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The k-chart, based on support vector data description, has received recent attention in the literature. We review four different methods for choosing the bandwidth parameter, s, when the k-chart is designed using the Gaussian kernel. We provide results of extensive Phase I and Phase II simulation studies varying the method of choosing the bandwidth parameter along with the size and distribution of sample data. In very limited cases, the k-chart performed as desired. In general, we are unable to recommend the k-chart for use in a Phase I or Phase II process monitoring study in its current form. Copyright © 2017 John Wiley & Sons, Ltd.

Statistical process control (SPC) is a collection of problem-solving tools used to achieve process stability and improve process capability through variation reduction. Because of its sound statistical basis and intuitive use of visual displays, SPC has been extensively used in manufacturing and health care and service industries. Deploying SPC involves both a technical aspect and a proper environment for continuous improvement activities based on management support and worker empowerment. Many of the commonly used SPC tools, including histograms, fishbone diagrams, scatter plots, and defect concentration diagrams, were proposed prior to the advent of microcomputers as efficient methods to record and visualize data for single (or few) variable(s) processes. As the volume, variety, and velocity of data continues to evolve, there are opportunities to supplement and improve these methods for understanding and visualizing process variation. In this paper, we propose enhancements to some of the basic quality tools that can be easily applied with a desktop computer. We demonstrate how these updated tools can be used to better characterize, understand, and/or diagnose variation in a case study involving a US manufacturer of structural tubular metal products. Finally, we create the quality visualization toolkit to allow practitioners to implement some of these visualization tools without the need for training, extensive statistical background, and/or specialized statistical software. Copyright © 2014 John Wiley & Sons, Ltd.

Methods are examined for obtaining probability limits for Phase I Shewhart ―X charts when the process mean and standard deviation are estimated. The design methods assume m independent random samples of size n will be taken periodically from a normally distributed process. It is shown that the joint distribution of the standardized subgroup means follows either an approximate or exact multivariate t distribution, depending on the standard deviation estimator used. The multivariate t distribution is used to define the probability limits for the Phase I ―X chart. Extensive simulation compares the performance of the proposed limits with other design procedures. Tables of design constants are given for , and simple procedures for obtaining design constants are given for . Copyright © 2004 John Wiley & Sons, Ltd.

A count of the number of defects is often used to monitor the quality of a production process. When defects rarely occur in a process, it is often desirable to monitor the time between the occurrence of each defect rather than a count of the number of defects. An exponential distribution often provides a useful model of the time between defects. Phase I control charts for exponentially distributed processes are discussed. Methods for computing the control limits are given and the overall Type I error rates of these charts are evaluated. Copyright © 2002 John Wiley & Sons, Ltd.