SYSTEM AND METHOD FOR PROVIDING WORKFORCE AND WORKLOAD MODELING

• US 20090171718A1
• Filed: 01/02/2008
• Published: 07/02/2009
• Est. Priority Date: 01/02/2008
• Status: Abandoned Application

• Current Assignee: Verizon Patent and Licensing Incorporated (Verizon Communications Inc.)
• Original Assignee: Verizon Services Corporation (Verizon Communications Inc.), Verizon Services Organization Incorporated (Verizon Communications Inc.)
• Inventors: Barlow, Lisa R., Ross, Katherine T., Wehmeyer, Paula, Drew, James H.
• Application Number:
  US11/968,315
• Publication Number:
  US 20090171718A1
• Time in Patent Office: Days
• US Class Current: 705/7
• CPC Class Codes: G06Q 10/0633 Workflow analysis
• CPC Class Codes: H04L 41/142 using statistical or mathematical methods
• CPC Class Codes: H04L 41/147 for predicting network behaviour
• CPC Class Codes: H04L 41/149 for prediction of maintenance
• CPC Class Codes: H04L 41/50 Network service management, e.g. ensuring proper service fulfilment according to agreements

Abstract

Workforce and workload prediction is provided. A portal is configured to receive input from a user, wherein the input relates to forecasting workload and workforce for providing a communication service. The workload includes ticket loads relating to repair and provisioning, and the workforce includes human resources and equipment. A load prediction is output in response to the input, wherein the load prediction is generated by a forecast model that utilizes regression analysis of historical data and real-time information that includes workforce availability data and one or more factors impacting the workload.

Claims

1. A method comprising:

presenting a portal configured to receive input from a user, wherein the input relates to forecasting workload and workforce for providing a communication service, the workload including ticket loads relating to repair and provisioning, the workforce including human resources and equipment; and

outputting a load prediction in response to the input, wherein the load prediction is generated by a forecast model that utilizes regression analysis of historical data and real-time information that includes workforce availability data and one or more factors impacting the workload.

2. A method according to claim 1, wherein the factors include weather information, the method further comprising:

detecting a change in one of the factors; and

generating a notification to the user to update the load prediction based on the change.

3. A method according to claim 1, further comprising:

notifying the user that the load prediction results in a particular quantity of incomplete jobs.

4. A method according to claim 1, further comprising:

receiving another input from the user, wherein the input specifies one or more of the factors including workload interval, workforce capacity, workforce productivity, or a combination thereof; and

modifying the load prediction in response to the other input.

5. A method according to claim 1, wherein the portal provides a collection point for the real-time information from a plurality of third party data sources.

6. A method according to claim 1, wherein the load prediction is self-correcting based on a moving average of prior load predictions.

7. A method according to claim 1, further comprising:

periodically retrieving new real-time information, wherein the load prediction is updated in response to new real-time information

8. An apparatus comprising:

a communication interface configured to present a portal configured to receive input from a user, wherein the input relates to forecasting workload and workforce for providing a communication service, the workload including ticket loads relating to repair and provisioning, the workforce including hum an resources and equipment; and

a processor configured to output a load prediction in response to the input, wherein the load prediction is generated by a forecast model that utilizes regression analysis of historical data and real-time information that includes workforce availability data and one or more factors impacting the workload.

9. An apparatus according to claim 8, wherein the factors include weather information, the processor is further configured to detect a change in one of the factors, and to generate a notification to the user to update the load prediction based on the change.

10. An apparatus according to claim 8, wherein the processor is further configured to notify the user that the load prediction results in a particular quantity of incomplete jobs.

11. An apparatus according to claim 8, wherein the portal is further configured to receive another input from the user, the input specifying one or more of the factors including workload interval, workforce capacity, workforce productivity, or a combination thereof, the processor being further configured to modify the load prediction in response to the other input.

12. An apparatus according to claim 8, wherein the portal provides a collection point for the real-time information from a plurality of third party data sources.

13. An apparatus according to claim 8, wherein the load prediction is self-correcting based on a moving average of prior load predictions.

14. An apparatus according to claim 8, wherein new real-time information is periodically retrieved, and the load prediction is updated in response to new real-time information.

15. A computer-implemented method comprising:

defining a plurality of prediction periods;

determining a quantity of tickets corresponding to each of the prediction periods;

computing an average for the tickets for a predetermined duration;

determining average amount of precipitation, or average amount of relative humidity, or a combination thereof, for a corresponding plurality of locations;

retrieving coefficients corresponding to the locations, wherein the coefficients indicate degree of influence of weather condition or prior load for the respective locations; and

outputting a predicted ticket load based on the ticket average, the average amount of precipitation, the average amount of relative humidity, and the coefficients.

16. A method according to claim 15, wherein data about the precipitation and the relative humidity are retrieved in real-time.

17. A method according to claim 15, further comprising:

receiving workforce information; and

determining quantity of incomplete jobs based on the workforce information and the predicted ticket load.

18. A method according to claim 15, wherein the predicted ticket load is based on a moving average of prior predictions of the ticket loads.

19. A method according to claim 15, further comprising:

receiving a user command from a browser; and

updating the predicted ticket load in response to the user command.

20. A method according to claim 15, wherein the predicted ticket load relates to repair of a communications equipment or provisioning of a communication service.