Determining the optimal allocation of parameters for multivariate measurement system analysis

Pan, Jeh‐Nan; Li, Chung-I; Ou, Szu-Chen

doi:10.1016/j.eswa.2015.04.038

Cited by 9 publications

(3 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, a revised P/T ratio for multivariate MSA with correlated quality characteristics was proposed. Nonetheless, in performing GR&R studies, most industries today are still using the approval criteria for P/T ratios stipulated in QS9000 (15). A similar issue can be observed in the variation contribution of GR&R components.…”

Section: Discussionmentioning

confidence: 95%

“…If there is a reproducibility problem, it may be caused by the actions or errors of the system operators, such that further training may be required. Most institutions utilize a QS 9000 or D1-9000 quality system in conjunction with evaluations of GR&R (4,15). The measurement variation can be applied to a QC program for a clinical biochemistry laboratory.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improvement of the Quality of Work in a Biochemistry Laboratory via Measurement System Analysis

Chen

Liao

et al. 2016

Chin. J. Physiol.

View full text Add to dashboard Cite

An adequate and continuous monitoring of operational variations can effectively reduce the uncertainty and enhance the quality of laboratory reports. This study applied the evaluation rule of the measurement system analysis (MSA) method to estimate the quality of work conducted in a biochemistry laboratory. Using the gauge repeatability & reproducibility (GR&R) approach, variations in quality control (QC) data among medical technicians in conducting measurements of five biochemical items, namely, serum glucose (GLU), aspartate aminotransferase (AST), uric acid (UA), sodium (Na) and chloride (Cl), were evaluated. The measurements of the five biochemical items showed different levels of variance among the different technicians, with the variances in GLU measurements being higher than those for the other four items. The ratios of precision-to-tolerance (P/T) for Na, Cl and GLU were all above 0.5, implying inadequate gauge capability. The product variation contribution of Na was large (75.45% and 31.24% in normal and abnormal QC levels, respectively), which showed that the impact of insufficient usage of reagents could not be excluded. With regard to reproducibility, high contributions (of more than 30%) of variation for the selected items were found. These high operator variation levels implied that the possibility of inadequate gauge capacity could not be excluded. The analysis of variance (ANOVA) of GR&R showed that the operator variations in GLU measurements were significant (F=5.296, P=0.001 in the normal level and F=3.399, P=0.015 in the abnormal level, respectively). In addition to operator variations, product variations of Na were also significant for both QC levels. The heterogeneity of variance for the five technicians showed significant differences for the Na and Cl measurements in the normal QC level. The accuracy of QC for five technicians was identified for further operational improvement. This study revealed that MSA can be used to evaluate product and personnel errors and to improve the quality of work in a biochemical laboratory through proper corrective actions.

show abstract

Section: Discussionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Improvement of the Quality of Work in a Biochemistry Laboratory via Measurement System Analysis

Chen

Liao

et al. 2016

Chin. J. Physiol.

View full text Add to dashboard Cite

show abstract

“…They argued that the approach provides shorter standard deviations for & / and / & than the normal one-staged AGRR. Pan et al [26] suggested a revised % Tolerance for the multivariate GRR that provides smaller mean squared error and mean absolute percentage error than the conventional % Tolerance. They also calculated the optimal ( , , ) in terms of the new performance metric using the principal component analysis.…”

Section: Previous Studiesmentioning

confidence: 99%

Assessment of the Adequacy of Gauge Repeatability and Reproducibility Study Using a Monte Carlo Simulation

Kim

Park

2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

ANOVA gauge repeatability and reproducibility study is the most popular tool for measurement system analysis. Two experimental designs can be applied depending on the durability of the objects. If repeated measurements are possible or sufficient homogeneous nonrepeatable samples are available, crossed design is appropriate; otherwise, nested design should be used. In this paper, we investigated the adequacy of ANOVA gauge repeatability and reproducibility study from the perspective of practitioners. We proposed a Monte Carlo simulation that is close to the realistic procedure to evaluate the adequacy of both structures. During the evaluation, we considered the average performance metrics, percentage of correct decision, histogram shape, and symmetric mean absolute percentage error for the four popular performance metrics, namely, % Study Variation, % Contribution, % Tolerance, and the number of distinct categories. The experimental results show that the nested design fails to judge the precision of the gauge while the crossed design succeeds.

show abstract

Impact of measurement errors on the performance and distributional properties of the multivariate capability index $$\mathbf{NMC }_\mathbf{pm }$$ NMC pm

2017

View full text Add to dashboard Cite

Determining the optimal allocation of parameters for multivariate measurement system analysis

Cited by 9 publications

References 18 publications

Improvement of the Quality of Work in a Biochemistry Laboratory via Measurement System Analysis

Improvement of the Quality of Work in a Biochemistry Laboratory via Measurement System Analysis

Assessment of the Adequacy of Gauge Repeatability and Reproducibility Study Using a Monte Carlo Simulation

Impact of measurement errors on the performance and distributional properties of the multivariate capability index $$\mathbf{NMC }_\mathbf{pm }$$ NMC pm

Contact Info

Product

Resources

About