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Proposes the use of a β, or difference‐from‐nominal, control chart for short‐run industrial processes providing certain conditions are met. While some manufacturers have used delta charts in the past, only recently have delta charts been discussed in the literature –and then under a variety of different names. This has led to a situation where β charts have been inappropriately applied because the conditions for their use were not clearly understood. Explains the β control chart and the conditions appropriate for its use. In addition, presents a case study of the use of the β chart in a short‐run manufacturing process.

Control charts for statistical quality control have been the subject of academic study for many years. Various analytical approaches to economic control chart design have been advanced, although none has found wide use in practice. Describes a simulation approach to the investigation of control chart economics. Simulation can provide guidance on chart design issues such as sample size, sampling interval and the use of alternative chart alarm rules. Applies the method to the economic comparison between variables control charting and other inspection strategies such as 100 per cent inspection. Presents some generalized results, allowing comparison to be made for various scenarios. Emphasizes the importance of process capability in the choice of quality control strategy and demonstrates the economic advantages of control charting where special or assignable causes exist.

Studies the necessity of controlling the variation of the skewness of the process distribution in order to reduce the product scrap. Proposes a γ control chart for detecting the skewness shift, also implements a simulation procedure to decide the control limits of the γ chart.

The purpose of this paper is to introduce and investigate the performances of a new CUSUM‐S² control chart designed to monitor the sample variance of samples from a normally distributed population.

The proposed chart monitors a statistic computed as a logarithmic transformation of the sample variance; the introduction of the sample variance logarithmic transformation has a twofold effect: to quickly detect the occurrence of an “out‐of‐control” condition; to deal with a quasi‐standard normal statistic.

A design strategy trying to minimize the “out‐of‐control” average run length (ARL) of the chart is presented and the statistical performance of the CUSUM‐S² chart has been assessed through a comparison with an EWMA‐S² control chart proposed in the literature to monitor the process dispersion.

The paper only deals with uncorrelated normally distributed data.

The obtained results show how the CUSUM‐S² chart is particularly suitable when reduction in the process dispersion should be detected by means of subgroups having limited sample sizes.

The paper shows the new CUSUM‐S² control chart allows a decreasing of the variability to be detected faster than the corresponding EWMA‐S² control chart proposed earlier in the literature.

The principal application domain for statistical process control (SPC) charts has been for process control and improvement in manufacturing businesses. However, the number of applications reported in domains outside of conventional production systems has been increasing in recent years. Implementing SPC chart approaches in non‐standard applications gives rise to many potential complications and poses a number of challenges. This paper reviews non‐standard applications of SPC charts reported in the literature from the period 1989 to 2000, inclusive. Non‐standard applications are analysed with respect to application domain, data sources used and control chart techniques employed. Applications are classified into five groups according to the types of problem to which control chart techniques have been applied. For each group the nature of the applications is described and analysed. The review does not show a paradigm shift in the types of SPC control chart applications but does show clearly that the application boundaries extend considerably beyond manufacturing and that the range of problems to which SPC control chart techniques can be applied is much wider than commonly assumed. The paper highlights the critical fundamental and technical issues that need to be addressed when applying SPC chart techniques in a range of non‐standard applications. Wider managerial issues of importance for successful implementations in non‐standard applications of SPC control charts are also discussed.

The purpose of this paper is to develop an exponentially weighted moving average (EWMA) control chart based on the Wilcoxon rank-sum (WRS) statistic using repetitive sampling to improve the sensitivity of the EWMA control chart to process mean shifts regardless of the prior knowledge of the underlying process distribution.

The proposed chart is developed without any distributional assumption of the underlying quality process for monitoring the location parameter. The authors developed formulae as well as algorithms to facilitate the design and implementation of the proposed chart. The performance of the proposed chart is investigated in terms of the average run-length, standard deviation of the run-length (RL), average sample size and percentiles of the RL distribution. Numerical examples are given as illustration of the design and implementation of the proposed chart.

The proposed control chart presents very attractive RL properties and outperforms the existing nonparametric EWMA control chart based on the WRS in the detection of the mean process shifts in many situations. However, the performance of the proposed chart relatively deteriorates for small phase I sample sizes.

This study develops a new control chart for monitoring the process mean using a two-sample test regardless of the nature of the underlying process distribution. The proposed control chart does not require any assumption on the type (or nature) of the process distribution. It requires a small number of subgroups in order to reach stability in the phase II performance.

The concept of the proposed R chart is based on the sum of chi squares (χ²). The average run lengths (ARLs) of the proposed R chart are computed and compared with the ARLs of a standard R chart, Shewhart variance chart proposed by Chang and Gan, a CUSUM range chart (with and without FIR feature) proposed by Chang and Gan and also with an EWMA range chart proposed by Crowder and Hamilton for various chart parameters. This paper aims to show that only FIR CUSUM schemes perform better than the proposed R chart but other CUSUM and EWMA schemes are less efficient than the proposed R chart.

The concept of the proposed R chart is based on the sum of chi squares (χ²). The proposed R chart divides the plot area into three regions, namely: outright rejection region; outright acceptance region; and transition region. The NULL hypothesis is rejected if a point falls beyond the control limit, and accepted if it falls below the warning limit. However, when a point falls beyond the warning limit, but not beyond the control limit, the decision is taken on the basis of individual observations of the previous H samples, which are considered to evaluate statistic U, that is the sum of chi squares. The NULL hypothesis is rejected if U exceeds a predefined value (U*) and accepted otherwise.

