Monitoring location and scale of multivariate processes subject to a multiplicity of assignable causes

ABSTRACT

This paper presents a fully adaptive multivariate statistical process control scheme for processes where multiple assignable causes may occur. The assignable causes affect both the mean vector and the covariance matrix, monitored by a T² chart and a control chart based on entropy, respectively. Markov chain theory is employed to model the stochastic operation of the proposed scheme and optimize its economic-statistical operation. A numerical investigation and a sensitivity analysis are conducted in order to evaluate the performance and investigate the robustness of the model. A real example is employed to illustrate the operation of the scheme.

KEYWORDS:

• Quality control
• Statistical Process Control (SPC)
• correlation
• multiple assignable causes
• multivariate analysis

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Konstantinos A. Tasias

Konstantinos A. Tasias is an aeronautical engineer while he also holds a degree in business administration and a master’s degree in Industrial Engineering and Operations Management. He has obtained a PhD in Statistical Process Control from the University of Western Macedonia. His main research interests are in the area of Statistical Quality Control, Maintenance and Inventory management.

George Nenes

George Nenes is an associate professor at the Department of Mechanical Engineering at the University of Western Macedonia, Greece. He has obtained a diploma (five-year degree) in Mechanical Engineering, an MSc in Management of Production Systems and a PhD in Statistical Quality Control from the Aristotle University of Thessaloniki. He has worked as a postdoc researcher in Erasmus University of Rotterdam and as a visiting lecturer in various universities in Greece. His work has been published in a variety of journals, including European Journal of Operational Research, IIE Transactions and International Journal of Production Economics. His main research interests are in the area of Statistical Quality Control and Supply Chain Management.