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The purpose of this study is to propose an effective unsupervised one-class-classifier (OCC) support vector machine (SVM)-based single multivariate control chart (OCC-SVM) to simultaneously monitor “location” and “scale” shifts of a manufacturing process.

The step-by-step approach to developing, implementing and fine-tuning the intrinsic parameters of the OCC-SVM chart is demonstrated based on simulation and two real-life case examples.

A comparative study, considering varied known and unknown response distributions, indicates that the OCC-SVM is highly effective in detecting process shifts of samples with individual observations. OCC-SVM chart also shows promising results for samples with a rational subgroup of observations. In addition, the results also indicate that the performance of OCC-SVM is unaffected by the small reference sample size.

The sample responses are considered identically distributed with no significant multivariate autocorrelation between sample observations.

The proposed easy-to-implement chart shows satisfactory performance to detect an out-of-control signal with known or unknown response distributions.

Various multivariate (e.g. parametric or nonparametric) control chart(s) are recommended to monitor the mean (e.g. location) and variance (e.g. scale) of multiple correlated responses in a manufacturing process. However, real-life implementation of a parametric control chart may be complex due to its restrictive response distribution assumptions. There is no evidence of work in the open literature that demonstrates the suitability of an unsupervised OCC-SVM chart to simultaneously monitor “location” and “scale” shifts of multivariate responses. Thus, a new efficient OCC-SVM single chart approach is proposed to address this gap to monitor a multivariate manufacturing process with unknown response distributions.

Presents a new procedure for quality control and quality assurance in scenarios where several variables and attributes have to be monitored. The proposed procedure, named integrated process control, begins with the definition of control stations on the production line, where a single chart that aggregates several variables and attributes is used. This procedure is complemented by using Pareto charts, which determine the quality characteristics contributing the most to the number of defectives. The integrated process control also uses traditional control charts; however, these are used selectively following the indication of the Pareto charts. The joint use of these tools facilitates the identification and solution of quality problems, allowing the improvement actions to be taken at the right time and place. The key advantages of the proposed procedure are: the ability to handle variables and attributes on a single integrated chart, the statistical approach, providing a solid basis for decision making, and the strong managerial appeal provided by the integrated charts.

This article seeks to explain how to monitor the chronic obstructive disease patient and control any complications so that timely treatment can be applied.

Control charts and statistical process control (SPC) theory were used on chronic respiratory patient follow‐up and control. Controlling several variables simultaneously, using univariate charts, can be misleading, more so when there are correlated variables, so multivariate and univariate control charts were studied.

One‐sided control charts are preferable when the aim is to detect changes in the mean solely in one direction. Thus, one‐sided, univariate and multivariate charts were built, which identified previously undetected out‐of‐control events.

The study's main limitation is its retrospective nature. However, following‐up individual patients can highlight medical therapy effects.

The article concludes that control charts, in particular one‐sided ones, are a valuable tool for monitoring chronic respiratory patients, thus contributing to medical decision making.

The article highlights control chart application to chronic respiratory patient follow‐up, permitting a global view of patient evolution over time.

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.

Explains that, as global competition changes the expanding service sector, quality will play an increasingly pivotal role in both attracting and retaining service customers. Reveals that research into service quality has addressed two important dimensions: promoting quality through the design of improved service systems; and searching for reliable instruments for measuring service quality. Proposes a procedure to help the service provider interpret service quality data to improve the service delivery system. Additionally, evaluates a two‐step control chart procedure for evaluating service operations using SERVQUAL type instruments.

One of the major obstacles contributing to the cost, time and efficiency of improving the quality output of manufacturing systems is the propagation of defectives or errors through the system. Conventional individual control chart design does not address the problem of the interrelation of the processes adequately. Owing to the increasing complexity of manufacturing systems as well as the problems caused by the natural variability of the systems, trial‐and‐error methods are the most commonly used technique for the implementation of the control charts. Trial‐and‐error methods are very costly, time consuming and highly disruptive to the real system. Hence, a systematic and holistic computer‐based methodology is proposed in this paper to obtain a control chart configuration which improves productivity and quality, and reduces cost. Simulation is used as a platform to conduct the control chart system design because different scenarios can be tested off‐line so that statistical process control can be performed effectively without making costly mistakes and disturbing the real system.

