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.

Six sigma program, gauge repeatability and reproducibility, precision to tolerance (P/T) ratio, ANOVA, classical GR&R method, long form method,

Purpose-The purpose of this research is to develop a new key performance index (KPI) and its interval estimation for measuring the service quality from customers' perceptions, since most service quality data follow non-normal distribution. Design/methodology/approach-Based on the non-normal process capability indices used in manufacturing industries, a new KPI suitable for measuring service quality is developed using Parasuraman's 5th Gap between customers' expectation and perception. Moreover, the confidence interval of the proposed KPI is established using the bootstrapping method. Findings-The quantitative method for measuring the service quality through the new KPI and its interval estimation is illustrated by a realistic example. The results show that the new KPI allows practising managers to evaluate the actual service quality level delivered within each of five SERVQUAL categories and prioritize the possible improvement projects from customers' perspectives. Moreover, compared with the traditional method of sample size determination, a substantial amount of cost savings can be expected by using the suggested sample sizes. Practical implications-The paper presents a structured approach of opportunity assessment for improving service quality from a strategic alignment perspective, particularly in the five dimensions: tangibles, reliability, responsiveness, assurance, and empathy. The new approach provides practising managers with a decision-making tool for measuring service quality, detecting problematic situations and selecting the most urgent improvement project. Once the existing service problems are identified and improvement projects are prioritized, it can lead to the direction of continuous improvement for any service industry. Originality/value-Given a managerial target on any desired service level as well as customers' perceptions and expectations, the new KPI could be applied to any non-normal service quality and other survey data. Thus, the corporate performance in terms of key factors of business success can also be measured by the new KPI, which may lead to managing complexities and enhancing sustainability in service industries.

Recently, gauge repeatability and reproducibility (GR&R) study has been highly regarded by the quality practitioners when QS9000 and D19000 become fashionable requirements for manufacturing industries. Measurement plays a significant role in helping organizations improve their product quality. Good quality of products is the key factor towards business success. Therefore, how to ensure the quality of measurement becomes an important task for quality practitioners. In performing the GR&R study, several parameters, such as the appropriate sample size of parts (n), number of inspectors ( p) and replicate measurements (k) are frequently asked by quality personnel in industries. The adequacy of current way of (n, p, k) selection is very questionable. A statistical method using the shortest confidence interval and its associated computer programming algorithm are presented in this paper for evaluating the optimal allocation among sample size of parts (n), number of inspectors ( p) and replicate measurements (k). Hopefully, it can provide a useful reference for quality practitioners in industries.