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

• US 7,945,426 B2
• Filed: 05/22/2010
• Issued: 05/17/2011
• Est. Priority Date: 01/12/2006
• Status: Expired due to Fees

• Current Assignee: GlobalFoundries, Inc.
• Original Assignee: International Business Machines Corporation
• Inventors: Thomas, John C., Malkin, Peter Kenneth, Layton, Jeff R., Kostyk, Timothy J., Santhanam, Padmanabhan, Perun, Stephen G., Pyra, Kenneth L., Kratschmer, Theresa C., Weller, Scott W.
• Primary Examiner(s): KHUU, HIEN DIEU THI
• Application Number:
  US12/785,435
• Publication Number:
  US 20100234976A1
• Time in Patent Office: 360 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 of a design, production and testing process in a mass manufacturing process includes steps of: collecting error data relating to a product; classifying the error data into categories of symptoms; mapping the symptom to a revealing condition of the product; mapping the revealing condition to a test type; mapping a scope of a fix to phases of error injection mapping; and recommending modifications to an end user for at least one of the design, production, delivery, and testing process based on the scope of the fix.

Claims

1. A computer node in a network, said computer node comprising:

a central processing unit;

a network interface coupled with the central processing unit for enabling communication with other computer nodes in the network;

a storage device;

a database; and

a memory comprising logic for;

receiving an http request;

determining which handler among a plurality of handlers to invoke in order to process the request; and

invoking the handler;

wherein the handler is one of;

an error data collection handler for collecting error data comprising a symptom relating to the product, and storing the error data in the database;

an error data classification handler for classifying the error data and storing results of the classifying in the database;

an analysis handler for using the classified error data stored in the database for;

mapping the symptom to a revealing condition by classifying the symptom into at least one attribute based on a phase of error injection and a scope of a fix for the error, an action, and a duration of ownership;

wherein said symptom includes as an attribute a service context in which a given defect was found, wherein said service context comprises;

scheduled maintenance, non-scheduled maintenance, and product recall;

mapping the revealing condition to a test type; and

mapping the scope of the fix to the phase of error injection; and

a suggested actions report handler for recommending modifications to an end user for at least one of the design, testing, manufacture, and delivery of the product, wherein said recommended modifications comprise;

addition of a new test phase, an indication of whether or not a given produce is ready for public sale, and justifications for the given recommendations.

2. The computer node of claim 1 wherein said computer node is an HTTP server.

3. The computer node of claim 1 wherein the handlers are embodied as logic within the memory and executing on the computer node.

4. The computer node of claim 1 wherein the database comprises a PSEC server database.

5. The computer node of claim 4 wherein the memory further comprises:

a PSEC client handler; and

a PSEC client interface servlet.

6. A non-transitory computer readable storage medium comprising computer program instructions stored therein for causing a computer processor to optimize at least one of a design, testing, manufacturing, and delivery process for a product in a mass manufacturing process, wherein said computer program instructions cause the computer to execute steps of:

collecting error data comprising a symptom relating to the product;

storing the error data

mapping the symptom to a revealing condition by classifying the symptom into at least one attribute based on a phase of error injection and a scope of a fix for the error, an action, and a duration of ownership;

wherein said symptom includes as an attribute a service context in which a given defect was found, wherein said service context comprises;

scheduled maintenance, non-scheduled maintenance, and product recall;

mapping the revealing condition to a test type;

mapping the scope of the fix to the phase of error injection; and

recommending modifications to an end user for at least one of the design, testing, manufacture, and delivery of the product based on the scope of the fix, wherein said recommended modifications comprise;

addition of a new test phase, an indication of whether or not a given produce is ready for public sale, and justifications for the given recommendations.

7. The non-transitory computer readable storage medium of claim 6 wherein the steps of collecting, mapping the symptom, mapping the revealing condition, mapping the scope, and recommending modification are performed at scheduled intervals.

8. The non-transitory computer readable storage medium of claim 6 wherein the steps of collecting, mapping the symptom, mapping the revealing condition, mapping the scope, and recommending modification are performed for every subsequent version of the product.

9. The non-transitory computer readable storage medium of claim 6 wherein the symptom comprises at least one attribute selected from a group consisting of:

phase when found, vehicle identification, unique identifier, open data, close data, 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 affected, and complexity level.