Multivariate cumulative sum (MCUSUM) control charts are widely used in industry because they are powerful and easy to use. They cumulate recent process data to quickly detect out-of-control situations. MCUSUM procedures will usually give tighter process control than classical quality control charts. A MCUSUM signal does not mean that the process is producting bad product. Rather it means that a...

Control limits in traditional Multivariate Quality Control Charts (MQCC), such as Hotelling's T control chart, Multivariate Cumulative Sum (MCUSUM) and Multivariate Exponentially-Weighted Moving Average (MEWMA) control charts are based on multivariate normal assumption. This assumption is not usually satisfied, especially in real life applications. The purpose of a control chart based on suppor...

In the development of industrial processes there are situations where it is necessary to control or simultaneously monitor two or more quality variables of the production process. The problems of process monitoring where several related variables are studied can be controlled by means of multivariate control charts. The objective of this work is to describe the implementation of the control cha...

Extended Abstract In many situations, the quality of a process can be characterized by a single continuous random variable, which is usually assumed to follow a normal distribution. However, it is increasingly common for processes to be characterized by several, usually correlated, variables. (Kim and Reynolds 2005) Multivariate control charts are widely used to monitor industrial processes (Ma...

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...

Statistical process control charts have been widely used for monitoring the quality characteristics of manufacturing processes. Analysis of unnatural patterns is an important aspect of control charting. The occurrence of unnatural patterns implies that a process is influenced by assignable causes, and corrective actions should be taken. In practice, there are many situations in which the simult...

background: one of the key issues in determining the quality of water in rivers is to create a water quality control network with a suitable performance. the measured qualitative variables at stations should be representative of all the changes in water quality in water systems. since the increase in water quality monitoring stations increases annual monitoring costs, recognition of the station...

Background: Nowadays, quality of patient care is one of the major and important concerns of health care delivery which is extremely dependent on the medical staff. Objectives: The purpose of this study was to investigate the relationship between Quality of Work Life (QWL) and quality of patient care. Methods: This study was a descri...