Estimating capability index cpkfor processes with asymmetric tolerances

2000 ◽  
Vol 29 (11) ◽  
pp. 2593-2604 ◽  
Author(s):  
W. L. Pearn ◽  
G. H. Lin
Keyword(s):  
Capability Index ◽  
Asymmetric Tolerances

A NEW GENERALIZATION OF Cpm FOR PROCESSES WITH ASYMMETRIC TOLERANCES

1999 ◽  
Vol 06 (04) ◽  
pp. 383-398 ◽  
Author(s):  
K. S. CHEN ◽  
W. L. PEARN ◽  
P. C. LIN
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Exact Distribution ◽  
Process Capability Index ◽  
Index Formula ◽  
Capability Index ◽  
Special Cases ◽  
Asymmetric Tolerances ◽  
Incapability Index ◽  
Original Index

Greenwich and Jahr-Schaffrath9 introduced the incapability index C pp , which is a simple transformation of the index [Formula: see text] proposed by Chan et al.3 Chen10 considered the incapability index [Formula: see text], a generalization of C pp , to handle processes with asymmetric tolerances. Based on the same idea on [Formula: see text], we consider a new generalization [Formula: see text], which is a modification of the process capability index C pm . In the cases of symmetric tolerances, the new generalization [Formula: see text] reduces to the original index C pm . The new generalization [Formula: see text] not only takes the proximity of the target value into consideration, like those of C pm and [Formula: see text], but also takes into account the asymmetry of the specification limits. We compare the new generalization [Formula: see text] with C pa (1, 3) and C pa (0, 4), two special cases of C pa (u, v) recommended by Vännman7 for asymmetric tolerances. We also investigate the statistical properties of the natural estimator [Formula: see text], assuming the process is normally distributed. We obtain the exact distribution and an explicit form of the probability density function of [Formula: see text]. In addition, we compute the rth moment-expected value, variance of [Formula: see text], and analyze the bias as well as the MSE of [Formula: see text].

Data driven supplier selection as a circular economy enabler: A Taguchi capability index for manufactured products with asymmetric tolerances

2021 ◽  
Vol 47 ◽  
pp. 101249 ◽  
Author(s):  
Chin-Hsin Wang ◽  
Mohd Helmi Ali ◽  
Kuen-Suan Chen ◽  
Yeneneh Tamirat Negash ◽  
Ming-Lang Tseng ◽  
...  
Keyword(s):  
Circular Economy ◽  
Supplier Selection ◽  
Data Driven ◽  
Capability Index ◽  
Manufactured Products ◽  
Asymmetric Tolerances

scholarly journals Fuzzy Evaluation Model of Process Improvement Capability with Costs Consideration

2021 ◽  
Vol 11 (10) ◽  
pp. 4344
Author(s):  
Kuen-Suan Chen ◽  
Shui-Chuan Chen ◽  
Ting-Hsin Hsu ◽  
Min-Yi Lin ◽  
Chih-Feng Wu
Keyword(s):  
Process Improvement ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Fuzzy Evaluation ◽  
Sampling Errors ◽  
Capability Index ◽  
Testing Method ◽  
Improvement Potential ◽  
Testing Accuracy

The Taguchi capability index, which reflects the expected loss and the yield of a process, is a useful index for evaluating the quality of a process. Several scholars have proposed a process improvement capability index based on the expected value of the Taguchi loss function as well as the corresponding cost of process improvement. There have been a number of studies using the Taguchi capability index to develop suppliers’ process quality evaluation models, whereas models for evaluating suppliers’ process improvement potential have been relatively lacking. Thus, this study applies the process improvement capability index to develop an evaluation model of the supplier’s process improvement capability, which can be provided to the industry for application. Besides, owing to the current need to respond quickly, coupled with cost considerations and the limits of technical capabilities, the sample size for sampling testing is usually not large. Consequently, the evaluation model of the process improvement capability developed in this study adopts a fuzzy testing method based on the confidence interval. This method reduces the risk of misjudgment due to sampling errors and improves the testing accuracy because it can incorporate experts and their accumulated experiences.

Developing an Outsourcing Partner Selection Model for Process with Two-Sided Specification Using Capability Index and Manufacturing Time Performance Index

2019 ◽  
Vol 26 (03) ◽  
pp. 1950015 ◽  
Author(s):  
Kuen-Suan Chen ◽  
Tsang-Chuan Chang ◽  
Yun-Tsan Lin
Keyword(s):  
Performance Index ◽  
Business Models ◽  
Partner Selection ◽  
Selection Model ◽  
Index Formula ◽  
Time Performance ◽  
Capability Index ◽  
Point Estimates ◽  
Manufacturing Time

In the face of fierce global competition, firms are outsourcing important but nonessential tasks to external professional companies. Corporations are also turning from competitive business models to cooperative strategic partnerships in hopes of swiftly responding to consumer needs and enhancing overall efficiency and industry competitiveness. This research developed an outsourcing partner selection model in hopes of helping firms select better outsourcing partners for long-term collaborations. Process quality and manufacturing time are vital when evaluating outsourcing partner. We therefore used process capability index [Formula: see text] and manufacturing time performance index [Formula: see text] in the proposed model. Sample data from random samples are needed to calculate the point estimates of indices, however, it is impossible to obtain a sample with a structure completely identical to that of the population, which means that sampling generates unavoidable sampling errors. The reliability of point estimates are also uncertain, which inevitably leads to misjudgment in some cases. Thus, to reduce estimate errors and increase assessment reliability, we calculated the [Formula: see text]% confidence intervals of the indices [Formula: see text] and [Formula: see text], then constructed the joint confidence region of [Formula: see text] and [Formula: see text] to develop an outsourcing partner selection model that will help firms select better outsourcing partners for long-term collaborations. We also provide a case as an illustration of how the proposed selection model is implemented.

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