The fast evolution of management systems standards ISO 9000 and ISO 14000 worldwide, from unknown entities to well‐established management practices, represents a facet of the global marketplace in which many firms operate. Over 400,000 firms in over 150 countries have adopted ISO 9000 since it was introduced in 1986. Its successor, ISO 14000, was introduced in 1996 and has already been adopted by over 30,000 firms in over 100 countries. Reports on the results of an ISO 9000/14000 mail survey, administered in four Far eastern countries including Japan, South Korea, Hong Kong and Taiwan to explore and compare the similarities and differences of motivations, implementations and certification benefits among these countries. Survey data have been analyzed using the multivariate statistical methods and techniques such as factor analysis, cluster analysis, Kruskal‐Wallis test, etc. Several conclusions and suggestions are made based on the statistical analysis results.

Generally, an industrial product has more than one quality characteristic. In order to establish performance measures for evaluating the capability of a multivariate manufacturing process, several multivariate process capability indices have been developed in the past few years. Among them, Taam's MC p and MC pm indices have the drawback of overestimation and Hubele's three-component capability vector lacks simplicity in practice. In this article, taking the correlation among multiple quality characteristics into account, we develop two novel indices; NMC p and NMC pm . Using two numerical examples we demonstrate that the true performance of multivariate processes are accurately reflected in our NMC p and NMC pm indices and in their associated interval estimates. Finally, simulation results show that our indices outperform both those of Taam and Hubele.

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SUMMARYStatistical control chart is commonly used in the industry to help ensure stability of manufacturing process and it can also be used to monitor the environmental data, such as industrial waste or effluent of manufacturing process. However, control chart needs to be modified if the set of environmental data exhibits the property of long memory. In this paper, a control chart for autocorrelated data using autoregressive fractionally integrated moving-average (ARFIMA) model is proposed to monitor the long-memory air quality data. Finally, we use the air quality data of Taiwan as examples to compare the difference between ARFIMA and autoregressive integrated moving-average (ARIMA) models. The results show that residual control charts using ARFIMA models are more appropriate than those using ARIMA models.