Kuen-Suan Chen

Service quality evaluation by service quality performance matrix

Excellent service quality and high customer satisfaction is the key issue and challenge for todays service industry. Customer perception (satisfaction) and customer expectation (importance) determines the service quality performance. Questionnaires help service providers to realize their service quality performance, and the weighted average of customer satisfaction and the associated variance are commonly used indices reflecting customer expectation and customer percep tion. To evaluate, effectively and efficiently, service quality performance, this paper aims to portray customer expectation and customer satisfaction by assuming that the evaluation scales for expectation and satisfaction are between 0 and 1. This paper defines a customer satisfaction index and a customer expectation index based on the two parameters of a beta distribution, and the unbiased estimators for these two indices are provided. A standardized Service Quality Performance Matrix helps managers realize the service quality performance for important service elements with respect to the locations of the satisfaction index and the expectation index on the Service Quality Performance Matrix to propose adequate service quality improvement plans and strategies.

Distributional and inferential properties of the process accuracy and process precision indices

Process capability indices such as Cp, k, and Cpk, have been widely used in manufacturing industry to provide numerical measures on process potential and performance. While Cp measures overall process variation, k measures the degree of process departure. In this paper, we consider the index Cp and a transformation of k defined as Ca = 1 - k which measures the degree of process centering. We refer to Cp as the process precision index, and Ca as the process accuracy index. We consider the estimators of Cp and Ca, and investigate their statistical properties. For Cp we obtain the UMVUE and the MLE. We show that this UMVUE is consistent, and asymptotically efficient. For Ca, we investigate its natural estimator. We obtain the first two moments of this estimator, and show that the natural estimator is the MLE, which is asymptotically unbiased and asymptotically efficient. We also propose an efficient test based on the UMVUE of Cp We show that the proposed test is the UMP test.

A PRACTICAL IMPLEMENTATION OF THE PROCESS CAPABILITY INDEX Cpk

Abstract The generalization , a modification of the process capability index Cpm, not only takes the proximity of the target value into consideration but also takes into account the asymmetry of the specification limits. In this paper, based on the theory of testing hypothesis we develop a step-by-step procedure using estimator of for the practitioners in making decisions when the process is normally distributed. An efficient MAPLE computer program is developed to calculate the corresponding p-value. For non-normal sample data, an example is presented that an approach based on Johnson transformation is to transform the nonnormal data to normality. The proposed decision making rule can be used to test whether the process is capable or not.

Process capability analysis for an entire product

Process capability indices (PCIs) are powerful means of studying the process ability for manufacturing a product that meets specifications. Several capability indices including C p , C pu , C pl and C pk have been widely used in manufacturing industry to provide common quantitative measures on process potential and performance. The formulas for these indices are easily understood and can be straightforwardly applied. However, those process capability indices are inappropriate for asymmetric tolerances and could not be applied to evaluate multiprocess products. Based on C p , C pu , C pl and C pk , this research aims to develop one process capability analysis chart (PCAC) for precisely measuring an entire product composed of symmetric tolerances, asymmetric tolerances, larger-the-better and smaller-the-better characteristics. The process capability analysis chart evaluates the capabilities of multi-process products and provides chances for continuous improvement on the manufacturing process.

One‐sided capability indices CPU and CPL: decision making with sample information

Process capability indices have been used in the manufacturing industry to provide quantitative measures on process potential and performance. The formulae for these indices are easy to understand and straightforward to apply. But, since sample data must be collected in order to calculate these indices, a great degree of uncertainty may be introduced into capability assessments due to sampling errors. Currently, most practitioners simply look at the value of the index calculated from the sample data and then make a conclusion on whether their processes meet the capability requirement. This approach is not reliable since sampling errors are ignored. Procedures for two‐sided capability indices, Cp, Cpk, and Cpm have been developed to assist practitioners to determine whether their processes meet the capability requirement based on sample information. In this paper, we first obtain unbiased estimators of CPU and CPL. We then develop a procedure similar to those of Cp, Cpk, and Cpm, for the one‐sided capability indices CPU and CPL. Practitioners can use the procedure to test whether their processes meet the capability requirement.

