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In the present article, we propose a generally weighted moving average control chart based on a three-parameter logarithmic transformation for monitoring shifts in the process variability (regarded...

The development and application of the traditional univariate and multivariate quality control charts are distribution-based following the assumption that the process outputs come from some specifi...

Recently, a MIX S2 control chart using both attribute and variable inspections is proposed by Ho and Quinino (2016) to monitor the process variability. This paper considers the variable sampling in...

Resumen Uno de los aspectos más significativos para mejorar la calidad de los procesos es evitar el aumento de la variabilidad, por ello es importante el monitoreo continuo de la característica de calidad, que permita conocer su estado y comportamiento en el tiempo. Una de las principales herramientas son las cartas de control. Hawkins plantea un procedimiento para monitorear la variabilidad del proceso mediante cartas de control CUSUM. En este artículo se presenta su aplicación en la medida de variabilidad de los valores de pH en el uso de fungicidas; así, como los resultados sobre su desempeño en comparación con la carta de control de Shewhart, la cual no es tan efectiva para detectar corrimientos pequeños. Abstract One of the most significant aspects to improve the quality of the processes is to avoid the increase of the variability, so it is important the continuous monitoring of the quality feature, which allows to know its condition and behavior over time. One of the main tools is Control Charts. Hawkins creates a method for monitoring process variability using the CUSUM Control Charts. This paper shows its application in the measure of variability of the pH values in the use of fungicides; as well as the results on their performance compared to the Shewhart Control Chart, which is not as effective at detecting small shifts.

Control charts are effective tools for signal detection for both manufacturing and service processes. Much of the data in service industries come from processes exhibiting non-normal or unknown dis...

Water is one of the most important elements for human life, water treatment is done for human consumption and must fulfill the health requirements with the levels of certain parameters. Quality of Water Treatment II is the second water purification installation owned by PDAM Tirta Moedal Semarang City with production capacity of 60 l/s. Variables used in the water treatment process are correlated with each other, so used multivariate control chart. The Multivariate Exponentially Weighted Moving Average control chart is used for monitoring process mean, and the Multivariate Exponentially Weighted Moving Variance control chart is used for monitoring process variability. The variables used are colour, turbidity, organic substance, manganese and the total dissolved solid. MEWMA control chart with λ = 0.5, showed that the process mean is controlled statistically. MEWMV control chart showed that variability is controlled statistically in λ = 0.4, ω = 0.2 and L = 3.3213. MEWMA and MEWMV control chart showed that the process is not capable because it obtained the value of process capability index less than 1. Keywords: Water, Multivariate Exponentially Weighted Moving Average, Multivariate Exponentially Weighted Moving Variance, process capability.

In this article, we propose new cumulative sum (CUSUM) control charts using the ordered ranked set sampling (RSS) and ordered double RSS schemes, with the perfect and imperfect rankings, for monitoring the variability of a normally distributed process. The run length characteristics of the proposed CUSUM charts are computed using the Monte Carlo simulations. The proposed CUSUM charts are compared in terms of the average and standard deviation of run lengths with their existing competitor CUSUM charts based on simple random sampling. It turns out that the proposed CUSUM charts with the perfect and imperfect rankings are more sensitive than the existing CUSUM charts based on the sample range and standard deviation. A similar trend is present when these CUSUM charts are compared with the fast initial response features. An example is also used to demonstrate the implementation and working of the proposed CUSUM charts.

The monitoring of process variability is an important feature to get optimal output from a process. Control charts are vital tools used for efficient process monitoring. The commonly used types of charts include Shewhart, cumulative sum and exponentially weighted moving average (EWMA) control charts. This study focuses on dispersion control charts using some efficient transformation for small and medium instabilities. We intend to propose an assorted method to monitor a range of disturbances in process dispersion, using the well-known max approach. We have used several measures to evaluate the suggested assorted control chart. Based on these measures, we have compared the proposed assorted method with many existing charts. The study proposal outperforms the existing counterparts in detecting various amounts of shifts in process dispersion. Finally, a real-life application of the proposed chart is demonstrated to monitor the flow width measurements in a hard-bake process. The work was partially sponsored by Deanship of Scientific Research (DSR), King Fahd University of Petroleum and Minerals, Dhahran 31261, through project # IN171007. Authors Nasir Abbas, Usman Saeed and Muhammad Riaz are thankful to King Fahd University of Petroleum and Minerals for providing excellent research facilities. Scopus

The S2 (or S2) chart is one of the most useful tools in Operations Management (OM) for monitoring process variability. Previous studies on this chart with estimated variance did not compare their one- and two-sided designs. We make this comparison, revealing the large performance difference between them and providing new insights for the users.

