Rashid Mehmood

Bio: Rashid Mehmood is an academic researcher from Universiti Teknologi Malaysia. The author has contributed to research in topics: Control chart & Statistical process control. The author has an hindex of 10, co-authored 22 publications receiving 359 citations. Previous affiliations of Rashid Mehmood include Quaid-i-Azam University & King Fahd University of Petroleum and Minerals.

Control charts for location based on different sampling schemes

Abstract: Control charts are the most important statistical process control tool for monitoring variations in a process A number of articles are available in the literature for the X control chart based on simple random sampling, ranked set sampling, median-ranked set sampling (MRSS), extreme-ranked set sampling, double-ranked set sampling, double median-ranked set sampling and median double-ranked set sampling In this study, we highlight some limitations of the existing ranked set charting structures Besides, we propose different runs rules-based control charting structures under a variety of sampling strategies We evaluate the performance of the control charting structures using power curves as a performance criterion We observe that the proposed merger of varying runs rules schemes with different sampling strategies improve significantly the detection ability of location control charting structures More specifically, the MRSS performs the best under both single- and double-ranked set strategies with varyi

On the Performance of Auxiliary‐based Control Charting under Normality and Nonnormality with Estimation Effects

Abstract: Process monitoring through control charts is a quite popular practice in statistical process control. From a statistical point of view, a superior control chart is the one which has an efficient design structure, for the case of both known and unknown parameters. There are auxiliary information–based location charts for an improved monitoring of process mean level. These charting structures have some limitations like assuming normality, the parameters to be known and focusing mainly on phase I monitoring. In many practical situations, nonnormal process behaviors are more frequent. Information about process parameters is not available, and we have to rely on the limited data available from the process to establish the limits in phase I and then use them in phase II monitoring. To have a compromise between the statistical and the practical purposes, a natural desire is to have a control chart that can serve both the concerns efficiently. This study is planned for the same objective focusing the auxiliary-based Shewhart's control charts for location parameter. We have investigated the properties of the design structures of different location charts based on some already used and some new estimators with known and unknown parameters for normal and nonnormally distributed processes. By evaluating the performance of different charting structures in terms of power and run length properties in phase I and phase II, we have identified those more capable of making a good compromise between the abovementioned purposes in terms of statistical efficiency and practical desires. Copyright © 2012 John Wiley & Sons, Ltd.

On the Performance of Different Control Charting Rules

Abstract: In the literature a number of control charting rules are proposed to decide whether a process is in control or out of control. Some issues with these rules will be highlighted in this article. By redefining and listing a set of rules we will evaluate their performance on the X-bar, R, S and S2 charts. Also we will compare the performance of these rules using their power curves to figure out the superior ones. Application of a few of these rules with real data sets will show their detection ability and use for practitioners.

On Effective Dual Use of Auxiliary Information in Variability Control Charts

Abstract: During the last decade, variance control charts based on different sampling schemes have attracted research interest in the field of statistical process control. These charts used extra (auxiliary) information either for ranking of units or estimation rather than using it for both. The effectiveness of a control chart can be increased by utilizing the auxiliary information for dual purposes. This article is focused on developing a generalized structure of variance control charts based on dual use of auxiliary information under different sampling strategies and runs rules. The generalized structure mainly depends on three auxiliary information-based estimators with dual use of auxiliary information, three bivariate process distributions, and variety of sampling schemes. The performance of the proposed control charts is investigated by assessing the power curve. We have observed that the proposals of the study perform better than its complement. An application example is also provided for practitioners' concerns to monitor the stability of physicochemical parameter of groundwater. Copyright © 2015 John Wiley & Sons, Ltd.

Quality Quandaries: On the Application of Different Ranked Set Sampling Schemes

Abstract: Sampling methods play a vital role in many disciplines, including medical sciences, engineering, education, and industrial processes. In order to obtain representative sampling units, we rely on the choice of an adequate sampling mechanism. A commonly u..

Some Recent Developments on the Effects of Parameter Estimation on Control Charts

Abstract: Statistical process control plays a key role in today's highly competitive industrial environment since it allows quality practitioners to timely detect out-of-control situations and take actions whenever necessary in order to ensure that the products or services produced correspond to certain quality standards. Control charts are the tools quality practitioners use, and their monitoring performance is of major importance in practical applications. Since the values of the parameters used for the design of the charts' control limits are usually unknown in practice, the practitioners need to estimate them using an in-control retrospective sample. It has been shown that parameter estimation severely affects the control charts' properties. Many recent studies focused on investigating the impact of parameter estimates on the performance of control charts and on ways of diminishing this impact. This paper aims to provide an up-to-date critical review on the methodologies that have recently been developed in this area. Copyright © 2013 John Wiley & Sons, Ltd.

Monitoring the Coefficient of Variation Using Control Charts with Run Rules

Abstract: Monitoring the coefficient of variation (CV) is a successful approach to Statistical Process Control when the process mean and standard deviation are not constant. In recent years the CV has been investigated by many researchers as the monitored statistic for several control charts. Viewed under this perspective, this paper presents a new efficient method to monitor the CV by means of Run Rules (RR) type charts. Tables are provided to show the statistical run length properties of Shewhart- y , RR2,3 -y , RR3,4 -y and RR4,5 -y control charts for several combinations of in control CV values y0 , sample size n and shift size r. Indeed, comparative studies have been performed to find the best control chart for each combination. An example illustrates the use of these charts on real data gathered from a metal sintering process.

Mixed Cumulative Sum–Exponentially Weighted Moving Average Control Charts: An Efficient Way of Monitoring Process Location

Abstract: Shewhart, exponentially weighted moving average (EWMA), and cumulative sum (CUSUM) charts are famous statistical tools, to handle special causes and to bring the process back in statistical control. Shewhart charts are useful to detect large shifts, whereas EWMA and CUSUM are more sensitive for small to moderate shifts. In this study, we propose a new control chart, named mixed CUSUM-EWMA chart, which is used to monitor the location of a process. The performance of the proposed mixed CUSUM-EWMA control chart is measured through the average run length, extra quadratic loss, relative average run length, and a performance comparison index study. Comparisons are made with some existing charts from the literature. An example with real data is also given for practical considerations. Copyright © 2014 John Wiley & Sons, Ltd.