EconPapers    
Economics at your fingertips  
 

Measuring Process Performance Based on Expected Loss with Asymmetric Tolerances

W. L. Pearn, Y. C. Chang and Chien-Wei Wu

Journal of Applied Statistics, 2006, vol. 33, issue 10, 1105-1120

Abstract: By approaching capability from the point of view of process loss similar to Cpm , Johnson (1992) provided the expected relative loss Le to consider the proximity of the target value. Putting the loss in relative terms, a user needs only to specify the target and the distance from the target at which the product would have zero worth to quantify the process loss. Tsui (1997) expressed the index Le as Le = Lot + Lpe , which provides an uncontaminated separation between information concerning the process relative off-target loss (Lot) and the process relative inconsistency loss (Lpe). Unfortunately, the index Le inconsistently measures process capability in many cases, particularly for processes with asymmetric tolerances, and thus reflects process potential and performance inaccurately. In this paper, we consider a generalization, which we refer to as [image omitted] , to deal with processes with asymmetric tolerances. The generalization is shown to be superior to the original index Le. In the cases of symmetric tolerances, the new generalization of process loss indices [image omitted] , [image omitted] and [image omitted] reduces to the original index Le, Lot, and Lpe , respectively. We investigate the statistical properties of a natural estimator of [image omitted] [image omitted] and [image omitted] when the underlying process is normally distributed. We obtained the rth moment, expected value, and the variance of the natural estimator [image omitted] , [image omitted] , and [image omitted] . We also analyzed the bias and the mean squared error in each case. The new generalization [image omitted] measures process loss more accurately than the original index Le.

Keywords: Asymmetric tolerances; bias; mean squared error; process capability indices; process loss indices (search for similar items in EconPapers)
Date: 2006
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/02664760600746871 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:33:y:2006:i:10:p:1105-1120

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664760600746871

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:japsta:v:33:y:2006:i:10:p:1105-1120