A transformation technique to estimate the process capability index for non-normal processes

Hosseinifard, Seyedehzahra, Abbasi, Babak, Ahmad, S and Abdollahian, M 2009, A transformation technique to estimate the process capability index for non-normal processes, International journal of advanced manufacturing technology, vol. 40, pp. 512-517, doi: 10.1007/s00170-008-1376-x.

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Title A transformation technique to estimate the process capability index for non-normal processes
Author(s) Hosseinifard, SeyedehzahraORCID iD for Hosseinifard, Seyedehzahra orcid.org/0000-0002-1064-9079
Abbasi, Babak
Ahmad, S
Abdollahian, M
Journal name International journal of advanced manufacturing technology
Volume number 40
Start page 512
End page 517
Total pages 6
Publisher Springer
Place of publication Berlin, Germany
Publication date 2009-01
ISSN 0268-3768
Keyword(s) Science & Technology
Automation & Control Systems
Engineering, Manufacturing
Process capability index
Non-normal process
Root transformation
Box-Cox transformation and quintile-based capability indices
Summary Estimating the process capability index (PCI) for non-normal processes has been discussed by many researches. There are two basic approaches to estimating the PCI for non-normal processes. The first commonly used approach is to transform the non-normal data into normal data using transformation techniques and then use a conventional normal method to estimate the PCI for transformed data. This is a straightforward approach and is easy to deploy. The alternate approach is to use non-normal percentiles to calculate the PCI. The latter approach is not easy to implement and a deviation in estimating the distribution of the process may affect the efficacy of the estimated PCI. The aim of this paper is to estimate the PCI for non-normal processes using a transformation technique called root transformation. The efficacy of the proposed technique is assessed by conducting a simulation study using gamma, Weibull, and beta distributions. The root transformation technique is used to estimate the PCI for each set of simulated data. These results are then compared with the PCI obtained using exact percentiles and the Box-Cox method. Finally, a case study based on real-world data is presented.
Language eng
DOI 10.1007/s00170-008-1376-x
Field of Research 150302 Business Information Systems
080699 Information Systems not elsewhere classified
010401 Applied Statistics
09 Engineering
08 Information And Computing Sciences
01 Mathematical Sciences
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2008, Springer-Verlag London
Persistent URL http://hdl.handle.net/10536/DRO/DU:30084889

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