GRAPHICAL RISK-BASED PERFORMANCE MEASUREMENT AND BENCHMARKING SYSTEM AND METHOD

• US 20090024429A1
• Filed: 12/13/2007
• Published: 01/22/2009
• Est. Priority Date: 07/19/2007
• Status: Active Grant

• Current Assignee: HSB Solomon Associates LLC (Münchener Rückversicherungs-Gesellschaft AG)
• Original Assignee: HSB Solomon Associates LLC (Münchener Rückversicherungs-Gesellschaft AG)
• Inventors: Havener, John P., Jones, Richard B.
• Granted Patent: US 8,224,690 B2
• Time in Patent Office: Days
• US Class Current: 705/7
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 10/06313 Resource planning in a project environment
• CPC Class Codes: G06Q 10/0635 Risk analysis of enterprise or organisation activities
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: G06Q 10/06393 Score-carding, benchmarking or key performance indicator [KPI] analysis
• CPC Class Codes: G06Q 30/0203 Market surveys; Market polls
• CPC Class Codes: Y02P 90/80 Management or planning

Abstract

A system and method provides analysis of pre- and post-project effects on frequency and severity of operational loss. The method includes the computation of the frequency of incidents and the severity of incidents for equipment based on recorded incident data. Risk, a function of frequency and severity, is calculated before and after a project to determine if the project has changed the risk of an incident in a piece of equipment or facility and if the project has changed the nature of risk (i.e. increasing severity while decreasing frequency). The method may be used to compute forecast estimates of future operational losses, assess goals for improving current performance relative to demonstrated industry performance, and determine statistical confidence intervals of forecast such that risk, and changes in risk, may be visually quantified and communicated.

Claims

1. A computer-implemented method for computing the risk of pre- and post-project incidents comprising the steps of:

collecting incident data;

computing total loss for pre- and post-project incidents;

computing a frequency of the pre- and post-project incidents;

computing a severity of the pre- and post-project incidents;

computing a risk of the pre- and post-project incidents using the frequency and severity of the pre- and post-project incidents; and

generating of a frequency-severity framework with iso-risk curves.

2. The computer-implemented method of claim 1, further comprising the step of:

rendering the frequency, severity, and risk of the incidents.

3. The computer-implemented method of claim 2, further comprising the steps of:

computing a forecast estimate for the frequency based on the validated incident data;

computing a forecast estimate for the severity;

computing a forecast estimate for the risk; and

rendering at least one of the computed forecast estimates.

4. The computer-implemented method of claim 3, further comprising the steps of:

computing a confidence interval for at least one of the computed forecast estimates; and

rendering a confidence interval for at least one of the computed forecast estimates.

5. The computer-implemented method of claim 4, further comprising the steps of:

validating the incident data;

censoring the incident data for a time period.

6. The computer-implemented method of claim 5, wherein rendering the frequency-severity, and risk of the incident comprises the step of:

plotting an indicator of the shift between frequency, severity, and risk of the incidents for a time periods.

7. The computer-implemented method of claim 1, further comprising the steps of:

validating the incident data; and

mapping the incident data into at least one study category.

8. The computer-implemented method of claim 1, further comprising the steps of:

validating the incident data; and

censoring the data to include applicable incidents.

9. The computer-implemented method of claim 1, further comprising the steps of:

validating the incident data; and

dividing the incident data into baseline and post-project groups.

10. The computer-implemented method of claim 1, wherein generating a frequency-severity framework with iso-risk curves comprises the step of:

generating industry performance iso-risk curves.

11. The computer-implemented method of claim 10, wherein the industry performance iso-risk curves comprise:

iso-risk curves based on comparative performance data from industry surveys.

12. The computer-implemented method of claim 11, further comprising the step of:

comparing the frequency, severity, and risk with the industry performance iso-risk curves.

13. The computer-implemented method of claim 12, wherein generating industry performance iso-risk curves includes the step of:

setting project goals based on the industry performance iso-risk curves.

14. A system comprising:

a server, comprising;

a processor,

a storage subsystem;

a database stored by the storage subsystem comprising;

incident data; and

a computer program stored by the storage subsystem, when executed causing the processor to;

collect incident data;

compute the total loss for pre- and post-project incidents;

compute the frequency of the pre- and post-project incidents;

compute the severity of the pre- and post-project incidents;

compute the risk of the pre- and post-project incidents using the frequency and severity of the pre- and post-project incidents; and

construct a visual representation of a frequency-severity framework with iso-risk curves; and

render the frequency, severity, and risk of the validated incident data on the visual representation.

15. The system of claim 14, wherein the computer program, when executed, further causes the processor to:

render the frequency, severity, and risk of the incidents;

16. The system of claim 14, wherein the computer program, when executed, further causes the processor to:

compute a forecast estimate for the frequency of the incidents;

compute a forecast estimate for the severity of the incidents;

compute a forecast estimate for the risk of the incidents; and

render at least one of the computed forecast estimates.

17. The system of claim 14, wherein the computer program, when executed, further causes the processor to:

compute a confidence interval for at least one of the computed forecast estimates; and

rendering a confidence interval for at least one of the computed forecast estimates.

18. The system of claim 14, wherein the computer program, when executed, further causes the processor to:

censor the incident data to a relevant time periods.

19. The system of claim 18, wherein the computer program, when executed, further causes the processor to:

render at least one indicator of the shift between frequency-severity of the validated incidents for the relevant time periods.

20. The system of claim 14, wherein the computer program, when executed, further causes the processor to:

validate the incident data

21. The system of claim 14, wherein the computer program, when executed, further causes the processor to:

map the incident data into at least one study category.

22. The system of claim 14, wherein the computer program, when executed, further censors the data to include applicable incidents.

23. The system of claim 14, wherein the computer program, when executed, further causes the processor to:

divides the incident data into baseline and post-project groups.

24. The system of claim 14, wherein the computer program, when executed, further causes the processor to:

generate industry performance iso-risk curves.

25. The system of claim 24, wherein the industry performance iso-risk curves comprise:

iso-risk curves based on comparative performance data from industry surveys.

26. The system of claim 24, wherein the computer program, when executed, further causes the processor to:

compare the frequency, severity, and risk values of the incident data to the industry performance iso-risk curves.

27. The system of claim 24, wherein the computer program, when executed, further causes the processor to:

allows a user to set project goals based on the industry performance iso-risk curves.