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The purpose of this paper is to provide a new approach for detecting the small sustained process shifts in multistage systems with correlated multiple quality characteristics.

The authors propose a new multivariate linear regression model for a multistage manufacturing system with multivariate quality characteristics in which both the auto-correlated process outputs and the correlations occurring between neighboring stages are considered. Then, the multistage multivariate residual control charts are constructed to monitor the overall process quality of multistage systems with multiple quality characteristics. Moreover, an overall run length concept is adopted to evaluate the performances of the authors’ proposed control charts.

In the numerical example with cascade data, the authors show that the detecting abilities of the proposed multistage residual MEWMA and MCUSUM control charts outperform those of Phase II MEWMA and MCUSUM control charts. It further demonstrates the usefulness of the authors’ proposed control charts in the Phase II monitoring.

The research results of this paper can be applied to any multistage manufacturing or service system with multivariate quality characteristics. This new approach provides quality practitioners a better decision making tool for detecting the small sustained process shifts in multistage systems.

Once the multistage multivariate residual control charts are constructed, one can employ them in monitoring and controlling the process quality of multistage systems with multiple characteristics. This approach can lead to the direction of continuous improvement for any product or service within a company.

In order to meet the requirements of 6σ management and to overcome the deficiencies of the theory for using the pre-control chart to evaluate and monitor quality stability, the purpose of this paper is to probe into the quality stability evaluation and monitoring guidelines of small batch production process based on the pre-control chart under the conditions of the distribution center and specifications center non-coincidence (0<ɛ≤1.5σ), the process capability index C _p ≥2 and the virtual alarm probability α=0.27 percent.

First, the range of the quality stability evaluation sampling number in initial production process is determined by using probability and statistics methods, the sample size for the quality stability evaluation is adjusted and determined in initial production process according to the error judgment probability theory, and the guideline for quality stability evaluation has been proposed in initial production process based on the theory of small probability events. Second, the alternative guidelines for quality stability monitoring and control in formal production process are proposed by using combination theory, the alternative guidelines are initially selected based on the theory of small probability events, a comparative analysis of the guidelines is made according to the average run lengths values, and the monitoring and control guidelines for quality stability are determined in formal production process.

The results obtained from research indicate that when the virtual alarm probability α=0.27 percent, the shifts ɛ in the range 0<ɛ≤1.5σ and the process capability index C _p ≥2, the quality stability evaluation sample size of the initial production process is 11, whose scondition is that the number of the samples falling into the yellow zone is 1 at maximum. The quality stability evaluation sample size of the formal production process is 5, and when the number of the samples falling into the yellow zone is ≤1, the process is stable, while when two of the five samples falling into the yellow, then one more sample needs to be added, and only if this sample falls into the green zone, the process is stable.

Research results can overcome the unsatisfactory 6σ management assumptions and requirements and the oversize virtual alarm probability α of the past pre-control charts, as well as the shortage only adaptable to the pre-control chart when the shifts ɛ=0. And at the same time, the difficult problem hard to adopt the conventional control charts to carry out process control because of a fewer sample sizes is solved.

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.

Total quality management programmes emphasizing continuous improvement have become increasingly popular as companies perceive the importance of quality in maintaining or enhancing their competitive position. Although improvement programmes are extremely valuable, they do not eliminate the need for control. Unfortunately, companies striving for quality improvement may de‐emphasize, or even abandon, the use of process control charts, believing that process control cannot be applied during periods of change. There is a need for a procedure formally linking the methodology of statistical process control – which assumes process stability – to the management of quality improvement programmes – which assumes constant positive change. Provides a procedure to develop and use process goal charts. The procedure uses a straightforward adaptation of the standard process control chart methodology to support and control the planned change of a continuous improvement programme. The procedure is illustrated with a simple example and possible extensions of the procedure are suggested.

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.

The second part of this article, published in last month's issue of AIRCRAFT ENGINEERING, described some of the practical applications of the quality control method to machine shop production and, in particular, the use of control charts based on measurement. The present issue deals with a method of quality control based on the use of limit gauges, and with the applications of the control chart for “number defective” appropriate to this method. A further article, to be published in a subsequent issue, will discuss the extension, of the quality control idea to the sampling inspection of components and similar products in bulk, as an economic alternative to the detail (100 per cent) inspection which hitherto has been customary in such cases.

The purpose of this paper is to develop an innovative and quite new Six Sigma quality control (SSQC) chart for the benefit of Six Sigma practitioners. A step-by-step procedure for the construction of the chart is also given.