A multivariate dispersion control chart monitors changes in the process variability of multiple correlated quality characteristics. In this article, we investigate and compare the performance of charts designed to monitor variability based on individual and grouped multivariate observations. We compare one of the most well-known methods for monitoring individual observations -- a multivariate EWMA chart proposed by Huwang et al -- to various charts based on grouped observations. In addition, we compare charts based on monitoring with overlapping and nonoverlapping subgroups. We recommend using charts based on overlapping subgroups when monitoring with subgroup data. The effect of subgroup size is also investigated. Steady-state average time to signal is used as performance measure. We show that monitoring methods based on individual observations are the quickest in detecting sustained shifts in the process variability. We use a simulation study to obtain our results and illustrated these with a case study.

This research aims to propose a nonparametric double exponentially weighted moving average (NDEWMA) control chart for detecting shifts in a process using the Mood statistic. A Monte Carlo simulation study was used. The data had a normal distribution and a non-normal distribution where the magnitude of shift size (δ) was δ = 1, 1.05, 1.10 - 2.00 and 3.00, respectively. A performance comparison of the control charts was evaluated by using the Average Run Length (ARL). In the case of an in-control process, a large ARL value is desired, while a small ARL value is desired when the process is out-of-control. From the results of the simulation study, it was observed that the proposed NDEWMA control chart is effective in detecting small shifts in the process and gives better performance compared to the nonparametric exponentially weighted moving average control (NEWMA) control chart based on Mood statistics.

A two-sided run sum S control chart is proposed and its average run length performance is evaluated via a Markov chain technique. The performance of the chart is compared to several well-known control-charting procedures for the monitoring of process variability. One-sided counterparts of the proposed run sum charts are also discussed. The numerical results demonstrate an improved performance of the run sum S control charts as compared with control charts with runs rules, especially in the detection of increasing shifts in process variability. A practical guidance for the selection of the appropriate charting procedure is also given.

Control charts are important statistical tool used to monitor fluctuations in the process location and dispersion parameters. The issues relating to the appropriate choice of control charts for the effective detection of process variability are addressed, and different control chart structures, such as Shewhart-type, exponentially weighted moving average and cumulative sum are explored under ideal assumption of normality and contaminated normal environments, and hence, those control charts structures are identified which are more capable to detect aberrant changes in the process dispersion. Copyright © 2015 John Wiley & Sons, Ltd.

For certain types of medicine the biological strength or bioactivity of a drug is the main characteristic for release of products to the market. A pharmaceutical company may decide to use their own in-house reference standard to test the drug instead of using the expensive international reference standard. The company is then legally obliged to verify that the in-house reference standard remains stable over time with respect to the international one. This is a special problem within statistical process control (SPC) since the monitoring period is relatively short and bioassays are typically heterogenous. The objective of this article is to apply methods from SPC and dose-finding studies to different study designs and assess how well these methods perform in detecting a decline in bioactivity. The included methods are the exponentially weighted moving average (EWMA), Shewhart chart, and analysis of variance (ANOVA)-type contrasts (linear, Helmert, and reverse-Helmert). An optimal a-spending function was selected first to avoid inflating the familywise error rate. The normal a-spending function seemed to perform generally the best. Then from the results, the linear, reverse-Helmert, and the EWMA (¿ = 0.6) resulted in high power when a change occurred at earlier time points, while Helmert and the EWMA (¿ > 0.6) performed better in later declines. Linear contrasts and the Shewhart chart performed better irrespective of the decline profile. Having more bioassay runs at the beginning of the stability study increased the probability of detecting a decline. If there is no prior information on the expected deterioration profile, linear contrasts or Shewhart chart should be preferred. Otherwise, reverse-Helmert or Helmert contrasts should be chosen for either early or late deterioration, respectively. Keywords: Bioassay, Dose-finding, Familywise error, Quality control, Sequential tests, Stability

When the production run is short and process parameters change frequently, it is difficult to monitor the process using traditional control charts. In such a case, the coefficient of variation (CV) is very useful for monitoring the process variability. The CV control chart, however, is not sensitive at small shifts in the magnitude of CV. This study suggest the CV-GWMA(generally weighted moving average) control chart, combining the GWMA technique, which shows better performance than the EWMA(exponentially weighted moving average) or DEWMA(double exponentially weighted moving average) technique in detecting small shifts of the process. Through a performance evaluation, the proposed control chart showed more excellent performance than the existing CV-EWMA control chart or the CV-DEWMA control chart in detecting small shifts in CV.

