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

Content Provider	Springer Nature Link
Author	Hosseinifard, S. Z. Abbasi, Babak Ahmad, S. Abdollahian, M.
Copyright Year	2008
Abstract	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.
Starting Page	512
Ending Page	517
Page Count	6
File Format	PDF
ISSN	02683768
Journal	The International Journal of Advanced Manufacturing Technology
Volume Number	40
Issue Number	5-6
e-ISSN	14333015
Language	English
Publisher	Springer-Verlag
Publisher Date	2008-02-05
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Process capability index Non-normal process Root transformation Box-Cox transformation and quintile-based capability indices Computer-Aided Engineering (CAD, CAE) and Design Mechanical Engineering Production/Logistics Industrial and Production Engineering
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Control and Systems Engineering Mechanical Engineering Computer Science Applications Software

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