Multivariate quality control charts show some advantages to monitor several variables in comparison with the simultaneous use of univariate charts. Nevertheless, there are some disadvantages when multivariate schemes are employed. The main problem is how to interpret the out-of-control signal of a multivariate chart. For example, in the case of control charts designed to monitor the mean vector...

Control charts are extensively used in manufacturing contexts to monitor production processes. This article illustrates economical design of a variable sample size and control limit Hotelling’s T2 control chart based on a novel cost model when occurrence times of the assignable causes are exponentially distributed. The proposed nonlinear cost model is an extension of Duncan’s (J Am Stat ...

Control charts have been used effectively for years to monitor processes and detect abnormal behaviors. However, most control charts require a specific distribution to establish their control limits. The bootstrap method is a nonparametric technique that does not rely on the assumption of a parametric distribution of the observed data. Although the bootstrap technique has been used to develop u...

Traditional literature on statistical quality control discusses separately multivariate control charts for independent processes and univariate control charts for autocorrelated processes. We extend univariate residual monitoring to the multivariate environment, and propose using vector autoregressive residuals (VAR) to monitor multivariate processes in the presence of serial correlation. We ma...

Control charts are graphical tools widely used to monitor manufacturing processes to quickly detect any change in a process that may result in a change in product quality. The statistic plotted on a control chart is based on samples of n ≥ 1 observations (rational subgroups) that may be taken at regular sampling intervals. However, there are numerous practical applications using individual obse...

Usually, there are two phases in constructing a multivariate control chart. Phase I is to estimate the in-control process parameters and to establish control limits using historical data. Phase II is when the control limits are used to monitor the process. This paper focuses on determining the optimal number of samples in Phase I since number of samples affect the cost of quality control in pra...

The most applied statistical methods for monitoring multivariate attribute processes have been developed assuming that they have a multinomial distribution, see e.g. Marcucci (1985) and Cassady and Nachlas (2006). However this assumption is not always reasonable; indeed, it is more general and correct to suppose that in each item it is possible to identify one or more of k ordered and not mutua...

In this paper, a Multivariate-Multistage Quality Control (MVMSQC) procedure is investigated. In this procedure discriminate analysis, linear regression and control chart theory are combined to control the means of correlated characteristics of a process, which involves several serial stages. Furthermore, the quality of the output at each stage depends on the output of the previous stage as well...