SYSTEM AND METHOD FOR COMBINATION-BASED DATA ANALYSIS

• US 20130110874A1
• Filed: 12/18/2012
• Published: 05/02/2013
• Est. Priority Date: 10/14/2008
• Status: Active Grant

• Current Assignee: Siemens Industry Software Limited (Siemens AG)
• Original Assignee: Camstar Systems, Inc. (Siemens AG)
• Inventors: Shah-Hosseini, Amin
• Granted Patent: US 10,120,912 B2
• Time in Patent Office: Days
• US Class Current: 707/776
• CPC Class Codes: G06F 16/2465 Query processing support for facilitating data mining operations in structured databases
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

A method and system for supply chain data analysis. The method includes storing supply chain data including test data, genealogy data, repair data, some factors and some items, in one or more databases and selecting a portion of the factors from the stored data, and a time range for analysis. The method then selects one or more criterion for analysis; extracts a portion of the stored data; analyzes the extracted portion of the stored data to detect a plurality of faulty combination of factors and items that results in an unexpected change in a key performance index, according to said extracted portion of the plurality of combinations. The method then performs correlation analysis on said plurality of faulty combinations to determine a root cause for the detected combination of factors; and generates a subset of said plurality of faulty combinations, according to said root causes of said plurality of faulty combinations.

Claims

1. A computer implemented method for supply chain data analysis, the method comprising:

storing supply chain data including test data, genealogy data, repair data, a plurality of factors and a plurality of items, in one or more databases;

aggregating, by one or more computers, the stored supply chain data in a plurality of combinations, each combination including one or more items and one or more actors;

receiving one or more criteria for analysis;

extracting, by the one or more computers, a portion of the plurality of combinations according to received one or more criteria;

analyzing said extracted portion of the plurality of combinations, by the one or more computers, to detect a plurality of faulty combination of factors and items that results in an unexpected change in a key performance index, according to said extracted portion of the plurality of combinations;

performing correlation analysis on said plurality of faulty combinations, by the one or more computers, to determine a root cause for each faulty combination; and

generating, by the one or more computers, a subset of said plurality of faulty combinations, according to said root causes of said plurality of faulty combinations.

2. The method of claim 1, further comprising

performing trend analysis on said subset of said plurality of faulty combinations;

and

making recommendation for promising combinations and range of test parameters to maximize a yield for selected factors.

3. The method of claim 1, further comprising performing an entropy-based function that shows the variations between items for each of a plurality of selected factors.

4. The method of claim 1, further comprising allocating resources including one or more of stations, fixtures, and operators to maximize resource utilization.

5. The method of claim 1, wherein the factors include one or more of the group consisting of product, product category, product version, test, subtest, measurement, station, station type, operator, assembly line, site, building, software version, hardware version, component, join, board version, fail category, error message, component parent, component supplier, component child, fail code, defect code, repair type, component, location of a component on a product, part number, lot name, lot size, customer, site, product, call reason, operator, defect code, severity, and failed component.

6. The method of claim 1, wherein said detecting a root cause comprises braking down a test measurement based on related factors and items in said plurality of faulty combinations.

7. The method of claim 1, further comprising creating production plans based on a required number of units to be tested in a period of time, a test time, and a failure rate.

8. The method of claim 1, further comprising extracting test data, return and maintenance data, and supplier data, from the stored data.

9. The method of claim 1, further comprising extracting data insights from the stored data collected from test, return and repair, and supply chain processes.

10. The method of claim 1, further comprising performing what-if analysis on a plurality of selected factors according to said subset of said plurality of faulty combinations.

11. The method of claim 1, further comprising allocating resources including one or more of stations, fixtures, and operators according to one or more desired functions selected by the user including one or more of minimize cost, minimize scrap, and minimize time-in-process, and outputting best one or more combinations that meet the selected desired functions.

12. The method of claim 1, wherein said extracting a portion of the plurality of combinations according to selected one or more criteria comprises retrieving factor properties from the stored data, integrating the retrieved factor properties to find different combinations of factors, retrieving subtest data to be matched with the different combinations, retrieving measurement data to be matched with the combinations, and determining test structure and test limits.

13. The method of claim 1, further comprising accepting a threshold quantity and a threshold percentage for a group of data points that share same values for one or more of a plurality of selected factors, and displaying a bubble chart graph depicting a plurality of indicators with increasing sizes to illustrate more data in said group of data points.

14. The method of claim 1, wherein said key performance index includes one or more of test failure, product return, part repair, return rate and genealogy factor.

15. A system for supply chain data analysis comprising:

one or more databases for storing supply chain data including test data, genealogy data, repair data, a plurality of factors and a plurality of items;

an input device for selecting a portion of the plurality of factors from the stored data, and a time range for analysis, and selecting one or more criteria for analysis;

an extract &

calculate module electrically coupled to the one or more databases for aggregating the stored supply chain data in a plurality of combinations, each combination including one or more items and one or more factors and extracting a portion of the plurality of combinations according to one or more criteria; and

a multi-dimensional failure analysis module for analyzing said extracted portion of the plurality of combinations to detect a plurality of faulty combination of factors and items that results in an unexpected change in a key performance index, according to said extracted portion of the plurality of combinations, performing correlation analysis on said plurality of faulty combinations to determine a root cause for each faulty combination; and

generating a subset of said plurality of faulty combinations, according to said root causes of said plurality of faulty combinations.

16. The system of claim 15, wherein the multi-dimensional failure analysis module is configured to perform an entropy-based function that shows the variations between items for each selected factor.

17. The system of claim 15, further comprising a test configuration analysis module for finding promising configurations and range of test parameters to maximize a yield.

18. The system of claim 17, wherein the test configuration analysis module is configured to create production plans based on a required number of units to be tested in a period of time, test time, and failure rate.

19. The system of claim 15, wherein the extract &

calculate module is configured to extract data insights from the stored data collected from test, return and repair, and supply chain processes.

20. The system of claim 15, further comprising a production planning module for allocating resources including one or more of stations, fixtures, and operators according to one or more desired functions selected by the user including one or more of minimize cost, minimize scrap, and minimize time-in-process, and outputting best one or more combinations that meet the selected desired functions.

21. The system of claim 15, wherein said key performance index includes one or more of test failure, product return, part repair, return rate and genealogy factor.