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...

S EVERAL DIFFERENT TYPES of commercially available control specimens (Chemonitor,* Enzatrol,* and Versatol, A, A Alt., E, and E-Nt) are employed by many laboratories for quality control purposes. These control specimens may be submitted each day for each determination performed in the laboratory. Unless the concentrations of these control specimens are made to vary in some unpredictable fashion...

Micro-manufacturing processes are characterized by high process variability and an increased significance of measurement uncertainty in relation to tight tolerance specifications. Therefore, an approach that separates the superposition of measurement and manufacturing variation is demanded. A novel design for a quality control chart that makes it possible to monitor, control and extract measure...

Introduction The outcome of thyroid surgery is usually assessed according to two major complications: recurrent laryngeal nerve paralysis and hypoparathyroidism. The purpose of the study was to monitor the outcomes of thyroid surgery using Shewhart control charts, and then to identify possible ways to improve quality by exploring the special causes of variation in the observed complication rate...

One of the objectives of the research done in statistical process control is to obtain control charts that show few false alarms but, at the same time, are able to detect quickly the shifts in the distribution of the quality variables employed to monitor a productive process. In this paper the synthetic-T control chart is developed, which consists of the simultaneous use of a CRL chart and a Ho...

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...

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...

Control chart techniques have been widely used in industries to monitor a process in quality improvement. Whenever we deal with variables data, we usually employ a combination of X-bar chart and R chart (or S chart) to monitor both the center and the spread of the process. In this paper, we propose a simple alternative, that is, we design a single chart to monitor both the center and the spread...

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...