OBJECTIVE METRICS MEASURING VALUE OF EMPLOYEES

• US 20150095120A1
• Filed: 09/30/2013
• Published: 04/02/2015
• Est. Priority Date: 09/30/2013
• Status: Abandoned Application

• Current Assignee: NCR Corporation
• Original Assignee: NCR Corporation
• Inventors: Finley, Michael Cain, Gibson, Ray A.
• Application Number:
  US14/041,582
• Publication Number:
  US 20150095120A1
• Time in Patent Office: Days
• US Class Current: 705/7.42
• CPC Class Codes: G06Q 10/06311 Scheduling, planning or task assignment for a person or group
• CPC Class Codes: G06Q 10/063118 Staff planning in a project environment
• CPC Class Codes: G06Q 10/06398 Performance of employee with respect to a job function
• CPC Class Codes: H04W 4/02 Services making use of location information
• CPC Class Codes: H04W 4/38 for collecting sensor information

Abstract

Objective metrics measuring value of employees are provided. Enterprise metrics are gathered for when specific employees are present and an enterprise. Correlations between performance values of the enterprise based at least in part on the enterprise metrics are made.

Claims

1. A processor-implemented method programmed in memory or a non-transitory processor-readable medium and to execute on one or more processors of a device configured to execute the method, comprising:

tracking, via the device, a date and time that an employee is physically present at an enterprise;

monitoring, via the device, one or more enterprise metrics for the date and time at the enterprise; and

correlating, via the device, one or more performance values for the one or more enterprise metrics with the employee physically present.

2. The method of claim 1 further comprising, obtaining, via the device, other performance values associated with other enterprise metrics and for other dates and times for the employee and for other employees.

3. The method of claim 2 further comprising, training, via the device, a Bayesian Network based at least in part on the one or more performance values, the one or more enterprise metrics, other performance values for the other enterprise metrics, the employee, and the other employees.

4. The method of claim 3 further comprising, generating, via the device, from the Bayesian Network one or more outcome weights for each employee with respect to other employees, the enterprise metrics, and the other enterprise metrics.

5. The method of claim 4 further comprising, generating, via the device, an overall contribution score for a particular employee based at least in part on summing the particular employee'"'"'s one or more outcome weights.

6. The method of claim 4 further comprising, predicting, via the device, from the Bayesian Network outcomes for different proposed combinations of the employees based at least in part on a future time and date at the enterprise.

7. The method of claim 6 further comprising, proposing, via the device, an optimal grouping of the employees with one another for the future date and time.

8. The method of claim 1, wherein tracking further includes using a geographical position communicated from an application on a mobile device of the employee to track the date and time the employee is physically present at the enterprise.

9. The method of claim 1, wherein tracking further includes using an enterprise check-in technology to determine when the employee is physically present at the enterprise for the date and time.

10. The method of claim 1, wherein monitoring further includes acquiring a transactional metric for a transaction occurring at the enterprise on the date and time as the enterprise metric.

11. The method of claim 1, wherein correlating further includes assigning an objective contribution value to the employee based on a calculation of the one or more enterprise metrics for the date and time.

12. A processor-implemented method programmed in memory or a non-transitory processor-readable medium and to execute on one or more processors of a device configured to execute the method, comprising:

obtaining, at the device, one or more transaction details for one or more transactions occurring at an enterprise;

receiving, at the device, one or more employee identifiers for one or more employees present during each transaction at the enterprise;

packaging, at the device, the one or more transaction details and the one or more employee identifiers with one or more enterprise performance ratings for each of the transactions to form one or more packaged metrics; and

transmitting, from the device, the one or more packaged metrics to an evaluation service to obtain one or more objective contribution ratings for each employee.

13. The method of claim 12 further comprising, periodically updating the evaluation service with additional packaged metrics for other transactions to get updated objective contribution ratings.

14. The method of claim 12, wherein obtaining further includes acquiring the transaction details from one or more Point-Of-Sale (POS) devices present at the enterprise.

15. The method of claim 12, wherein receiving further includes obtaining some of the employee identifiers from geographic coordinates associated with mobile devices of some employees or from check-in technology present at the enterprise.

16. The method of claim 12, wherein packaging further includes obtaining the one or more enterprise performance values from evaluation of one or more enterprise rules or one or more policies that map one or more values for the one or more transaction details to the one or more enterprise performance ratings.

17. The method of claim 12, wherein transmitting further includes formatting the one or more packaged metrics for the evaluation service, the evaluation service is a Bayesian Network or a Neural Network.

18. A system, comprising:

a server having a numerical analysis module;

wherein the numerical analysis is configured to be trained based at least in part on gathered one or more enterprise metrics, one or more enterprise performance ratings for the one or more enterprise metrics, and one or more employee identifiers for one or more employees physically present during one or more transactions tied to the one or more enterprise metrics, and wherein the numerical analysis module is further configured to generate outcome one or more contribution ratings for each employee.

19. The system of claim 18, wherein the numerical analysis module is a Bayesian Network.

20. The system of claim 18, wherein the server is a cloud server.