A New Maximum Exponentially Weighted Moving Average Control Chart for Monitoring Process Mean and Dispersion

et al. 2019

Random causes are vital part of every process in manufacturing and nonmanufacturing environments, and these do not affect the product features. Special causes, on the other hand, come because of some burden(s) in a process and requires special attention; otherwise, it ruins the products excellence. Special causes are categorized into small, moderate, and large shifts and are handled by statistical quality control charts. The Shewhart control chart is well known for large shifts, while the cumulative sum and exponentially weighted moving average are more effective in detecting small to moderate shifts. However, in practice, many processes require the simultaneous monitoring of both the small to the large shifts. In this study, we have designed an adaptive EWMA for dispersion parameter in connection with Huber and Tukey's bisquare functions. The performance measures used in this study include average run length, extra quadratic loss, relative average run length, and performancecomparison index. We have observed that the study proposals are good competitors to the other counter parts for an efficient monitoring of shifts of varying amounts. An illustrative example using real data is given to demonstrate the implementation of the study proposal.

Section: Performance Analysis and Comparisonsmentioning

confidence: 99%

An adaptive approach to EWMA dispersion chart using Huber and Tukey functions

Zaman

Lee

et al. 2019

“…Salazar and Sinha 20 were first to develop control charts for monitoring the process mean using RSS and showed that their proposed charts performed better than those charts based on the SRS. Muttlak and Al-Sabah, 21 Abujiya and Muttlak,22 Al-Omari and Haq, 23 Abujiya et al, 24 Mehmood et al, 25 Abujiya et al, 26 and Haq et al [27][28][29] are the major references on the development of control charts using the RSS technique, but no one has given devotion to develop a control chart based on the RSS method with nonparametric approach. So, in this study, we used the RSS method under the scenario of nonparametric approach for development of control chart to monitor the process target.…”

Section: And the Reference Thereinmentioning

confidence: 99%

An Efficient Nonparametric EWMA Wilcoxon Signed‐Rank Chart for Monitoring Location

Abid

Nazir

et al. 2016

The statistical performance of traditional control charts for monitoring the process shifts is doubtful if the underlying process will not follow a normal distribution. So, in this situation, the use of a nonparametric control charts is considered to be an efficient alternative. In this paper, a nonparametric exponentially weighted moving average (EWMA) control chart is developed based on Wilcoxon signed‐rank statistic using ranked set sampling. The average run length and some other associated characteristics were used as the performance evaluation of the proposed chart. A major advantage of the proposed nonparametric EWMA signed‐rank chart is the robustness of its in‐control run length distribution. Moreover, it has been observed that the proposed version of the EWMA signed‐rank chart using ranked set sampling shows better detection ability than some of the competing counterparts including EWMA sign chart, EWMA signed‐rank chart, and the usual EWMA control chart using simple random sampling scheme. An illustrative example is also provided for practical consideration. Copyright © 2016 John Wiley & Sons, Ltd.

“…Recently, the technique has found its way into the area of the statistical quality control. For example, see Abujiya and Muttlak, Al‐Sabah, Jafari and Mirkamali, Al‐Omari and Haq, Mehmood et al ., Haq et al ., Abujiya et al ., Haq et al ., Abujiya et al ., Haq and Al‐Omari, and Mehmood et al ., to name but a few. Comprehensive review on the theory, methods, and applications of RSS can be found in Wolfe .…”

Section: Design Of Combined Shewhart–cumulative Sum S Chart Using Extmentioning

confidence: 99%

A New Combined Shewhart–Cumulative Sum S Chart for Monitoring Process Standard Deviation

Abujiya

Lee

2015

The combined application of a Shewhart chart and cumulative sum (CUSUM) control chart is an effective tool for the detection of all sizes of process shifts as the scheme combines the advantages of a CUSUM at detecting small to moderate shifts and Shewhart for the quick detection of very large shifts. This article proposes new combined Shewhart–CUSUM S charts based on the extreme variations of ranked set sampling technique, for efficient monitoring of changes in the process dispersion. Using Monte Carlo simulations, the combined scheme is designed to minimize the average extra quadratic loss over the entire process shift domain. The results show that the combined Shewhart–CUSUM S charts uniformly outperform several other procedures for detecting increases and decreases in the process variability. Moreover, the proposed scheme can detect changes that are small enough to escape the Shewhart S chart or fairly large to escape detection by the CUSUM S chart. Numerical example is given to illustrate the practical application of the proposed scheme using real industrial data. Copyright © 2015 John Wiley & Sons, Ltd.