Originally developed at Bell Laboratories by Dr Walter Shewhart [1] in 1924 specifically to help detect statistical changes in process quality, control charts have since become one of several primary tools of quality control and process improvement. Quality control charts are chronological graphs of process data that, although based in statistical theory, are easy for practitioners to use and i...

The control of unintended variation is an objective central to modern industrial quality management methods, based largely on the theoretical work of Walter A. Shewhart. As industrial quality management techniques find their place in health care, professionals may feel threatened by the effort to reduce variation. Understanding may reduce this fear. Variation of the types addressed in quality c...

An enzyme-linked immunosorbent assay (ELISA) for the typing of foot and mouth disease virus (FMDV) antigen was employed for the routine laboratory diagnosis of FMD at a regional veterinary laboratory in northern Thailand. An objective procedure was developed to monitor the test performance of the ELISA, using absolute test control limits in a Shewhart-CUSUM (cumulative sum) control chart method...

Nobel-winning Sir Winston Churchill in his history of the Second World War wrote a chapter on the "wizards" who had helped Britain win the war in the air by the development and use of radar. William Grey Walter (1910-1977) was one of those young wizards. He used his experience to design and construct a life-like robot with a nonlinear dynamic brain, which offered an existence proof that brains ...

Parameter estimation causes a considerable stochastic error in standard Shewhart charts. This problem can be solved using suitable correction factors. But if the normality assumption itself fails, in addition a nonvanishing model error will occur. By then, a nonparametric alternative, such as the recently proposed MIN chart, might be a better idea throughout. However, for those reluctant to giv...

This paper presents the economic design of X control charts for monitoring a critical stage of the main production process at a tile manufacturer in Greece. Two types of X-charts are developed: a chart of the Shewhart type with fixed parameters and adaptive charts with variable sampling intervals and/or sample size. Our prime motivation was to improve the statistical control scheme employed for...

On-line monitoring of process variability is strategic to achieve high standards of quality and maintain at acceptable levels the number of nonconforming items. Shewhart control charts are the simplest Statistical Process Control (SPC) procedure to achieve this goal. An efficient implementation of a control chart requires the optimal selection of its design parameters. They can be selected acco...

Statistical process control, a recognized technique for improving quality and productivity, has been widely employed throughout various industries. The conventional Shewhart control charts are applicable only when the collected sample data are real-valued data. For the purpose of controlling uncertain information when interval-valued data inevitably appear in the manufacturing or service proces...

In this paper, we consider a nonparametric Shewhart chart for fuzzy data. We utilize the fuzzy data without transforming them into a real-valued scalar (a representative value). Usually fuzzy data (described by fuzzy random variables) do not have a distributional model available, and also the size of the fuzzy sample data is small. Based on the bootstrap methodology, we design a nonparametric S...