SYSTEM AND METHOD TO PROVIDE PREDICTIVE ANALYSIS TOWARDS PERFORMANCE OF TARGET OBJECTS ASSOCIATED WITH ORGANIZATION

• US 20140244362A1
• Filed: 09/05/2013
• Published: 08/28/2014
• Est. Priority Date: 02/27/2013
• Status: Abandoned Application

• Current Assignee: TATA Consultancy Services Limited (Tata Sons Pvt Ltd.)
• Original Assignee: TATA Consultancy Services Limited (Tata Sons Pvt Ltd.)
• Inventors: Chaudhury, Dhruba Jyoti
• Application Number:
  US14/019,356
• Publication Number:
  US 20140244362A1
• Time in Patent Office: Days
• US Class Current: 705/7.37
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change

Abstract

This disclosure relates to predictive performance analysis of target objects associated with an organization. In one embodiment, a performance predictive analysis method is disclosed, comprising: receiving one or more parameters and an associated intensity level to predict performance of a target object associated with an organization; determining a proportionality relation between the performance of the target object and the one or more parameters, by applying a logical regression technique over the one or more parameters; selecting a threshold value from a pre-defined truth table to convert the one or more parameters into one or more group-level model factors with the associated intensity level; determining an impact of the one or more parameters on the performance in terms of a band wise distribution; and identifying a probabilistic effect, based on the determined impact, of the proportionality relation between the performance of the target object and the one or more parameters.

Claims

1. A performance predictive analysis system, comprising:

a processor; and

a memory disposed in communication with the processor, and storing processor-executable instructions comprising instructions to;

receive one or more parameters and an associated intensity level to predict performance of a target object associated with an organization;

determine a proportionality relation between the performance of the target object and the one or more parameters, by applying a logical regression technique over the one or more parameters;

select a threshold value from a pre-defined truth table to convert the one or more parameters into one or more group-level model factors with the associated intensity level;

determine an impact of the one or more parameters on the performance in terms of a band wise distribution; and

identify a probabilistic effect, based on the determined impact, of the proportionality relation between the performance of the target object and the one or more parameters.

2. The system of claim 1, wherein the performance of the target object is measured as a variation in customer satisfaction index.

3. The system of claim 1, wherein the one or more parameters comprise projects related data, prior customer satisfaction index information, and corresponding survey response information, project events and project scenario.

4. The system of claim 1, wherein the one or more parameters comprise a previously identified probabilistic effect.

5. The system of claim 1, wherein the band wise distribution comprises numerical ranges of 0-2%, 2-5%, 5-10%, 10-15%, 15-20% and %20+.

6. The system of claim 1, wherein the proportionality relation is at least one of:

a delta positive increase, or a delta negative slippage in a customer satisfaction index.

7. The system of claim 6, the instructions further comprising instructions to:

provide one or more recommendations with respect to the delta negative slippage in the customer satisfaction index.

8. The system of claim 1, wherein the input parameters received are each categorized as either parameters inducing a negative effect, or parameters causing a positive effect.

9. The system of claim 1, the instructions further comprising instructions to:

generate a standard form of the probabilistic effect by applying a technique of normalization over the band wise distribution;

the standard form being generated by using a previous customer satisfaction index data.

10. A performance predictive analysis method, comprising:

receiving one or more parameters and an associated intensity level to predict performance of a target object associated with an organization;

determining a proportionality relation between the performance of the target object and the one or more parameters, by applying a logical regression technique over the one or more parameters;

selecting a threshold value from a pre-defined truth table to convert the one or more parameters into one or more group-level model factors with the associated intensity level;

determining an impact of the one or more parameters on the performance in terms of a band wise distribution; and

identifying a probabilistic effect, based on the determined impact, of the proportionality relation between the performance of the target object and the one or more parameters.

11. The method of claim 10, wherein the performance of the target object is measured as a variation in customer satisfaction index.

12. The method of claim 10, wherein the one or more parameters comprise projects related data, prior customer satisfaction index information, and corresponding survey response information, project events and project scenario.

13. The method of claim 10, wherein the one or more parameters comprise a previously identified probabilistic effect.

14. The method of claim 10, wherein the band wise distribution comprises numerical ranges of 0-2%, 2-5%, 5-10%, 10-15%, 15-20% and %20+.

15. The method of claim 10, wherein the proportionality relation is at least one of:

a delta positive increase, or a delta negative slippage in a customer satisfaction index.

16. The method of claim 10, wherein:

the input parameters received are each categorized as either parameters inducing a negative effect, or parameters causing a positive effect; and

the parameters providing a negative effect comprise project related issues, events, or weaknesses, and the parameters providing a positive effect comprise project related best practices, actions, or improvements.

17. The method of claim 10, further comprising:

generating a standard form of the probabilistic effect by applying a technique of normalization over the band wise distribution;

the standard form being generated by using a customer satisfaction index data.

18. The method of claim 15, further comprising:

providing one or more recommendations with respect to the delta negative slippage in the customer satisfaction index.

19. A non-transitory computer-readable medium storing computer-executable performance predictive analysis instructions comprising instructions to:

receive one or more parameters and an associated intensity level to predict performance of a target object associated with an organization;

determine a proportionality relation between the performance of the target object and the one or more parameters, by applying a logical regression technique over the one or more parameters;

select a threshold value from a pre-defined truth table to convert the one or more parameters into one or more group-level model factors with the associated intensity level;

determine an impact of the one or more parameters on the performance in terms of a band wise distribution; and

identify a probabilistic effect, based on the determined impact, of the proportionality relation between the performance of the target object and the one or more parameters.