Estimation of system assembly and test manufacturing yields through product complexity normalization

Cisco Systems, Inc. (Cisco) has recently adopted Six Sigma as the main platform to drive quality improvements in its manufacturing operations. A key component of the improvement strategy is the ability to define appropriate manufacturing yield goals. Cisco's manufacturing operations can be divided, at a very high level, in two major steps: Printed Circuit Board Assembly (PCBA) and System Assembly and Test. The company has already deployed a global yield goal definition methodology for the PCBA operation, but the creation of a similar methodology for the System Assembly and Test operation proved difficult: Cisco lacked a universal methodology to determine the expected variation on manufacturing performance resulting from differences on product design and manufacturing processes attributes. This thesis addresses this gap by demonstrating a methodology to relate relevant design and process attributes to the System Assembly and Test manufacturing yield performance of all products. The methodology uses statistical analysis, in particular Artificial Neural Networks, to generate a yield prediction model that achieves excellent prediction accuracy (4.8% RMS error). Although this study was performed using Cisco Systems' product and manufacturing data, the general process outlined in this exercise should be applicable to solve similar problems in other companies and industries. The core components of the methodology outlined can be easily reproduced: 1) identify the key complexity attributes, 2) design and execute a data collection plan and 3) generate statistical models to test the validity and impact of the selected factors.

Department dc:contributor.department

Leaders for Manufacturing Program at MIT

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2009

Author dc:creator

• Olivella Sierra, Andrés

Advisor dc:contributor.advisor

• David E. Hardt and Roy E. Welsch.

• Engineering Systems Division.
• Sloan School of Management.
• Leaders for Manufacturing Program.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/53221

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/53221