Customer impact predictive model and combinatorial analysis

• US 8,813,025 B1
• Filed: 01/12/2009
• Issued: 08/19/2014
• Est. Priority Date: 01/12/2009
• Status: Active Grant

• Current Assignee: Bank of America Corp.
• Original Assignee: Bank of America Corp.
• Inventors: Cowan, John, Nemecek, Rachel, Dunlap, Edward M. Jr., Williams, Thomas R., Hammet, Claudia P., Ulmer, David H.
• Primary Examiner(s): Chen, Xi D
• Application Number:
  US12/352,024
• Time in Patent Office: 2,045 Days
• Field of Search: None
• US Class Current: 717/104
• CPC Class Codes: G06F 11/008 Reliability or availability analysis
• CPC Class Codes: G06F 8/60 Software deployment
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities

Abstract

Systems and methods for objective Deployment Failure risk assessments are provided, which may include fault trees. Systems and methods for the analysis of fault trees are provided as well. The risk assessments system may involve the development of a fault tree, assigning initial values and weights to the events within that fault tree, and the subsequent revision of those values and weights in an iterative fashion, including comparison to historical data. The systems for analysis may involve the assignment of well-ordered values to some events in a fault tree, and then the combination those values through the application of specialized, defined gates. The system may further involve the revision of specific gates by comparison to historical or empirical data.

Claims

1. A computer implemented method for assessing deployment failure risk, the method comprising:

creating a fault tree analytical model for the prediction of deployment failures by electronically linking at least one first data object, at least one second data object and a third data object, the first data object corresponding to at least one root cause, said at least one root cause comprising a high level of design complexity, the second data object corresponding to a minor effect based at least in part on the root cause, and the third data object corresponding to a failed customer interaction based at least in part on the one minor effect;

storing in computer readable memory one or both of a value and a weight corresponding to the minor effect, wherein the correspondence between the minor effect and the value and the weight is obtained through the application of at least one pre-defined result table specifying at least the first data object and at least one necessary outcome, and wherein the outcome comprises a gate modification of the table; and

using a processor to compare an output value of the minor effect to a historical value of the minor effect and utilizing the game modification in the result table to change one or both of the value and the weight based on the comparison.

2. The method of claim 1 wherein:

the first data object corresponds to a lack of a production safeguard; and

the second data object corresponds to the likelihood of a production safeguard failure.

3. The method of claim 1 wherein:

the first data object corresponds to the failure of fail-over technology; and

the second data object corresponds to a likelihood of a production safeguard failure.

4. The method of claim 1 wherein:

the first data object corresponds to at least one of inadequate technical design and inadequate business requirements; and

the second data object corresponds to a likelihood of a technology problem.

5. The method of claim 1 wherein:

the first data object corresponds to at least one of a hardware error missed in testing and a deferred testing incident; and

the second data object corresponds to a likelihood of a technology problem.

6. The method of claim 1 wherein:

the first data object corresponds to at least one of a failure to create a traceability matrix and a failure to review a traceability matrix; and

the second data object corresponds to a likelihood of a technology problem.

7. The method of claim 1 wherein:

the first data object corresponds to a likelihood of a failure to establish a correct issue escalation path; and

the second data object corresponds to a likelihood of a breakdown in product failure analysis.

8. The method of claim 1 wherein the value stored is a Ψ

-value.

9. A computer program product, comprising a non-transitory computer usable medium having a computer readable program code embodied therein, said computer readable program code adapted to be executed to implement a method for generating a deployment failure risk assessment report, said method comprising:

creating a fault tree analytical model for the prediction of deployment failures, the fault tree model including at least one root cause, said at least one root cause comprising a high level of design complexity, at least one minor effect based on the at least one root cause and at least one primary effect corresponding to a failed customer interaction and based on the minor effect;

using the analytical model to generate a first quantitative value corresponding to the minor effect; and

replacing the first quantitative value with a second quantitative value based on a historical value corresponding to the minor effect, wherein the correspondence between the first quantitative value and the weight is obtained through the application of at least one pre-defined result table specifying the second quantitative value wherein the second quantitative value comprises a change in a result table, said change in the result table effected via a gate modification.

10. The computer program product of claim 9 wherein, in the method:

the root cause is at least one of the failure to establish properly configured product failure monitoring and a failure of product failure alerting functionality; and

the minor effect is the likelihood of a breakdown in product failure awareness.

11. The computer program product of claim 9 wherein, in the method:

the root cause is at least one of a product failure occurring during a low transaction volume period and a product failure not impacting core customer functions; and

the minor effect is a likelihood of a breakdown in product failure awareness.

12. The computer program product of claim 9 wherein, in the method:

the root cause is the likelihood of at least one of a non-change related software failure, a non-change related hardware failure, and a peak capacity overload; and

the minor effect is a likelihood of a technology problem.

13. The computer program product of claim 9 wherein, in the method;

the root cause is at least one of a low severity defect and a short testing duration; and

the minor effect is a likelihood of a technology problem.

14. The computer program product of claim 9 wherein, in the method:

the root cause is at least one of a code change being placed into production without a traceable requirement and the code being developed in the wrong code base; and

the minor effect is a likelihood of a technology problem.

15. The computer program product of claim 9 wherein, in the method:

the root cause is at least one of an introduction of emerging technology, an introduction of a new vendor, a high number of impacted applications and a high number of new interfaces; and

the minor effect is a likelihood of a technology problem.

16. The computer program product of claim 9 wherein, in the method:

the root cause is at least one of a high number of impacted associates, a high number of geographic impacts and a change crossing lines of business; and

the minor effect is a likelihood of a technology problem.

17. The computer program product of claim 9 wherein at least one of the first and second quantitative values is a Ψ

-value.