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Describes a qualitative study on interaction between systems for production control and quality control within industrial organisations. Production control and quality control interact in a sense. Good performance for one aspect often influences or frustrates the performance of the other. As far as is known, the phenomenon of interaction between production control and quality control has not been described extensively in literature. Therefore we decided to form an inductive theory, based on fieldwork. As a result the study had a particularly qualitative character. It was necessary to sharply delineate the concepts of production control and quality control. The authors regard production control and quality control as two separate, but mutually influencing, control systems in the organisation. On the basis of four case studies, a model was developed that describes and explains the interaction phenomenon between production control and quality control. From this interaction model, points of attention can be deduced for the re‐design of production control systems and quality control systems. The model was tested in two other case studies involving consultants in the area of production control.

Overview All organisations are, in one sense or another, involved in operations; an activity implying transformation or transfer. The major portion of the body of knowledge concerning operations relates to production in manufacturing industry but, increasingly, similar problems are to be found confronting managers in service industry. It is only in the last decade or so that new technology, involving, in particular, the computer, has encouraged an integrated view to be taken of the total business. This has led to greater recognition being given to the strategic potential of the operations function. In order to provide greater insight into operations a number of classifications have been proposed. One of these, which places operations into categories termed factory, job shop, mass service and professional service, is examined. The elements of operations management are introduced under the headings of product, plant, process, procedures and people.

The librarian and researcher have to be able to uncover specific articles in their areas of interest. This Bibliography is designed to help. Volume IV, like Volume III, contains features to help the reader to retrieve relevant literature from MCB University Press' considerable output. Each entry within has been indexed according to author(s) and the Fifth Edition of the SCIMP/SCAMP Thesaurus. The latter thus provides a full subject index to facilitate rapid retrieval. Each article or book is assigned its own unique number and this is used in both the subject and author index. This Volume indexes 29 journals indicating the depth, coverage and expansion of MCB's portfolio.

While quality has been widely accepted as essential in today’s global competition, limited work has been conducted on the management processes that lead to it. This paper addresses the control processes in quality assurance and total quality management. The generic management control process is first presented and its deficiencies are highlighted. The paper then proposes control processes for quality assurance and total quality management. Finally, these two processes are compared.

Reviews and evaluates different quality tools that are commonly available in manufacturing and service industries and integrates them into an effective quality control and improvement system based on their characteristics and appropriateness when applying these tools. The study is based on the enhancement of the basic total control methodology (TCM) model which was originally developed and implemented by Motorola Semiconductors Hong Kong Ltd (MSHKL). During the implementation, several opportunities can be identified in terms of classification methods of quality tools, extent of quality tools covered, and interrelationships among the quality tools. Therefore, a study was carried out to enhance the basic model by integrating over 40 quality tools into a three‐tiered model in order to make it more comprehensive in terms of application from a practical point of view. Demonstrates the effectiveness achieved after the implementation of the TCM model.

THE statistical method of controlling product quality during manufacture—a technique commonly known by the shorter title, Quality Control—is a very recent development in the field of production engineering. In point of fact the first application of this technique to machine‐shop production in this country took place late in 1940 at the Croydon works of Messrs. Creed & Co. Ltd., a firm whose name is a household word in electrical communications, for the Creed teleprinter is known all over the world. At the same time it must not be thought that quality control as such, or the use of the so‐called quality control chart as an aid to manufacture, is a purely wartime development. Statistical methods have been employed in British industry, notably in electric lamp manufacture, and in the production of both cotton and woollen textiles, for more than fifteen years, whilst the technique of quality control itself was originated by Dr. W. A. Shewhart of the Bell Telephone Laboratories, New York, in 1922. But where engineering practice was concerned, the application of statistical methods did not present itself until quite recently as a necessary or even convenient solution of manufacturing difficulties.

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 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.

This article presents some discussions on the role of production engineers in quality and reliability planning and control in the context of the manufacturing sector in Singapore. Superior productive efficiency in manufacturing can only be achieved through effective quality control and reliability planning. To be able to compete with other and more advanced countries, Singapore has to emphasise modern manufacturing methods, like automated, computer‐integrated and flexible manufacturing systems. Therefore, concerted efforts are now being taken to train adequately skilled and professional manpower to operate and manage such manufacturing systems. The role of production engineers in operating and managing the modern manufacturing systems has thus become more important and demanding.

THE first part of this article, published in last month's issue of AIRCRAFT ENGINEERING, laid stress on the importance of the technique of quality control to modern production engineering processes, and discussed the basic principles of this technique. The present issue describes the practical applications of quality control and the use of control charts.