ABSTRACT
This article is the first of its kind which proposes a Variable Parameters (VP) chart to monitor the coefficient of variation (CV). Formulae for various performance measures and the algorithms to optimize these performance measures are proposed. The VP CV chart consistently outperforms the five alternative CV charts in the literature, for all shift sizes. Compared to the Exponentially Weighted Moving Average (EWMA) CV2 chart, the VP CV chart outperforms it for moderate and large shift sizes, while for small shift sizes, the EWMA CV2 chart outperforms the VP CV chart. Subsequently, the VP CV chart is implemented on an industrial example.
Additional information
Notes on contributors
Wai Chung Yeong
Wai Chung Yeong is a Senior Lecturer in the Department of Operations and Management Information Systems, Faculty of Business and Accountancy, Universiti Malaya (UM). He received his Ph.D. from the School of Mathematical Sciences, Universiti Sains Malaysia (USM). His research interest is in Statistical Process Control.
Sok Li Lim
Sok Li Lim is a Senior Lecturer in the Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya (UM). She received her Ph.D. from the School of Mathematical Sciences, Universiti Sains Malaysia (USM). Her research interest is in Statistical Process Control.
Michael Boon Chong Khoo
Michael Boon Chong Khoo is a Professor in the School of Mathematical Sciences, Universiti Sains Malaysia (USM). He received his Ph.D. in Applied Statistics in 2001 from USM. His research interest is in Statistical Process Control. He is a member of the American Society for Quality.
Philippe Castagliola
Philippe Castagliola graduated with a Ph.D. from the UTC (Université de Technologie de Compiègne, France) in 1991. He is currently a Professor at the Université de Nantes, Institut Universitaire de Technologie de Nantes, France. He is also a member of the IRCCyN (Institut de Recherche en Communications et Cybernétique de Nantes), UMR CNRS 6597. His research activities include developments of new SPC techniques (non-normal control charts, optimized EWMA type control charts, control charts with estimated parameters, multivariate capability indices, monitoring of batch processes, etc.).