a selection of bubble size.

13. The system of claim 1, wherein the system is configured to only display models that meet a minimum R-squared value and have normally-distributed residuals.

14. The system of claim 1, wherein the system is configured to forecast the future headcount of more than one group of individuals.

15. The system of claim 14, wherein the historical headcount data comprises headcount time series related to multiple groups, and wherein each headcount time series is stored in connection with an identifier associated with the group of individuals to which the headcount time series relates.

16. The system of claim 15, wherein the headcount time series is located in the memory device in response to the request by utilizing the identifier.

17. The system of claim 1, wherein the processor is further configured to execute the computer-readable program code to:

disqualify for display any model rendered by the system that does not have an R-squared value that meets or exceeds a predefined minimum; and

disqualify for display any model rendered by the system that does not have normally-distributed residuals.

18. The system of claim 17, wherein the processor is further configured to execute the computer-readable program code to:

select the one linear regression model for display from any linear regression models not previously disqualified based on the number of data points in the time series used to render it;

select the one multivariate macroeconomic model for display from any multivariate macroeconomic models not previously disqualified based on the number of data points in the time series used to render it; and

select the one autoregressive moving average model for display from any autoregressive moving average models not previously disqualified based on an Akaike information criterion analysis.

19. A method for forecasting the future headcount of a group of individuals comprising:

storing historical headcount data for the group of individuals;

identifying macroeconomic variables that are correlated to the historical headcount data;

storing historical and forecasted macroeconomic data for the identified macroeconomic variables;

generating at least one linear regression model and at least one autoregressive moving average model utilizing the stored historical headcount data;

generating at least one multivariate macroeconomic model utilizing the stored historical headcount data and the stored macroeconomic data; and

presenting one of the at least one linear regression models, one of the at least one autoregressive moving average models, and one of the at least one multivariate macroeconomic models in combination.

20. The method of claim 19, wherein at least a portion of the historical headcount data was obtained via a network from a database comprising human resources information relating to the individuals.

21. The method of claim 19, wherein at least a portion of the macroeconomic data was obtained via a network from an online service provider.

22. The method of claim 19, wherein the historical headcount data comprises one or more headcount time series.

23. The method of claim 19, wherein the macroeconomic variables are identified utilizing a stepwise analysis process.

24. The method of claim 19, wherein the macroeconomic variables comprise time-lagged variables.

25. The method of claim 19, further comprising:

receiving a request from a user for a headcount forecast.

26. The method of claim 19, further comprising:

receiving a selection of minimum R-squared value and confidence level.

27. The method of claim 19, further comprising:

smoothing the historical headcount data to remove any outliers.

28. The method of claim 19, wherein a plurality of linear regression models are generated, a plurality of multivariate macroeconomic models are generated, and a plurality of autoregressive moving average models are generated.

29. The method of claim 28, wherein:

each linear regression model in the plurality of linear regression models was generated using a different portion of the historical headcount data;

each multivariate macroeconomic model in the plurality of multivariate macroeconomic models was generated using a different portion of the historical headcount data; and

each autoregressive moving average model in the plurality of autoregressive moving average models has either a different autoregressive order or a different moving average order.

30. The method of claim 29, further comprising:

disqualifying for display any model generated that does not have an R-squared value that meets or exceeds a predefined minimum; and

disqualifying for display any model generated that does not have normally-distributed residuals.

31. The method of claim 30, wherein:

the one linear regression model displayed is selected from any linear regression models not previously disqualified based on the length of the time series used to render it;

the one multivariate macroeconomic model displayed is selected from any multivariate macroeconomic models not previously disqualified based on the length of the time series used to render it; and

the one autoregressive moving average model displayed is selected from any autoregressive moving average models not previously disqualified based on an Akaike information criterion analysis.

32. A computer program product for forecasting the future headcount of a group of individuals comprising a computer-readable medium having computer-readable program code stored therein, wherein the computer-readable program code comprises:

a first code portion configured to obtain via a first network historical headcount data for the group of individuals;

a second code portion configured to identify macroeconomic variables that are correlated to the historical headcount data;

a third code portion configured to obtain historical and forecasted macroeconomic data corresponding to the identified macroeconomic variables;

a fourth code portion configured to generate at least one linear regression model and at least one autoregressive moving average model utilizing the stored historical headcount data; and

a fifth code portion configured to generate at least one multivariate macroeconomic model utilizing the historical headcount data and the macroeconomic data.

33. The computer program product of claim 32, further comprising:

a sixth code portion configured to display via a user interface one of the at least one linear regression models, one of the at least one autoregressive moving average models, and one of the at least one multivariate macroeconomic models in combination.

34. The computer program product of claim 32, further comprising:

a seventh code portion configured to receive a time value via a user interface;

an eighth code portion configured to input the time value into the at least one linear regression model, the at least one autoregressive moving average model, and the at least one multivariate macroeconomic model to calculate three headcount values corresponding to the time value; and

a ninth code portion configured to display the three headcount values via the user interface.