In this paper, monitoring procedure for process variability in multivariate setting based on individual observations which is a combination of (i) Hotelling’s T2 control chart in detecting out of control signal and (ii) implementation of Mason, Young and Tracy (MYT) decomposition and structure analysis technique for root cause analysis is introduced. The advantages of this procedure will be shown by using the case of a paper box production process in one of the Malaysian manufacturing companies. The successful application of this multivariate approach could act as a stimulant for most industries to imitate in process monitoring. Moreover, the computation efficiency in root cause analysis enables quality’s multiple characteristics to be monitored simultaneously. Based on the findings, the core issue that needs to be a matter of concern by the management team is the closure tap of the box. This process variation should be solved immediately to avoid the products’ quality from further deteriorating.

When the averages control chart is applied to monitor a manufacturing process, three parameters should be determined, sample size, sampling interval between successive samples and the control limit for the chart. This study shows how the effect of process variability caused by common and assignable causes on the values of averages control chart parameters. An example is presented and then based on this example, sensitivity analysis is performed to show the direction of control chart parameters changes in the presence of changes in the magnitude and frequency of process shift and the costs of discovering and correcting the causes of these shifts

[This abstract is based on the author's abstract.]Two modified semicircle charts are proposed for detecting a reduction in process variation that leads to process improvement. Each chart has two limits defined by the inner and outer semicircles. It is s..

The standard way of using the determinant of the average of sample covariance matrices for multivariate process variability control charting leads to a biased estimate of the control limits. In this paper, we present an improved control chart having unbiased control limits. It has smaller ARL than the standard chart and, hence, it is more sensitive to shifts in the process variability. An example is given to illustrate these advantages.

A new control chart, which employs the exponentially weighted moving average (EWMA) technique, is proposed. The statistic for the chart defines the area below a straight line as the control region, which makes the charting procedure easier than the usual approach. This chart can effectively monitor the process mean and the increased process variability simultaneously, and can detect the source and the direction of a change easily.

This paper introduces a new multivariate exponentially weighted moving average (EWMA) control chart. The proposed control chart, called an EWMA V-chart, is designed to detect small changes in the variability of correlated multivariate quality characteristics. Through examples and simulations, it is demonstrated that the EWMA V-chart is superior to the |S|-chart in detecting small changes in process variability. Furthermore, a counterpart of the EWMA V-chart for monitoring process mean, called the EWMA M-chart is proposed. In detecting small changes in process variability, the combination of EWMA M-chart and EWMA V-chart is a better alternative to the combination of MEWMA control chart (Lowry et al. , 1992) and |S|-chart. Furthermore, the EWMA M- chart and V-chart can be plotted in one single figure. As for monitoring both process mean and process variability, the combined MEWMA and EWMA V-charts provide the best control procedure.

Statistical process control (SPC) is an approach to process control that has been widely used in any industrial or non-industrial fields. SPC is based on so called Shewhart's conception of the process variability. This conception distinguishes variability caused by obviously effected common causes from variability caused by abnormal assignable causes. New manufacturing technologies and concepts strongly contributed to the real attainment of a high level of the processes capability. In such conditions a distinction of common variability causes from assignable causes is more and more difficult. Conventional attribute control charts are not adequate solution for monitoring and control of the processes with very low level of defects (in terms of ppm). The answer can be found in the application of CCC and CQC control charts. The paper deals with the presentation of the SW application which was created to contribute to the practical deployment of these effective control charting methods in practice.

In this paper we analyze several control charts suitable for monitoring process dispersion when subgrouping is not possible or not desirable. We compare the performances of a moving range chart, a ...

A Shewhart R control chart is developed to monitor the process variance for large production runs. It is not appropriate to monitor the process variance using the Shewhart R control chart for short production runs. In this paper, we propose the design of the R control chart for short production runs. For various combinations of the number of samples and the size of each sample, control coefficients of the R control chart are calculated by numerical methods. Using these results, the R control chart for short production runs is developed to monitor the process variance correctly. Finally, the design procedure of the R control chart for short production runs and the results of misusing the Shewhart R control chart are illustrated by an example.

