Bayesian networks in manufacturing

Bayesian networks in manufacturing Purpose – The purpose of this paper is to raise awareness among manufacturing researchers and practitioners of the potential of Bayesian networks (BNs) to enhance decision making in those parts of the manufacturing domain where uncertainty is a key characteristic. In doing so, the paper describes the development of an intelligent decision support system (DSS) to help operators in Motorola to diagnose and correct faults during the process of product system testing. Design/methodology/approach – The intelligent (DSS) combines BNs and an intelligent user interface to produce multi‐media advice for operators. Findings – Surveys show that the system is effective in considerably reducing fault correction times for most operators and most fault types and in helping inexperienced operators to approach the performance levels of experienced operators. Originality/value – Such efficiency improvements are of obvious value in manufacturing. In this particular case, additional benefit was derived when the product testing facility was moved from the UK to China as the system was able to help the new operators to get close to the historical performance level of experienced operators. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Manufacturing Technology Management Emerald Publishing

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Publisher
Emerald Publishing
Copyright
Copyright © 2011 Emerald Group Publishing Limited. All rights reserved.
ISSN
1741-038X
DOI
10.1108/17410381111149611
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to raise awareness among manufacturing researchers and practitioners of the potential of Bayesian networks (BNs) to enhance decision making in those parts of the manufacturing domain where uncertainty is a key characteristic. In doing so, the paper describes the development of an intelligent decision support system (DSS) to help operators in Motorola to diagnose and correct faults during the process of product system testing. Design/methodology/approach – The intelligent (DSS) combines BNs and an intelligent user interface to produce multi‐media advice for operators. Findings – Surveys show that the system is effective in considerably reducing fault correction times for most operators and most fault types and in helping inexperienced operators to approach the performance levels of experienced operators. Originality/value – Such efficiency improvements are of obvious value in manufacturing. In this particular case, additional benefit was derived when the product testing facility was moved from the UK to China as the system was able to help the new operators to get close to the historical performance level of experienced operators.

Journal

Journal of Manufacturing Technology ManagementEmerald Publishing

Published: Jul 26, 2011

Keywords: Bayesian network; Manufacturing; Fault diagnosis; System testing; Intelligent user interface; Decision support; Intelligent manufacturing systems; Mobile communication systems

References

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