The special causes of variations, which is also known as a shift, can occur in single or more than one related process characteristics. Statistical control tools such charts are useful to monitor shifts the parameters (location and/or dispersion). In real-life situation, shift emerging different sizes, and it hard identify with classical charts. Moreover, characteristics required attention beca...

Abstract Zaman et al. 1 proposed an adaptive exponential weighted moving average (AEWMA) control chart (CC) by integrating Hampel function features into the traditional EWMA CC structure. The purpose of this study is to find and fix inaccuracies in AEWMA . We reproduce results after rectifying errors applying Monte Carlo simulation. corrected version indicates better performance as compared run...

Time-weighted graphs are used to detect small shifts in statistical process control. The aim of this study is to evaluate the inclination of the acetabular component with CUmulative SUM (CUSUM) chart, Moving Average (MA) chart, and Exponentially Weighted Moving Average (EWMA) chart. The data were obtained directly from thirty patients who had undergone total hip replacement surgery at Ondokuz M...

The exponentially weighted moving average (EWMA) control chart is a popular tool used to monitor and identify slight unnatural variations in the manufacturing, industrial, service processes. In general, charts operate under assumption of normality observation attention quality feature, but it not easy maintain this practice. such situations, data random processes are correlated data, as stock p...

Automation in the service industry is emerging as a new wave of industrial revolution. Standardization and consistency quality an important part automation process. The control methods widely used manufacturing can provide measurement process monitoring. In particular, chart online monitoring technique be to quickly detect whether out control. However, more difficult than that because variabili...

During the last two decades, many new methods have appeared in statistical process monitoring with synthetic-type control charts being a prominent constituent. These became popular due to their simplicity and proclaimed excellent change point detection performance. Synthetic are nothing more than application of well-known long established runs rules. We show that better performance can be obtai...

Designs of the double sampling (DS) X chart are traditionally based on the average run length (ARL) criterion. However, the shape of the run length distribution changes with the process mean shifts, ranging from highly skewed when the process is in-control to almost symmetric when the mean shift is large. Therefore, we show that the ARL is a complicated performance measure and that the median r...

The sensitivity of four tests on Shewhart type Q-eharis and of specially designed EWMA and CUSUM Q-eharts to detect one-step permanent shifts of either a normal mean or standard deviation has been studied. The usual test that signals for one point beyond three standard deviations on a Shewhart chart is found to have poor sensitivity, generally. The test that signals when four out of five consec...

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...