Analytical-decision support system for improving management of quality and cost of a product

• US 6,922,684 B1
• Filed: 08/31/2000
• Issued: 07/26/2005
• Est. Priority Date: 08/31/2000
• Status: Active Grant

• Current Assignee: Teradata US, Inc. (Teradata Corporation)
• Original Assignee: NCR Corporation
• Inventors: Petz, Craig I., Hill, Gerald L., Aldridge, Bruce E.
• Primary Examiner(s): Hirl, Joseph P.
• Application Number:
  US09/652,974
• Time in Patent Office: 1,790 Days
• Field of Search: 706/60, 706/45, 706/46
• US Class Current: 706/60
• CPC Class Codes: G06N 5/04 Inference or reasoning models

Abstract

The present invention concerns an analytical-decision support system for improving management of quality and cost of a product throughout the entire product lifecycle of the product. The system comprises an atomic level data store for collecting data on component information, configuration information, and field information. A product-lifecycle-management analytics subsystem is included comprising: a query engine for compiling subsets of data from the atomic level data store; a first analysis tool for performing cost or quality analysis on the subsets to determine first analysis results; and a second analysis tool for analyzing detailed data in the atomic level data store to produce second analysis results. A management and operation subsystem is included for presenting the first analysis results after the first analysis tool performs the cost or quality analysis, and for presenting the second analysis results after the second analysis tool analyzes the detailed data. The management and operation subsystem further comprises: a user interface for receiving configuration data from a user for configuring the second analysis tool; and a warning notification subsystem for delivering warning notifications based on the first analysis results.

Claims

1. An analytical-decision support system for improving management of quality and cost of a product throughout the entire product lifecycle of the product, said system comprising:

(a) an atomic level data store for collecting data on component information, configuration information, and field information;

(b) a product-lifecycle-management analytics subsystem comprising;

(i) a query engine for compiling subsets of data from the atomic level data store;

(ii) a first analysis tool for performing cost or quality analysis on the subsets to determine first analysis results;

(iii) a second analysis tool for analyzing detailed data in the atomic level data store to produce second analysis results;

(c) a management and operation subsystem for presenting the first analysis results after the first analysis tool performs the cost or quality analysis, and for presenting the second analysis results after the second analysis tool analyzes the detailed data, the management and operation subsystem comprising;

(i) a user interface for receiving configuration data from a user for configuring the second analysis tool; and

(ii) a warning notification subsystem for delivering warning notifications based on the first analysis results.

2. The analytical-decision support system of claim 1, wherein the first analysis results are one or more operational metrics or one or more cost or quality factors.

3. The analytical-decision support system of claim 1, wherein the second analytical tool is a data mining tool.

4. The analytical-decision support system of claim 2, wherein the warning notification subsystem is adapted to generate the warning notifications based on the one or more cost or quality factors meeting a threshold.

5. The analytical-decision support system of claim 2, wherein the warning notification subsystem is adapted to generate the warning notifications based on the one or more operational metrics meeting a threshold.

6. The analytical-decision support system of claim 2, wherein said operational metrics or said cost or quality factors are incorporated in a management dashboard for providing product lifecycle management information.

7. The analytical-decision support system of claim 1, said atomic level data being supplied by parties involved throughout the lifecycle of said product, said parties comprising one or more of suppliers, original equipment manufacturers, assembly manufacturers, field service providers, call centers, sales entities, and consumers.

8. The analytical-decision support system of claim 2, said operational metrics relating to one or more stage(s) of the product lifecycle, said stages comprising one or more of:

design, manufacture, storage, assembly, configuration, shipping, delivery, handling, sales, support, and customer use.

9. The analytical-decision support system of claim 2, said one or more cost or quality factors relating to one or more stage(s) of the product lifecycle, said stages comprising one or more of:

design, manufacture, storage, assembly, configuration, shipping, delivery, handling, sales, support, and customer use.

