We describe the adaptation of the decision limit cumulative sum method (cusum) to internal quality control in clinical chemistry. With the decision limit method, the cusum is interpreted against a numerical limit, rather than by use of a V-mask. The method can be readily implemented in computerized quality-control systems or manually on controls charts. We emphasize the manual application here ...

The most commonly used statistical process control charts to detect special causes are Shewhart and Cusum charts. However, the Cuscore chart is a contemporary alternative that is especially well suited for detecting special causes that can be modeled in advance based on their characteristic effect on the system. In this paper, we develop the appropriate Cuscore statistic and the required contro...

Unlike a Shewhart chart, the exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts are memory control charts (also known as time weighted control charts) that are used for a quick detection of small shifts in the process mean. Control charts that combine information from present and past samples, like the EWMA and CUSUM charts have the ability to detect process changes ...

In order to monitor a process, quality practitioners frequently use Shewhart control charts (e.g. X –R, P-charts, etc.), so named after the pioneering work of Dr. Walter Shewhart. It can be shown that if there are sharp, intermittent changes to a process, these types of charts are highly effective. However, if one is interested in a small, sustained shift in a process, other types of control ch...

• The usual CUSUM chart for the mean (CUSUM-X̄) is a chart used to quickly detect small to moderate shifts in a process. In presence of outliers, this chart is known to be more robust than other mean-based alternatives like the Shewhart mean chart but it is nevertheless affected by these unusual observations because the mean (X̄) itself is affected by the outliers. An outliers robust alternative ...

Statistical process control (SPC) requires statistical methodologies that detect changes in the pattern of data over time. The common methodologies, such as Shewhart, cumulative sum (cusum), and exponentially weighted moving average (EWMA) charting, require the in-control values of the process parameters, but these are rarely known accurately. Using estimated parameters, the run length behavior...

A distribution-free tabular CUSUM chart is designed to detect shifts in the mean of an autocorrelated process. The chart’s average run length (ARL) is approximated by generalizing Siegmund’s ARL approximation for the conventional tabular CUSUM chart based on independent and identically distributed normal observations. Control limits for the new chart are computed from the generalized ARL approx...

In this paper, we propose a nonparametric CUSUM control chart for detecting a range of shifts in the location parameters based on previous research. This control chart is dynamically adaptive, ranks method-based nonparametric and self-starting; it can monitor various sizes of shifts in difference distributions simultaneously; and it can be used to monitor processes at the start-up stages. This ...

A control chart is one of the tools Statistical Process Control (SPC) used to monitor a production process. The charts ability detect data that crosses border often done using variable transformation. In this paper, we will introduce cumulative sum (CUSUM) based on Link Relative transformation technique regression approach. results showed with was more sensitive compared previous CUSUM and char...

Control charts are widely used for process monitoring. They show whether the variation is due to common causes or whether some of the variation is due to special causes. To detect large shifts in the process, Shewhart-type control charts are preferred. Cumulative sum (CUSUM) and exponentially weightedmoving average (EWMA) control charts are generally used to detect small and moderate shifts. Sh...