Under the assumption of normality, in this paper, the construction of SSQC chart is proposed in which the population mean and standard deviation are drawn from the process specification from the perspective of Six Sigma quality (SSQ). In this chart, the concept of target range is used to restrict the shift in the process within plus or minus 1.5 times of standard deviation. This control chart is useful in monitoring the process to ensure that the process is well maintained within the specification limits with minimum variation (shift).

A step-by-step procedure is given for the construction of the proposed SSQC chart. It can be easily understood and its application is also simple for Six Sigma practitioners. The proposed chart suggests for timely improvements in process mean and variation. The illustrative example shows the improved performance of the proposed new procedure.

The proposed approach assumes a normal population described by the known specification of the process/product characteristics though it may not be in all cases. This may call for a thorough study of the population before applying the chart.

The proposed SSQC chart is an innovative approach and is quite new for the practitioners. The paper assumes that the population standard deviation is known and is drawn from the specification of the process/product characteristics. The proposed chart helps in fine-tuning the process mean and bringing the process standard deviation to the satisfactory level from the perspective of SSQ.

The paper is the first of its kind. It is innovative and quite new to the Six Sigma practitioners who will find its application interesting.

The purpose of this paper is to propose a framework for the analysis of students’ ratings of teaching quality in higher education and the disclosure of risky issues undermining the quality of teaching and courses that require attention for continuous improvement. The framework integrates two decision-based methods: the standardized u-control chart and the ABC analysis using fuzzy weights. The control chart, using the students’ ratings, allows the identification of those courses requiring an improvement of teaching quality in the short-medium term. While the ABC analysis uses fuzzy weights to deal with the vagueness and uncertainty of students’ teaching evaluations and provides a risk map of the potential areas of teaching performances improvement in the long term. The proposed framework allows the identification of teaching and course quality aspects that need corrective actions in response to students’ criticisms in accordance with different levels of priority.

This study adopts two methods, commonly used in industrial applications, i.e. the u-control chart and ABC analysis. Combining the results of a literature review on teaching evaluation and the application of these two methods as building blocks for the assessment, a framework to detect potential risks reducing teaching quality in higher education is proposed. The application of the framework is shown through an action-based case study developed in an Italian public university.

The study proposes a framework that combines two methods, i.e. u-control chart and ABC analysis with fuzzy weights, to support the assessment of teaching and course quality. The framework is proposed as an assessment approach of the teaching performance in higher education with the purpose to continuously improve the quality of teaching and courses both in the short, medium and long term.

The study provides an original contribution to the understanding of how to analyze students’ evaluation of teaching performance in order to take proper and timely decisions on corrective actions in response to the need of continuously improving the level of teaching and course quality.

The purpose of this study is to develop a joint production, maintenance and quality control strategy involving a periodic preventive maintenance policy.

The proposed integrated policy is defined and modeled mathematically.

The paper focuses on finding simultaneously the optimal values of the preventive maintenance period, the buffer stock size, the sample size, the sampling interval and the control chart limits, such that the expected total cost per time unit is minimized.

The paper attempts to integrate in a single model the three main aspects of any manufacturing system: production, maintenance and quality. The considered system consists of one machine subject to a degradation process that directly affects the quality of products. The process and product quality control is carried out using an “x-bar” control chart. In the proposed model, a preventive maintenance action is performed every α inspections of product quality in order to reduce the shift rate to the “out-of-control” state. A corrective maintenance action is undertaken once the control limits are exceeded. In order to palliate perturbations caused by the stopping of the machine to undergo maintenance actions, a buffer stock is built up to ensure the continuous supply of the subsequent machine. The main goal of this work is to develop a model that captures the underlying link between the preventive maintenance policy, the buffer stock size and the parameters of an “x-bar” control chart used to control the quality of the product. Numerical experiments and a study of the effects of the input parameters variation on the obtained results are performed.

The existing models that simultaneously consider maintenance, inventory and control charts consist of a condition-based maintenance (CBM) policy. Periodic preventive maintenance (PM) has not been considered in such models. The proposed integrated model is original, in that it links production through buffer stocks, quality through a control chart and maintenance through periodic preventive maintenance (different practical settings and modeling approach than when CBM is used). Hence, this paper addresses practical situations where, for economic or technical reasons, only systematic periodic preventive maintenance is possible.

Traditional statistical tools are subject to certain constraints when they are applied to quality control in industries where the number of faults per working day is limited. An effective quality monitoring and analyzing tool is therefore needed to meet the specific requirements of these industrial sectors. Proposes a so‐called “Cause‐classified Control Chart”, based on fieldwork in the Nanchang Telecommunications Office of China. Trial results from several posts and telecommunications offices in China in recent years have positively shown that the Cause‐Classified Control Chart is an effective tool for quality enhancement in these specific industrial sectors.