Method to improve requirements, design manufacturing, and transportation in mass manufacturing industries through analysis of defect data

• US 7,305,325 B2
• Filed: 01/12/2006
• Issued: 12/04/2007
• Est. Priority Date: 01/12/2006
• Status: Expired due to Fees

• Current Assignee: GlobalFoundries, Inc.
• Original Assignee: International Business Machines Corporation
• Inventors: Malkin, Peter K., Thomas, John C., Kratschmer, Theresa C., Layton, Jeff R., Santhanam, Padmanabhan, Weller, Scott W., Perun, Stephen G., Pyra, Kenneth L., Kostyk, Tim J.
• Primary Examiner(s): Barlow; John
• Assistant Examiner(s): KHUU, HIEN DIEU THI
• Application Number:
  US11/330,823
• Publication Number:
  US 20070162257A1
• Time in Patent Office: 691 Days
• Field of Search: 702/183
• US Class Current: 702/183
• 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
• CPC Class Codes: G06Q 10/06395 Quality analysis or management
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

A computer-implemented method of optimizing at least one production or testing processes in a mass manufacturing industry, includes steps of: collecting error data relating to a product at a plurality of points along its production and distribution chain; classifying the error data into categories of errors to provide classified error data; analyzing relationships among the classified error data; and suggesting modifications to at least one of the production or testing processes based on the analysis.

Claims

1. A computer-implemented method of optimizing at least one of a design, production or testing process in a mass manufacturing process, the method comprising steps of:

collecting error data relating to a product at a plurality of points along its design, production and distribution chain, wherein the error data comprises both test data found during production testing of the product, as well as post production customer service events;

classifying the error data into categories of errors to provide classified error data;

analyzing relationships among the classified error data; and

recommending modifications to an end user for at least one of the design, production, delivery, or testing processes based on the analysis, the recommending step comprising steps of;

recommending modifications to the design of the product if the error data is collected after design but before manufacture of the product, and if a design error is found;

recommending modifications to the delivery process if the error data is collected after delivery, and if an error is found;

recommending modifications to the production process if the error data is collected after producing part of the product and if a part error is found;

recommending modifications to the process of making a subsystem of the part if the error data is collected after producing the subsystem of the product comprising the part; and

recommending modifications to the mass manufactuing process if the error data is collected after mass manufacturing, but before delivery, and if a mass manufacturing error is found.

2. The method of claim 1, wherein the categories of errors include at least one of the following:

errors found during product development;

errors found in instances of the product after manufacturing, but before delivery;

errors that occur as a result of the transportation process; and

errors found by a product service provider.

3. The method of claim 1 wherein the modifications include a correction of problems in one or more of the following:

design, test, manufacturing, and transportation in the mass manufacturing process.

4. The method of claim 1 wherein the method is performed in a plurality of iterations to ensure continuous improvement.

5. The method of claim 1, wherein the error data is at least one selected from the following attribute types:

phase when found, vehicle identification, unique identifier, revealing condition, open date, close date, customer impact, ownership duration, product impact, non-product impact, scope of fix, corrective action, responsible agent, part history, part hierarchy, part number, number of hits affect, and complexity level.

6. The method of claim 1, further comprising steps of:

mapping a symptom to a revealing condition;

mapping the revealing condition to a test type, and

mapping a scope of a fix to phases of error injection mapping.

7. The method of claim 1, wherein error data and analysis from a parts supplier can be used for a more comprehensive analysis of an organization that uses the supplied parts.

8. The method of claim 1, wherein classifications are derived automatically.

9. The method of claim 8, wherein the classifications are based on one or more of a scope of fix, action, and a duration of ownership, and phase of error injection.

10. The method of claim 1, further comprising

performing an analysis of aspects of the mass manufacturing development process and product, including at least one of:

evaluating testing effectiveness;

evaluating mass manufacturing process;

evaluating;

transportation process;

identifying safety concerns;

comparing in-process with post sales problems.

11. The method of claim 10, wherein the analysis of a given aspect of the mass manufacturing process includes the generation of two or more graphical representations of the classified error data.

12. The method of claim 11, wherein each graphical presentation includes one or more interpretations, including text.

13. The method of claim 1 further comprising a step of enabling the end user to provide the method steps of collecting, classifying, analyzing and recommending for a second user.

14. The method of claim 13 further enabling the end user to provide the steps of collecting, classifying, analyzing and recommending on a continuing basis to the second user.

15. The method of claim 13 further enabling the end user to update the steps or analysis techniques or both.

16. The method of claim 1 wherein the product of the mass manufacturing development process includes integrated software, hardware, and electronics.

17. The method of claim 1 wherein error data and analysis are used as a reliability measure for the overall quality of the process and product.