Construction of p-charts using degree of nonconformity

Authors:
Vahid Amirzadeh;Mashaallah Mashinchi;Abbas Parchami
Affiliations:
Department of Statistics, Faculty of Mathematics and Computer Sciences, Shahid Bahonar University of Kerman, P.O. Box 76135, 133 Kerman, Iran;Department of Statistics, Faculty of Mathematics and Computer Sciences, Shahid Bahonar University of Kerman, P.O. Box 76135, 133 Kerman, Iran;Department of Statistics, Faculty of Mathematics and Computer Sciences, Shahid Bahonar University of Kerman, P.O. Box 76135, 133 Kerman, Iran
Venue:
Information Sciences: an International Journal
Year:
2009

Citing 9
Cited 1

Fuzzy quality and analysis on fuzzy probability

Fuzzy Sets and Systems - Special issue on fuzzy methodology in system failure engineering
&agr;-Cut fuzzy control charts for linguistic data

International Journal of Intelligent Systems
Toward a generalized theory of uncertainty (GTU): an outline

Information Sciences—Informatics and Computer Science: An International Journal
An alternative approach to fuzzy control charts: Direct fuzzy approach

Information Sciences: an International Journal
Statistics with fuzzy data in statistical quality control

Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on BISCSE 2005 " Forging the Frontiers" Part II
Is there a need for fuzzy logic?

Information Sciences: an International Journal
A fuzzy approach to define sample size for attributes control chart in multistage processes: An application in engine valve manufacturing process

Applied Soft Computing
Development of fuzzy process control charts and fuzzy unnatural pattern analyses

Computational Statistics & Data Analysis
Fuzzy process control: construction of control charts with fuzzy numbers

Fuzzy Sets and Systems

Confidence interval of generalized Taguchi index

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Quantified Score

Hi-index	0.07

Visualization

Abstract

Control charts are among the simplest of on-line statistical process control techniques. When the quality characteristic is a variable, the p-chart takes time to react to shifts in the production process because of its weak response to small variations in the process mean and variance. In this paper, instead of considering an item to be either conforming or nonconforming, the ''degree of nonconformity'' based on fuzzy concepts is defined and a fuzzy p-chart based on the mean degree of nonconformity is constructed. It is shown that this chart has a better response to variations in both the mean and the variance of the process.