Capability indices for non-normal distributions with an application in electrolytic capacitor manufacturing

Abstract Process capability indices C p ( u , v ), which include the four basic indices C p , C pk , C pm and C pmk as special cases, have been proposed to measure process potential and performance. C p ( u , v ) are appropriate indices for processes with normal distributions, but have been shown to be inappropriate for processes with non-normal distributions. In this paper, we first consider two generalizations of C p ( u , v ), which we refer to as C Np ( u , v ) and C ′ Np ( u , v ), to cover cases where the underlying distributions may not be normal. Comparisons between C Np ( u , v ) and C ′ Np ( u , v ) are provided. The results indicated that the generalizations C Np ( u , v ) are superior to C ′ Np ( u , v ) in measuring process capability. We then present a case study on an aluminum electrolytic-capacitor manufacturing process to illustrate how the generalizations C Np ( u , v ) may be applied to actual data collected from the factories.

MULTIPROCESS PERFORMANCE ANALYSIS: A CASE STUDY

Statistical process control charts, which are essential tools to process control and improvement, have been widely used for monitoring individual factory production processes. In the multiprocess environment where a group of processes need to be monitor..

Statistical testing for assessing the performance of lifetime index of electronic components with exponential distribution

The electronics industry has heavily prioritized enhancing the quality, lifetime and conforming rate (conforming to specifications) of electronic components. Various methods have been developed for assessing quality performance. In practice, process capability indices (PCIs) are used as a means of measuring process potential and performance. Moreover, most PCIs have been developed or investigated under the assumption that electronic components have a lifetime with a normal distribution. However, PCIs for non‐normal distributions have seldom been discussed. Nevertheless, the lifetime of electronic components generally may possess an exponential, gamma or Weibull distribution and so forth. Under an exponential distribution, some properties of the PCIs and their estimators differ from those in a normal distribution. To utilize the PCIs more reasonably and accurately in assessing the lifetime performance of electronic components, this study constructs a uniformly minimum variance unbiased (UMVU) estimator of their lifetime performance index under an exponential distribution. The UMVU estimator of the lifetime performance index is then utilized to develop the hypothesis testing procedure. The purchasers can then employ the testing procedure to determine whether the lifetime of the electronic components adheres to the required level. Manufacturers can also utilize this procedure to enhance process capability.

An application of non-normal process capability indices

Numerous process capability indices, including Cp, Cpk, Cpm, and Cpmk, have been proposed to provide measures of process potential and performance. In this paper, we consider some generalizations of these four basic indices to cover non-normal distributions. The proposed generalizations are compared with the four basic indices. The results show that the proposed generalizations are more accurate than those basic indices and other generalizations in measuring process capability. We also consider an estimation method based on sample percentiles to calculate the proposed generalizations, and give an example to illustrate how we apply the proposed generalizations to actual data collected from the factory.

MAKING DECISIONS IN ASSESSING PROCESS CAPABILITY INDEX Cpk

SUMMARY Process capability indices Cp, Cpk and Cpm have been used in manufacturing industries to provide a quantitative measure of process potential and performance. The formulae for these indices are easy to understand and straightforward to apply. However, since sample data must be collected in order to calculate these indices, a great degree of uncertainty may be introduced into capability assessments owing to sampling errors. Currently, most practitioners simply look at the value of the index calculated from the sample data and then make a conclusion on whether the given process meets the capability (quality) requirement. This approach is not reliable, since sampling errors are ignored. Cheng (Qual. Engng., 7, 239‐259 (1994)) has developed a procedure involving estimators of Cp and Cpm for practitioners to use to determine whether a process meets the capability requirement or not. However, no procedure for Cpk was given, because difficulties were encountered in calculating the sampling distribution of the estimator of Cpk. In this paper we use a newly proposed estimator of Cpk to develop a procedure for practitioners to use so that decisions made in assessing process capability are more reliable. Copyright