Directional control schemes for processes with mixed-type data

Abstract

Mixed-type data consisting of both continuous observations and categorical observations are becoming prevalent in manufacturing processes and service management. The majority of existing statistical process control tools are designed to monitor either continuous data or categorical data but seldom both. In this article, we propose a directional exponentially weighted moving average control scheme composed of monitoring and diagnosis for mixed-type data. We assume that there is a latent unknown continuous distribution that determines the attribute levels of a categorical variable, and represent both continuous data and categorical data by standardised ranks. The proposed control chart also incorporates directional information to facilitate diagnosing the shift direction. Monte Carlo simulations demonstrate the efficiency of the proposed control scheme.

Keywords:

• EWMA
• latent variable
• ordinal attribute level
• standardised rank
• statistical process control

Acknowledgements

The authors thank the editor, associate editor and two anonymous referees for their many helpful comments that have resulted in significant improvements in the article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Tsung’s research was supported by the Hong Kong RGC General Research Funds [619612], [619913]; Li’s research was supported by the National Natural Science Foundation of China [grant number 71402133]; and the China Postdoctoral Science Foundation [grant number 2014M552464].