Apparatus and method for automatic refinement of business processes

• US 20070033161A1
• Filed: 08/03/2006
• Published: 02/08/2007
• Est. Priority Date: 08/04/2005
• Status: Abandoned Application

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: Park, Jae-bum, Kim, Jin hee
• Application Number:
  US11/498,083
• Publication Number:
  US 20070033161A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202

Abstract

An apparatus and a method for automatically refining a business process. The method for automatically refining a business process according to an exemplary embodiment of the present invention includes the steps of setting key performance indicators (KPIs) and generating a matrix using KPI-related items, performing regression analysis using the items prepared for the matrix and recording the results of the regression analysis, and reflecting the results of the regression analysis by recording items of the regression analysis in a repository.

Claims

1. An apparatus for automatically refining a business process, the apparatus comprising:

a storage unit that stores data, a process, and a result of regression analyses;

a data input unit that inputs a key performance indicator (KPI) and data associated with the input KPI extracted from the storage unit;

a regression analysis unit that performs regression analyses using the KPI and data associated with the input KPI input by the data input unit; and

a feedback unit that reflects the result of the regression analyses by storing the result of the regression analyses in the storage unit.

2. The apparatus of claim 1, wherein the storage unit comprises a data repository that stores data, a process repository that stores a process and a regression analysis result repository that stores the result of the regression analyses.

3. The apparatus of claim 1, wherein the data input unit comprises:

an element extracting module that extracts from the storage unit data or a process associated with KPI;

a list-creating module that analyzes a source of the data or the process extracted by the element extracting module and creates a list of the data or the process;

a classifying module that classifies elements of the list created by the list-creating module into data and processes; and

a matrix generating module that generates a matrix with combined data or process data from the data or process classified by the classifying module.

4. The apparatus of claim 3, wherein the matrix generating module extracts process data using information on the process resources stored in the process repository when the processes are classified, and extracts a most recent data in order to generate a matrix with combined data from the extracted data when the data is classified.

5. The apparatus of claim 3, wherein the regression analysis unit comprises:

a variable setting module that sets data or process data forming the matrix as independent variables, and KPIs obtained from the set independent variables as dependent variables;

a regression analysis case creating module that creates a list of regression analysis cases by using the independent variables and dependent variables set by the variable setting module; and

a regression analysis performing module that performs regression analyses with the list created by the regression analysis case creating module using the set independent and dependent variables, and records data results of the regression analyses in the regression analysis result repository.

6. The apparatus of claim 5, wherein the variable setting module determines whether the number of set dependent variables exceeds a predetermined number and subtracts from the set dependent variables when the number of set dependent variables exceeds the predetermined number so that the number of dependent variables is less than the predetermined number and obtains the values of the dependent variables when the number of set dependent variable is not more than the predetermined number.

7. The apparatus of claim 5, wherein the feedback unit comprises:

an optimum combination extracting module that extracts the optimum combinations of the dependent variables using the data results from the regression analysis result repository;

a regression analysis result reflecting module that reflects the data results of regression analyses by recording the optimum combinations extracted by the optimum combination extracting module in the data repository, the process repository, and the regression analysis result repository; and

a reporting module that reports the results of reflection by the regression analysis result reflecting module.

8. The apparatus of claim 7, wherein the regression analysis result reflecting module determines whether it is necessary to adjust the dependent variables in the variable combinations extracted by the optimum combination extracting module and, if necessary records adjusted combinations in the data repository, the process repository, and the regression analysis result repository and, if not necessary, records original combinations in the data repository, the process repository, and the regression analysis result repository.

9. The apparatus of claim 7, wherein the report of the result of reflection is given via one of a short message service (SMS), an e-mail and a wired or wireless communication method.

10. The apparatus of claim 1, wherein said KPI is established by a user.

11. The apparatus of claim 5, wherein the regression analysis case creating module creates a list of the regression analysis cases at a user'"'"'s request.

12. The apparatus of claim 7, wherein said reporting module reports the results of reflection to the user.

13. A method for automatically refining a business process, the method comprising:

(a) generating a data repository and a process repository by storing data and a process associated with a key performance indicator (KPI);

(b) inputting a KPI and data associated with the input KPI extracted from the data repository;

(c) performing regression analyses using the input KPI and the data associated with the input KPI; and

(d) reflecting the result of the regression analyses by storing the result of the regression analysis in a regression analysis result repository.

14. The method of claim 13, wherein operation (b) further comprises:

(b-1) extracting data or a process associated with the KPI from the data repository or the process repository;

(b-2) analyzing sources of the extracted data or process, and creating a list of the sources of the extracted data or process;

(b-3) classifying the list elements into data and processes; and

(b-4) generating a matrix with combined data or processes extracted from the classified data as list elements.

15. The method of claim 14, wherein operation (b-4) further comprises, when a process is classified, extracting data composing the process using information on process resources stored in the process registry, and when data is classified, extracting most recent data in order to generate a matrix with combined data from the extracted most recent data.

16. The method of claim 14, wherein operation (c) further comprises:

creating a list of regression analysis cases;

setting data forming the matrix as independent variables and the KPI to be obtained from the set independent variables as dependent variables; and

performing regression analyses with the list of regression analysis cases using the set variables and recording the result of the regression analyses in the regression analysis result repository.

17. The method of claim 16, wherein the setting of the dependent variables is implemented by determining whether the number of set dependent variables is more than a predetermined number, and subtracting from the set dependent variables when the number of set dependent variables exceeds the predetermined number so that the number of dependent variables is less than the predetermined number, and obtaining the values of the set dependent variables when the number of set dependent variables is not more than the predetermined number.

18. The method of claim 16, wherein operation (d) comprises:

extracting the optimum combinations of dependent variables with the data result extracted from the regression analysis result repository;

reflecting the data result of the regression analyses by recording the optimum combinations in the data repository, the process repository and regression analysis result repository; and

reporting the reflection result.

19. The method of claim 18, wherein the reflecting of the regression analysis results is implemented by determining whether it is necessary to adjust the dependent variables in the dependent variable combinations extracted by the optimum combination extracting module, recording the adjusted combinations in the data repository, the process repository, and the regression analysis result repository if adjustment is necessary, and recording the original combinations in the data repository, the process repository, and the regression analysis result repository if adjustment is not necessary.

20. The method of claim 18, wherein the report of the reflection result is given via one of a short message service (SMS), an e-mail and a wired or wireless communication method.

21. The method of claim 13, wherein said KPI is established by a user.

22. The method of claim 20, wherein the report of the reflection result is given to a user.

23. A computer-readable recording medium that records a program for executing, on a computer, a method for automatically refining a business process, the method comprising:

(a) generating a data repository and a process repository by storing data and a process associated with a key performance indicator (KPI);

(b) inputting a KPI and data associated with the input KPI extracted from the data repository;

(c) performing regression analyses using the input KPI and the data associated with the input KPI; and

(d) reflecting the result of the regression analyses by storing the result of the regression analysis in a regression analysis result repository.

24. The computer-readable recording medium of claim 23, wherein operation (b) further comprises:

(b-1) extracting data or a process associated with the KPI from the data repository or the process repository;

(b-2) analyzing sources of the extracted data or process, and creating a list of the sources of the extracted data or process;

(b-3) classifying the list elements into data and processes; and

(b-4) generating a matrix with combined data or processes extracted from the classified data as list elements.

25. The computer-readable recording medium of claim 24, wherein operation (b-4) further comprises, when a process is classified, extracting data composing the process using information on process resources stored in the process registry, and when data is classified, extracting most recent data in order to generate a matrix with combined data from the extracted most recent data.

26. The computer-readable recording medium of claim 24, wherein operation (c) further comprises:

creating a list of regression analysis cases;

setting data forming the matrix as independent variables and the KPI to be obtained from the set independent variables as dependent variables; and

performing regression analyses with the list of regression analysis cases using the set variables and recording the result of the regression analyses in the regression analysis result repository.

27. The computer-readable recording medium of claim 26, wherein the setting of the dependent variables is implemented by determining whether the number of set dependent variables is more than a predetermined number, and subtracts from the set dependent variables when the number of set dependent variables exceeds the predetermined number so that the number of dependent variables is less than the predetermined number, and obtaining the values of the set dependent variables when the number of set dependent variables is not more than the predetermined number.

28. The computer-readable recording medium of claim 26, wherein operation (d) comprises:

extracting the optimum combinations of dependent variables with the data result extracted from the regression analysis result repository;

reflecting the data result of the regression analyses by recording the optimum combinations in the data repository, the process repository and regression analysis result repository; and

reporting the reflection result.

29. The computer-readable recording medium of claim 28, wherein the reflecting of the regression analysis results is implemented by determining whether to adjust the dependent variables in the dependent variable combinations extracted by the optimum combination extracting module, recording the adjusted combinations in the data repository, the process repository, and the regression analysis result repository if adjustment is necessary, and recording the original combinations in the data repository, the process repository, and the regression analysis result repository if adjustment is not necessary.

30. The computer-readable recording medium of claim 28, wherein the report of the reflection result is given via one of a short message service (SMS), an e-mail and a wired or wireless communication method.