Traditional X and R control charts are used widely in industry, but do not respond quickly to small or moderate changes in the process output. Confirmation sample control (CSC) charts to detect changes in the process mean and process variability are proposed. These new control charts have substantially better operating characteristics than X and R charts. CSC charts require that any unusual observed sample be confirmed through an independent confirmation sample taken from the process. This makes CSC charts appealing to production personnel since the charts require the verification of bad news. The implementation of CSC charts is illustrated, and figures are given that allow the determination of appropriate design parameters.

Identification of the assignable causes of process variability and the restriction and elimination of their influence are the main goals of statistical process control (SPC). Identification of these causes is associated with so called tests for special causes or runs tests. From the time of the formulation of the first set of such rules (Western Electric rules) several different sets have been created (Nelson rules, Boeing AQS rules, Trietsch rules). This paper deals with the comparison analysis of these sets of rules, their basic statistical properties and the mistakes accompanying their application using SW support. At the end of this paper some recommendations for the correct application of the runs tests are formulated.

Cumulative sum (CUSUM) control charts have been widely used for monitoring the process mean. Relatively little attention has been given to the use of CUSUM charts for monitoring the process varianc...

Since the 1950s, various models have been proposed for a number of Shewhart-type charts including X and R charts employed jointly. Apart from such works, however, the design of a procedure to monitor and control process variability appears to have commanded very little attention. In 1989, Collani and Sheil first discussed the economic design of the s chart. However, they did not give a procedure to determine the optimal parameters of the s chart. This paper presents a procedure to economically determine the optimal parameters of the s chart. The results and the execution times of all numerical examples show that this procedure is quite accurate and efficient.

Wang and Raz presented a generic approach for constructing attributes control charts using linguistic data to control the process average. In this paper, we propose new linguistic control charts for process average and process variability based on the estimation of probability distribution existing behind the linguistic data.

Many standard texts on SPC offer simple procedures for setting up control charts. Although the procedures work well in some situations, there are many instances where the procedures do not produce useful charts. The failure to sustain SPC initiatives is often due to an over simplistic model of process variability. This paper examines the widely used procedures for setting up control charts and illustrates how these may fail when the process variability has certain characteristics. A more robust alternative procedure is examined in some detail. This involves taking a closer look at process variability and incorporating this with control chart design.

In recent years, the quality practitioners have concentrated on exploring the effect of measurement errors on the performance of control charts. To the best of our knowledge, the effect of measurement errors on simultaneous monitoring of the process mean and the process variability is neglected in the literature. In this paper, the effect of measurement errors on detecting and diagnosing performance of one of the most common simultaneous monitoring approaches in the literature is investigated. Two approaches namely multiple measurement approach as well as increasing sample size are suggested for compensating for the effect of measurement errors. The results of simulation study show that the measurement errors can adversely affect the detecting performance of maximum exponentially weighted moving average and mean squared deviation (MAX-EWMAMS) control chart while the effect of measurement errors on diagnosing performance of this control chart is negligible. The results also represent that both tasking multiple measurement on each sample point and increasing sample size can adequately compensate for the measurement errors effect.

Exponentially weighted moving average (EWMA) control charts are regarded as one of the most convenient tools in detecting small process shifts. Although EWMA control charts have been extensively used to monitor the mean of quality characteristics, there are few studies concentrating on the monitoring of process variability by using weighted moving control charts. In this paper, we propose an exponentially weighted moving sample variance (EWMSV) control chart for monitoring process variability when the sample size is equal to 1. The results are compared numerically with other similar methods using the average run length (ARL). Through an example, the practical considerations are presented to implement EWMSV control charts.

Efforts aimed at improving or monitoring quality must be based on measurement systems that are accurate and precise. The accuracy and precision of measurement systems can be controlled by the use of statistical methods, in particular by the use of control charts. A procedure has been proposed that involves determining the stability and precision of a measurement system. Comparison of the precision with overall process variability allows the processor to decide whether the measurement is adequate for monitoring the process.

The design of statistical procedures to monitor and control the variability of production processed has received comparatively little attention in the literature. This paper considers the use of a standard deviation Control Chart for this purpose.The d..

Graphical procedures for analyzing the results of nested sampling studies are presented and illustrated with examples. It is shown that a standard deviation control chart analysis will detect atypical results and nonhomogeneous variances that can greatl..

Factors affecting process variability have already been discussed (Chapter 2) and, as this variability may become large, measurement and/or control of workpiece size is usually necessary for products requiring high quality and consistency.