The comparisons also show that the CUSUM, EWMA and proposed R charts outperform the Shewhart R chart by a substantial amount. It is concluded that only FIR CUSUM schemes perform better than the proposed R chart, as it is second in ranking. The other CUSUM and EWMA schemes are less efficient than the proposed R chart.

CUSUM and EWMA charts can catch a small shift in the process average but they are not efficient to catch a large shift. Many researchers have also pointed out that these charts' applicability is limited to the chemical industries. Another limitation of CUSUM and EWMA charts is that they can catch the shift only when there is a single and sustained shift in the process average. If the shift is not sustained, then they will not be effective.

Many difficulties related to the operation and design of CUSUM and EWMA control charts are greatly reduced by providing a simple and accurate proposed scheme. The performance characteristics (ARLs) of the proposed charts described in this paper are very much comparable with FIR CUSUM, CUSUM, EWMA and other charts. It can be concluded that, instead of considering many chart parameters used in CUSUM and EWMA charts, it is better to consider a simple and more effective scheme, because a control chart loses its simplicity with multiple parameters. Moreover, practitioners may also experience difficulty in using these charts in production processes.

It is a modification of the Shewhart Range Chart but it is more effective than the Shewhart Range chart, as shown in the research paper.

The purpose of this paper is to evaluate the performance of a modified EWMA control chart (γEWMA control chart), which considers data distribution and incorporate its correlation structure, simulating in-control and out-of-control processes and to select an adequate value for smoothing parameter with these conditions.

This paper is based on a simulation approach using the methodology for evaluating statistical methods proposed by Morris et al. (2019). Data were generated from a simulation considering two factors that associated with data: (1) quality variable distribution skewness as an indicator of quality variable distribution; (2) the autocorrelation structure for type of relationship between the observations and modeled by AR(1). In addition, one factor associated with the process was considered, (1) the shift in the process mean. In the following step, when the chart control is modeled, the fourth factor intervenes. This factor is a smoothing parameter. Finally, three indicators defined from the Run Length are used to evaluate γEWMA control chart performance this factors and their interactions.

Interaction analysis for four factor evidence that the modeling and selection of parameters is different for out-of-control and in-control processes therefore the considerations and parameters selected for each case must be carefully analyzed. For out-of-control processes, it is better to preserve the original features of the distribution (mean and variance) for the calculation of the control limits. It makes sense that highly autocorrelated observations require smaller smoothing parameter since the correlation structure enables the preservation of relevant information in past data.

The γEWMA control chart there has advantages because it gathers, in single chart control: the process and modelling characteristics, and data structure process. Although there are other proposals for modified EWMA, none of them simultaneously analyze the four factors nor their interactions. The proposed γEWMA allows setting the appropriate smoothing parameter when these three factors are considered.

The purpose of this paper is to suggest better methods for monitoring the diagnostic and treatment services for providers of public health and the management of public health services. In particular, the authors examine the construction and use of industrial quality control methods as applied to the public providers, in both the prevention and cure for infectious diseases and the quality of public health care providers in such applications including water quality standards, sewage many others. The authors suggest implementing modern multivariate applications of quality control techniques and/or better methods for univariate quality control common in industrial applications in the public health sector to both control and continuously improve public health services. These methods entitled total quality management (TQM) form the foundation to improve these public services.

The study is designed to indicate the great need for TQM analysis to utilize methods of statistical quality control. All this is done to improve public health services through implementation of quality control and improvement methods as part of the TQM program. Examples of its use indicate that multivariate methods may be the best but other methods are suggested as well.

Multivariate methods provide the best solutions when quality and reliability tests show indications that the variables observed are inter-correlated and correlated over time. Simpler methods are available when the above factors are not present.

Multivariate methods will provide for better interpretation of results, better decisions and smaller risks of both Type I and Type II errors. Smaller risks lead to better decision making and may reduce costs.

Analysts will improve such things as the control of water quality and all aspects of public health when data are collected through experimentation and/or periodic quality management techniques.

Public health will be better monitored and the quality of life will improve for all especially in places where public development is undertaking rapid changes.

The manuscript is original because it uses well known and scientific methods of analyzing data in area where data collection is utilized to improve public health.

The traditional Shewhart control chart, the X-bar and R/S chart, cannot give support to decide when it is not economically feasible to stop the process in order to remove special causes. Therefore, the purpose of this paper is to propose a new control chart design – a modified acceptance control chart, which provides a supportive method for decision making in economic terms, especially when the process has high capability indices.

The authors made a modeling expectation average run length (ARL), which incorporates the probability density function of the sampling distribution of C_pk, to compare and analyze the efficiency of the proposed design.

This study suggested a new procedure to calculate the control limits (CL) of the X-bar chart, which allows economic decisions about the process to be made. By introducing a permissible average variation and defining three regions for statistical CL in the traditional X-bar control chart, a new design is proposed.

A framework is presented to help practitioners in the use of the proposed control chart. Two new parameters (C_p and C_pk) in addition to m and n were introduced in the expected ARL equation. The C_pk is a random variable and its probability function is known. Therefore, by using a preliminary sample of a process under control, the authors can test whether the process is capable or not.