Processes characterized by high dimensional and mixture data challenge traditional statistical process control charts. In this study, we propose a multivariate control chart based on the Gower distance that can handle a mixture of continuous and categorical data. An extensive simulation study was conducted to examine the properties of the proposed control chart under various scenarios and compa...

This working paper series is intended to facilitate discussion and encourage the exchange of ideas. Inclusion here does not preclude publication elsewhere. It is the original work of the author(s) and subject to copyright regulations. ABSTRACT We believe that conflicting decision signals resulting from examining Shewhart Mean and Variability Control Charts produce undesirable consequences. Ther...

The combination of Shewhart control charts and an exponentially weighted moving average (EWMA) control charts to simultaneously monitor shifts in the mean output of a production process has proven very effective in handling both small and large shifts. To improve the sensitivity of the control chart to detect off-target processes, we propose a combined Shewhart-EWMA (CSEWMA) control chart for m...

In today's highly competitive industrial environment due to fast technology development, quality practitioners will to detect out-of-control situations and take actions whenever is necessary as soon as possible. Accordingly, new statistical procedures have been enhanced incessantly both to handle high yield processes along with looking for methods of minimizing all quality cost. CCC-r chart, th...

Previously, quality control and improvement researchers discussed multivariate control charts for independent processes and univariate control charts for autocorrelated processes separately. We combine the two topics and propose vector autoregressive (VAR) control charts for multivariate autocorrelated processes. In addition, we estimateAR(p) models instead ofARMAmodels for the systematic cause...

Normality is a common assumption for many quality control charts. One should expect misleading results once this assumption is violated. In order to avoid this pitfall, we need to evaluate this assumption prior to the use of control charts which require normality assumption. However, in certain cases either this assumption is overlooked or it is hard to check. Robust control charts and bootstra...

In many situations, the times between certain events are observed and monitored instead of the number of events particularly when the events occur rarely. In this case, it is common to assume that the times between events follow an exponential distribution. Control charts are one of the main tools of statistical process control and monitoring. Control charts are used in phase I to assist operat...

A review of the literature on cause selecting charts (CSCs) in multistage processes is given, with a concentration on developments which have occurred since 1993. Model based control charts and multiple cause selecting charts (MCSCs) are reviewed. Several articles based on normally and non-normally distributed outgoing quality characteristics are analyzed and important issues such as economic d...

One of the primary tools used in statistical quality control is the control charts. The standard assumptions that are usually cited in justifying the use of control charts are that the data generated by the in control process are independent and identically distributed. However the independency assumption is not realistic in practice. Data sets collected from industrial processes may have corre...

One-class classification problems have attracted a great deal of attention from various disciplines. In the present study, we attempt to extend the scope of application of the one-class classification technique to statistical process control (SPC) problems. We propose new multivariate control charts that apply the effectiveness of one-class classification to improvement of Phase I and Phase II ...