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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 the past few years, several capability indices have been developed for evaluating the performance of multivariate manufacturing processes under the normality assumption. However, this assumption may not be true in most practical situations. Thus, the purpose of this paper is to develop new capability indices for evaluating the performance of multivariate processes subject to non-normal distributions.

In this paper, the authors propose three non-normal multivariate process capability indices (MPCIs) RNMC _p , RNMC _pm and RNMC _pu by relieving the normality assumption. Using the two normal MPCIs proposed by Pan and Lee, a weighted standard deviation method (WSD) is used to modify the NMC _p and NMC _pm indices for the-nominal-the-best case. Then the WSD method is applied to modify the multivariate ND index established by Niverthi and Dey for the-smaller-the-better case.

A simulation study compares the performance of the various multivariate indices. Simulation results show that the actual non-conforming rates can be correctly reflected by the proposed capability indices. The numerical example further demonstrates that the actual quality performance of a non-normal multivariate process can properly reflected by the proposed capability indices.

Process capability index is an important SPC tool for measuring the process performance. If the non-normal process data are mistreated as a normal one, it will result in an improper decision and thereby lead to an unnecessary quality loss. The new indices can provide practicing managers and engineers with a better decision-making tool for correctly measuring the performance for any multivariate process or environmental system.

Once the existing multivariate quality/environmental problems and their Key Performance Indicators are identified, one may apply the new capability indices to evaluate the performance of various multivariate processes subject to non-normal distributions.

The purpose of this paper is to explore the relationship between multivariate process capability indices and loss functions for both nominal‐the‐best and smaller‐the‐better cases, so the likelihood and consequences resulting from the nonconforming of a manufacturing process or an environmental system can be evaluated simultaneously.

In this paper, the authors present a new approach of correlated risk assessment by linking the multiple process capability indices and loss functions, in which the multivariate process capability indices and multivariate loss functions describe the likelihood and consequences as a result of nonconformities in multivariate manufacturing or environmental system, respectively. Then, the associated relationship equations are developed using multivariate methods. Moreover, a step‐by‐step procedure is provided to facilitate the implementation of the correlated risk assessment.

Given the multivariate process capability indices, the authors show that the expected loss can be estimated by developed relationship equations and two numerical examples are also given, to demonstrate how the correlated manufacturing and environmental risks can be properly assessed by linking the multivariate process capability indices and multivariate loss function.

The risk information of likelihood and expected loss, classified in the four planning zones of a strategic planning matrix, provides practising managers and engineers with a decision‐making tool for prioritizing their quality improvement projects when conducting risk assessment for any multivariate process or environmental system.

Once the existing quality/environmental problems and their Key Performance Indicators are identified, one may conduct risk assessment by applying the relationship equations to evaluate the impact of correlated risk on manufacturing processes or multiple environmental emissions inside company; this can lead to the direction of continuous improvement for any industry.

The purpose of this research is to provide a new loss function‐based risk assessment method so the likelihood and consequence resulting from the failure of a manufacturing or environmental system can be evaluated simultaneously.

Instead of using risk matrices of the occurrence and consequence separately for evaluating manufacturing and environmental risks, an integrated approach by exploring the relationship between process capability indices: C_p, C_pk and C_pm, and three different loss functions: Taguchi's loss function; Inverted normal loss function (INLF); and Revised inverted normal loss function (RINLF) is proposed.

The new method of quantitative risk assessment linking the likelihood and expected loss of failure is illustrated by two numeric examples. The results suggest that the revised inverted normal loss function (RINLF) be used in assessing manufacturing and environmental risks.

It gives decision‐makers a concrete tool to assess the likelihood and consequence of their processes. Linking the process capability indices and loss functions is particularly promising, as this provides a useful risk assessment tool for practitioners who want to reduce hazardous waste and manufacturing losses from their facilities.

The manufacturing and environmental risks are determined by paring the process capability indices and loss function. From the loss function‐based estimation, one can quantify the consequence of a manufacturing loss and get the severity rating in an objective way.

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.

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.

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.

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.

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.

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.

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.