Bayesian networks for statistical process control with attribute data
International Journal of Quality & Reliability Management
ISSN: 0265-671X
Article publication date: 23 January 2019
Issue publication date: 21 February 2019
Abstract
Purpose
Bayesian networks (BNs) are implemented for monitoring a process via statistical process control (SPC) where attribute data are available on output from the system. The paper aims to discuss this issue.
Design/methodology/approach
The BN provides a graphical and numerical tool to help a manager understand the effect of sample observations on the probability that the process is out-of-control and requires investigation. The parameters for the BN SPC model are statistically designed to minimize the out-of-control average run length (ARL) of the process at a specified in-control ARL and sample size.
Findings
The BN model outperforms adaptive np control charts in all experiments, except for some cases where only a large change in the proportion of sample defects is relevant. The BN is particularly useful when small sample sizes are available and when managers need to detect small changes in the proportion of defects produced by the process.
Research limitations/implications
The BN model is statistically designed and parameters are chosen to minimize out-of-control ARL. Future advancements will address the economic design of BNs for SPC with attribute data.
Originality/value
The BNs allow qualitative knowledge to be combined with sample data, and the average percentage of defects can be modeled as a continuous random variable. The framework of the BN easily permits classification of the system operation into two or more states, so diagnostic analysis can be performed simultaneously with statistical inference.
Keywords
Acknowledgements
The authors thank the reviewers for providing comments and suggestions which improved the paper.
Citation
Cobb, B. and Li, L. (2019), "Bayesian networks for statistical process control with attribute data", International Journal of Quality & Reliability Management, Vol. 36 No. 2, pp. 232-256. https://doi.org/10.1108/IJQRM-10-2017-0227
Publisher
:Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited