I N recent years, statistical process control (SPC) for autocorrelated processes has received a great deal of attention, due in part to the increasing prevalence of autocorrelation in process inspection data. With improvements in measurement and data collection technology, processes can be sampled at higher rates, which often leads to data autocorrelation. It is well known that the run length p...

In intervention studies in which randomization to groups is not possible, researchers typically use quasi-experimental designs. Time series designs are strong quasi-experimental designs but are seldom used, perhaps because of technical and analytic hurdles. Statistical process control (SPC) is an alternative analytic approach to testing hypotheses about intervention effects using data collected...

The deployment of statistical process control (SPC) in manufacturing environments is a prominent global phenomenon. Statistical Process Control largely used industries for monitoring the parameters. It standard method visualizing and controlling processes on basis measurements randomly selected samples. SPC was to evaluate quality production five sample products from Injection mould system year...

• Objective: To apply statistical process control (SPC) techniques to evaluate 3 components of the phacoemulsification cataract surgery procedure. • Design: Retrospective chart review. • Methods: We analyzed 180 cataract surgeries performed by 6 cataract surgeons (3 “high-volume” and 3 “low-volume” surgeons). Surgical parameters evaluated were phacoemulsification time (seconds), phacoemulsifica...

Control chart is the most important Statistical Process Control (SPC) tool used to monitor reliability and performance of manufacturing processes. Variability EWMA charts are widely used for the detection of small shifts in process dispersion. For ease in computation all the variability EWMA charts proposed so far are based on asymptotic nature of control limits. It has been shown in this study...

Control charts are standard statistical process control (SPC) tools for detecting assignable causes. These charts trigger a signal when a process gets out of control but they do not indicate when the process change has begun. Identifying the real time of the change in the process, called the change point, is very important for eliminating the source(s) of the change. Knowing when a process has ...

Conventional Statistical Process Control (SPC) schemes fail to monitor nonlinear and finite-state processes that often result from feedback-controlled processes. SPC methods that are designed to monitor autocorrelated processes usually assume a known model (often an ARIMA) that might poorly describe the real process. In this paper, we present a novel SPC methodology based on context modeling of...

The use of statistical process control methods in monitoring clinical performance To the Editor: The article by Spiegelhalter and colleagues [1] and the Counterpoint papers by Benneyan and Borgman [2], Lim [3], and Bolsin and Colson [4] deserve further comment. At least two issues should be raised, the first of which, the primacy of systems, is of crucial importance. Benneyan and Borgman state ...

Process-Oriented Basis Representation (POBREP) is an effective method of multivariate statistical process control that is seldom used in manufacturing. Rather than monitoring individual process variables, POBREP seeks to identify specific causes of production problems and map those into a basis matrix. These patterns can then be monitored with single-variable SPC techniques or traditional multi...