Browsing by Author "Liao, Mou-Yuan"

Now showing 1 - 5 of 5

Assessing the actual Gamma process quality - a curve-fitting approach for modifying the non-normal flexible index
(2015-08-03)
Liao, Mou-Yuan
;
Pearn, W. L.
;
Liu, Yen-Lun
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
Process capability indices (PCIs) have been widely adopted for quality assurance activities. By analysing PCIs, a production department can trace and improve a poor process to enhance product quality level and satisfy customer requirements. Among these indices, C-pk remains the most prevalent for facilitating managerial decisions because it can provide bounds on the process yield for normally distributed processes. However, processes are often non-normal in practice, and C-pk may quite likely misrepresent the actual product quality. Hence, the flexible index C-jkp, which considers possible differences in the variability above and below the target value, has been developed for practical use. However, C-jkp continues to suffer from serious bias in assessing actual capability, especially when the process distribution is highly skewed. In this paper, we modify C-jkp for assessing the actual process quality of a Gamma process. A correction factor is obtained by the curve-fitting method. The results show that our proposed method can significantly reduce the bias for calculation of actual nonconformities. Moreover, we introduce a sample estimator for our modified index. The ratio of this estimator\'s average value and the modified index is approximately 1. This implies that our proposed estimator can provide an appropriate estimation for assessing the actual Gamma process quality.
Estimating and testing process precision with presence of gauge measurement errors
(2007-10-01)
Pearn, W. L.
;
Liao, Mou-Yuan
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
Process capability indices have been widely used in the manufacturing industry. Those capability indices, quantifying process potential and performance, are important for any successful quality improvement activities and quality program implementation. Because of the simplicity and easy of understanding, the precision index C-p has gained its popularity for measuring process consistency. However, the quality of data on the process characteristics relies very much on the gauge measurement. Conclusions about capability of the process just only based on the single numerical value of the index are not reliable. In this paper, we not only conduct the performance of the index C-p with gauge measurement errors, but also present adjusted confidence interval bounds and critical values for capability testing purpose of C-p with unavoidable measurement errors. Our research would help practitioners to determine whether the factory processes meet the capability requirement, and make more reliable decisions.
Modified weighted standard deviation index for adequately interpreting a supplier's lognormal process capability
(2019-02-01)
Liao, Mou-Yuan
;
Pearn, W. L.
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
The process capability index has become an efficient tool for measuring a supplier's process performance. Cpk(WSD) is one popularly used index for assessing non-normal process capability when the process violates the normality assumption. Unfortunately, this index cannot accurately reflect the process yield, so it may produce a serious result if the practitioner compares the calculated Cpk(WSD) value with the capability requirement to determine whether the process meets that requirement. Hence, this study modifies Cpk(WSD) to provide an adequate measure of lognormal process capability. In addition, an estimator of this modified index is also provided. Simulations show that the bias of this estimator is slight, and the coverage probability of capability testing is close to the nominal confidence. This means that our proposed method is adaptable for use.
One-sided process capability assessment in the presence of measurement errors
(2006-11-01)
Pearn, W. L.
;
Liao, Mou-Yuan
;
工業工程與管理學系
;
Department of Industrial Engineering and Management
In the manufacturing industry, many product characteristics are of one-sided specifications. The well-known process capability indices C-PU and C-PL are often used to measure process performance. Most capability research works have assumed no measurement errors. Unfortunately, such an assumption is not realistic even if the measurement is conducted using highly sophisticated advanced measuring instruments. Therefore, conclusions drawn regarding process capability are not reliable. In this paper, we consider the estimation and testing of C-PU and C-PL with the presence of measurement errors, to obtain adjusted lower confidence bounds and critical values for true process capability, which can be used to determine whether the factory processes meet the capability requirement when the measurement errors are unavoidable. Copyright (C) 2005 John Wiley & Sons, Ltd.
Two Tests for Supplier Selection Based on Process Yield
(2011-03-01)
Pearn, W. L.
;
Liao, Mou-Yuan
;
Wu, Chien-Wei
;
Chu, Yao-Tsung
;
工業工程與管理學系
Market share is highly corrected with customer satisfaction and business success; many companies increase their outsourcing level to keep their core competition. Supplier selection problems have become an important issue of production management. Process yield is a standard criterion in the manufacturing industry as a common measure on process performance. The index S(PK) provides an exact measure on the process yield for normal processes. However, most studies considered the assessment of index S(PK) for just one single supplier; the testing procedure for suppliers selection has not been done at present. Hence, the principal purpose of this research is to determine the more capable process between two competing suppliers. Two tests are presented based on S(PK), and four bootstrap methods are used for obtaining the test statistics. By Monte Carlo simulations, performance comparisons of these methods are performed in terms of empirical size and selection power. Finally, a case on the color filter manufacturing process is investigated to demonstrate the applicability of the proposed method.