TY - JOUR
AU1 - Ken McNaught
AU2 - Andy Chan
AB - 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.
TI - Bayesian networks in manufacturing
JF - Journal of Manufacturing Technology Management
DO - 10.1108/17410381111149611
DA - 2011-07-26
UR - https://www.deepdyve.com/lp/emerald-publishing/bayesian-networks-in-manufacturing-4JWkaq0riJ
SP - 734
EP - 747
VL - 22
IS - 6
DP - DeepDyve
ER -