Multivariate control chats are generally used in situations where the simultaneous monitoring or control of two or more related quality characteristics is necessary. In most processes in the real world, distribution of the process characteristics are unknown or at least non-normal, so the non-parametric or distribution-free charts are desirable. Most non-parametric statistical process-control t...

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the...

Nonparametric control charts are presented in order to figure out the problem of detecting changes in the process median (or mean)‎, ‎or changes in the variability process where there is limited knowledge regarding the underlying process‎. ‎When observations are reported imprecise‎, ‎then it is impossible to use classical nonparametric control charts‎. ‎This paper is devoted to the problem of c...

Overdispersion is a phenomenon that generally occurs in the analysis of large sample sizes. In discrete data analysis, it refers to presence variation higher than implied by reference Binomial or Poisson distributions. The proportion nonconforming units clinical laboratories presents high variability and, generally, overdispersion. Therefore, required analyze most appropriate control charts ove...

a new method is developed for a fast identification of the stability situation of industrial processes. the proposed method includes two factor ratios of the control constants for the upper and lower control limits to process these constants. an indication ratio is then defined as the ratio of the maximum data range value to the difference between the maximum and average values for individual d...

1. Abstract The article considers the variable process control scheme for two dependent process steps with incorrect adjustment. Incorrect adjustment of a process may result in shifts in process mean, ultimately affecting the quality of products. We construct variable sample size and variable sampling interval (VSSI) control charts to effectively monitor the quality variable produced by the fir...

usually, in monitoring schemes the nominal value of the process parameter is assumed known. however, this assumption is violated owing to costly sampling and lack of data particularly in healthcare systems. on the other hand, applying a fixed control limit for the risk-adjusted bernoulli chart causes to a variable in-control average run length performance for patient populations with dissimilar...