An effective control scheme can be instrumental in increasing productivity and reducing cost. While facing an outlier-existing process, using the mean ( X ) control chart and the range (R) control chart for monitoring the process mean and variance will lead to high level false alarms. Recently, some median ( X~ ) control charts, such as the X~ Shewhart control chart, the exponentially weighted ...

Healthcare Failure Mode and Effects Analysis (HFMEA) is a methodology for correcting latent system errors before they lead to adverse events. We examined the utility of HFMEA in evaluating the sterilization and use of surgical instruments. First, a multidisciplinary team graphed the process in a flow diagram. A hazard analysis was then used to examine potential failure modes (i.e., ways in whic...

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...

In many processes in real practice at the start-up stages the process parameters are not known a priori and there are no initial samples or data for executing Phase I monitoring and estimating the process parameters. In addition, the practitioners are interested in using one control chart instead of two or more for monitoring location and variability of processes. In this paper, we consider a s...

Process monitoring and fault diagnosis using profile data remains an important and challenging problem in statistical process control (SPC). Although the analysis of profile data has been extensively studied in the SPC literature, the challenges associated with monitoring and diagnosis of multichannel (multiple) nonlinear profiles are yet to be addressed. Motivated by an application in multi-op...

Nonparametric control charts are useful in statistical process control (SPC) when there is a lack of or limited knowledge about the underlying process distribution, especially when the process measurement is multivariate. This paper develops a new multivariate self-starting methodology for monitoring location parameters. It is based on adapting the multivariate spatial rank to on-line sequentia...

In recent years, a growing number of software organizations have begun to focus on applying the concepts of statistical process control (SPC) to the software process, usually as part of an improvement program based on the Software CMM. There are a number of technical challenges to the successful use of these statistical techniques, primarily centered on the issues associated with high variatio...

Previously, it has been held that statistical process control (SPC) and engineering process control (EPC) were two distinct domains for process improvement. However, we design four control policies to integrate the two approaches using a proportional-integral-derivative (PID) feedback controller and Shewhart or moving centerline exponentially weighted moving average (MCEWMA) charts. We specific...