10. The analytical-decision support system of claim 1, said component information comprising one or more of supplier information, original equipment manufacturer information, component list information, manufacturing environment information, lot data, test results, picking data, packaging data, and shipping data.

11. The analytical-decision support system of claim 1, said configuration information comprising one or more of hardware-hardware configuration information, hardware-software configuration information, and software-software configuration information.

12. The analytical-decision support system of claim 1, said field information comprising one or more of field-test information, warranty information, repair information, technical support information, and technical service information.

13. The analytical-decision support system of claim 1, wherein the first analytical tool is adapted to generate one or more of:

product management analysis, process management analysis, service supplier analysis, management analysis, component supplier analysis, warranty analysis, maintenance analysis, and forecasting analysis from the operational data store or atomic level data store.

14. The analytical-decision support system of claim 2, said operational metrics comprising a data model of the compiled data, said data model being derived by the product-lifecycle-management analytics subsystem based on at least a portion of the data from the atomic level data store.

15. The analytical-decision support system of claim 2, said operational metrics comprising an aggregated data model, said aggregated data model being aggregated from two or more data models derived by the product-lifecycle-management analytics subsystem based on at least a portion of the data from the atomic level data store.

16. A method of improving management of quality and cost of a product throughout the product lifecycle of the product comprising the steps of:

capturing atomic level data on component information, configuration information, and field information from one or more parties involved throughout the product lifecycle;

compiling one or more subsets of data from the atomic level data;

performing cost or quality analysis on the subset to determine first analysis results;

providing the first analysis results to a user for identifying patterns, associations or correlation in the first analysis results;

delivering warning notifications based on the first analysis results;

based at least in part on the identified patterns, associations or correlation, receiving configuration input from the user;

based on the configuration input, further analyzing detailed data in the atomic level data to produce second analysis results; and

presenting the second analysis results.

17. The method of claim 16, wherein the first analysis results are one or more operational metrics or one or more cost or quality factors.

18. The method of claim 16, wherein the step of further analyzing detailed data comprises mining data in the atomic level data.

19. The method of claim 17, wherein the warning notifications are based on the one or more of the operational metrics meeting a threshold.

20. The method of claim 16, said step of capturing atomic level comprising receiving atomic level data from parties involved throughout the product lifecycle, said parties comprising one or more of suppliers, original equipment manufacturers, assembly manufacturers, field service providers, call centers, sales entities, and consumers.

21. The method of claim 17, wherein said one or more operational metrics are determined based on atomic level data compiled from one or more stage(s) of the product lifecycle, said stages comprising one or more of:

design, manufacture, storage, assembly, configuration, shipping, delivery, handling, sales, and customer use.

22. The method of claim 16, said component information comprising one or more of supplier information, original equipment manufacturer information, component list, manufacturing environment information, lot data, serial numbers, test results, picking data, packaging data, and shipping data.

23. The method of claim 16, said configuration information comprising one or more of hardware-hardware configuration information, hardware-software configuration information, and software-software configuration information.

24. The method of claim 16, said field information comprising one or more of field-test information, warranty information, repair information, return information, technical support information, and technical service information.

25. The method of claim 16, said step of further analyzing the detailed data comprising conducting one or more of:

product management analysis, process management analysis, service supplier analysis, management analysis, component supplier analysis, warranty analysis, maintenance analysis, and forecasting analysis.

26. In a system for improving management of quality and cost of a product throughout the product lifecycle of the product, a method for providing reiterative feed back to the system from a user, the method comprising the steps of:

capturing atomic level data on component information, configuration information, and field information from one or more parties involved throughout the product lifecycle;

compiling one or more subsets of data from the atomic level data;

performing cost or quality analysis on the subset to determine first analysis results;

providing the first analysis results to a user for identifying patterns, associations or correlation in the first analysis results;

delivering warning notifications based on the first analysis results;

based at least in part on the identified patterns, associations or correlation, receiving feedback data from the user;

based on the feedback data, further analyzing detailed data in the atomic level data to produce second analysis results; and

presenting the second analysis results.