The search of the optimal economic design of the Bayesian adaptive control charts for finite production runs can be a long and tedious procedure due to the intrinsic structure of the optimization problem, which requires a dynamic programming approach to select the best decision at each sampling epoch during the production horizon of the process. This paper aims to propose a new efficient procedure implementing a genetic algorithm neighbourhood search scheme embedded within the dynamic programming procedure with the aim of reducing the computational burden and achieving significant cost savings in the chart implementation.

The efficiency of the developed procedure has been verified through a comparison with another existing exhaustive approach working exclusively on one‐sided X¯ Bayesian control charts; then, it has been extended to the design of two‐sided Bayesian control charts.

The proposed procedure implementing the genetic algorithm neighbourhood search is very fast and efficient in detecting optimal solutions: it allows significant quality control cost savings to be achieved during the Bayesian charts implementation thanks to the possibility of investigating larger spaces of decisions than the existing optimization procedures.

With reference to discrete part manufacturing, where the assumption of finite production runs is often realistic, the design and implementation of adaptive Bayesian control charts by means of the proposed procedure allows significant cost savings to be achieved with respect to the fixed parameters Shewhart charts.

The exhaustive optimization procedure cannot be executed in a reasonable computational time when the space of decisions to select Bayesian chart design parameters significantly enlarges, which is the case of two‐sided control charts. The paper documents the proposed procedure which overcomes this problem and allows the two‐sided Bayesian chart to be designed and proposed as an efficient means to monitor short production runs.

The purpose of this paper is to evaluate and improve the quality and the reliability of pre‐treatment quality controls of an efficient technique of radiotherapy called IMRT (intensity‐modulated radiation therapy). The aim is then to determine if the controls can be safely reduced while keeping an optimal level of quality.

The statistical process control method (SPC) was applied to quality assurance in IMRT. In order to characterize prostate and head‐and‐neck treatment process variability, individual value control charts and moving‐range control charts were established.

Control charts showed that prostate and head‐and‐neck treatment processes are only subject to random causes of variability, which means they are statistically controlled. It was proved that both processes are statistically stable and capable.

The paper shows that SPC is an efficient method to objectively determine if quality controls can be reduced.

The purpose of this paper is to investigate the properties of the classical goodness of fit test statistics X², G², GM², and NM² in testing quality of process represented as the trinomial distribution with dip null hypothesis and to devise a control chart for the trinomial distribution with dip null hypothesis based on demerit control chart.

The research involves the linear form of the test statistics, the linear function of counts since the marginal distribution of the counts in any category is binomial or approximated Poisson, in which the uniformly minimum variance unbiased estimator is the linear function of counts. A control chart is used for monitoring student characteristics in Thailand. The control chart statistics based on an average of the demerit value computed for each student as a weighted average lead to a uniformly most powerful unbiased test marginally. The two‐sided control limits were obtained using percentile estimates of the empirical distribution of the averages of the demerit.

The demerit control chart of the weight set (1, 25, 50) shows a generally good performance, robust to direction of out‐of‐control, mostly outperforms the GM² and is recommended. The X², NM² are not recommended in view of inconsistency and bias. The performance of the demerit control chart of the weight set (1, 25, 50) does not dramatically change between both directions.

None of the multivariate control charts for counts presented in the literature deals with trinomial distribution representing the practical index of the quality of the production/process in which the classification of production outputs into three categories of “good”, “defective”, and “reworked” is common. The demerit‐based control chart presented here can be applied directly to this situation.

The research considers how to deal with the trinomial distribution with dip null hypothesis which no research study so far has presented. The study shows that the classical Pearson's X², Loglikelihood, modified Loglikelihood, and Neyman modified X² could fail to detect an “out‐of‐control”. This research provides an alternative control chart methodology based on demerit value with recommended weight set (1, 25, 50) for use in general.

This study aims to apply statistical process control (SPC) techniques to improve the quality and efficiency of the processes in a restaurant.

SPC tools such as check sheet, cause-and-effect analysis, Pareto chart, control charts and SERVQUAL methodology is adapted to measure and improve the quality of the system.

At the end, some suggestions for improving the quality of service system are proposed in this study to complete the research.

The most difficult part of this study was data collection. Because of the situation of the restaurant, the number of customers does not exceed 60 every day. Another limitation of this study is that the samples have been collected from the same population each day, and it may affect the final result.

The research is based on the present service system at a restaurant, located at a university campus in Cyprus.

A similar study can be applied in the social sector to evaluate and improve service quality.

In this paper, for the first time, SPC and SERVQUAL are used to evaluate and improve quality in the service sector.