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This paper mainly studies to build control charts under different alarm rule. For different alarm rule, the control limit parameters of a control chart should be changed, then some kinds of control schemes under different alarm rule were compared and the methods of calculating ARL for different control schemes wee given.

In this paper, the same distribution principle is developed based on the studies with many years for short run processes. The quality control philosophy and background of the principle are introduced. Some implementing techniques for the principle are presented, and application cases both in industry and evaluation & decision‐making areas are shown.

Quality science may be defined as the subject promoting both quality and productivity, which may include quality management, quality engineering and technology innovation. The research and application of quality science have been in existence for many years in China. This report first reviews its recent history as well as the analysis of the status quo. Finally, a prospectus and comments section for the future of quality science points out the development trends and its significant effectiveness on China’s modern economics.

In this paper, a two‐section method for measuring is introduced and the variation soruces of measurement process are analysed. Measuring is a special process. Various variation source must be firstly decomposed so that the statistical distribution law of measuring process can be established, and then implement monitoring control of the measuring process. A special method to obtain the m easuring variation is discussed, and a monitoring control technique for measuring process is studied based statistical distribution. Towards the end, we briefly introduce software design for the analysis and control of a measurement process.

With the development of the American Customer satisfaction index (ACSI), research on customer satisfaction measurement or evaluation methods have become significant in the last decade. Most of international customer satisfaction barometers or indices are evolved based on the cause and effect relationship model of ACSI. Of critical importance to validity of customer satisfaction indices is how to construct a measurement attribute or indicator model and provide an effective implementation method effectively. Quality Funcion Deployment (QFD) is a very useful tool for translating the customer voice into product design through quality engineering. In fact, this is a methodology for measuring and analyzing evaluation indicators by their relationship matrix. In this paper, we will make an effort to integrate the framework of QFD into teh measurement problem of customer satisfaction, and also develop a new multi‐phase QFD model for evaluation of Customer Satisfaction Index (CSI). From the houses of quality in this model, the evaluation indicators impacting on customer’s global satisfaction are identified by means of their relationship matrix. Then the evaluation indicator hierarchy and its measurement method for the customer satisfaction index are presented graphically. Furthermore, survey data from the Chinese autombobile maintenance sector and a relevant case study are utilized to show the implementation method of the QFD model used to measure and analyze of customer satisfaction.

The economic design of specification limits must be determined on an economic basis where we minimize total loss to society, which consists of both the producer and the consumer. Economic specification limits have been developed based on the assumption that the quality characteristic is normally distributed. Unfortunately, the assumption is not to meet some practical cases. In this paper, some non‐normal distributions are considered for quality characteristic with geometrical features. An economic model for selecting the optimum specification limits on the basis of minimizing total cost is introduced. A case study is presented to illustrate the application in practice.

As we know, the quality of processes is technically depicted by variation, a product or process with the best quality must naturally require the variation as less as possible. The variation is usually reduced with many ways, say, by adjusting parameters settings under robust design with many turns expensive experiements. So ones are trying to reach the robusiness by detecting cheap and simple methods. In this paper, a both practical and simple technique for quality improvement, namely reducing the variation, by data classification is studied. First, all possible system factors are included, which may dominate the variation law. And then we make use of the past observations and their classification as well as boxplot charts to find out the internal rule between the variation and the system factor. Next, adjust the location of the system factor according to the rule so that the variation could, to some extent, be lessened. Finally, two typical quality improvement cases based on data classification are presented.

The implementation of management systems such as the quality, environmental and occupational health and safety management systems to the national and/or international standards I 9000, ISO 14000 and AS/NZS 4801 has been well researched and studied in the past. However, since the release of the completely revised quality system standard ISO 9001 in December 2000, there has been only few publications and studies about the integration of these three management systems. This paper discusses not only the methods that can be used for ingtegration of management systems but also gives practical examples of its impelementation and benefits to the organisations. It is not the intention of this paper to investigate the similarities and differences of the three standards. However, the above mentioned standards also include some information and references on compatibility, if further study is required.

Multivariate Exponentially Weighted Moving Average (MEWMA) chart is a control chart under multivariate process based on the idea of EWMA that rapidly detects small dispersion with a trend. the MEWMA chart was first time discussed by Woodall. However, in his study the correlativity among the multivariate was not considered for the selection of the constant of the diagonal matrix (the smooth parameter λ). In this paper, the EWMA chart with a generalized smooth parameter matrix has been discussed which is believed to have a better fitness for practical production processes than Woodall’s approach.

How to measure Quality Performance (QP) or excellence performance in organizations is very important for improving the quality of an organization’s products and services. This paper takes Quality Function Deployment (QFD) as a ueful tool to identify the key characteristics of quality performance and measure the influence factors on quality performance. Most national quality awards provide a framework of the criteria to show the essential elements of an organization’s quality performance and get the Quality Performance Score (QPS) by self‐assessment using the criteria. By means of these criteria, especially, the criteria of China Quality Award (CQA), a measurable indicator system for quality performance is set up. A four‐phase QFD model of assessment for quality performance is developed. This QFD model not only presents the most important efforts for the deployment of the measurable indicators of quality performance, but also takes great advantage of evaluating the quality performance and obtaining the quality performance score. The measurable indicator hierarchy of quality performance is formed and its implementation method for assessment quality performance is described in this paper.