Different methods to fuzzy X¯-R control charts used in production

Different methods to fuzzy X¯-R control charts used in production PurposeThe control charts are used in many production areas because they give an idea about the quality characteristic(s) of a product. The control limits are calculated and the data are examined whether the quality characteristic(s) is/are within these limits. At this point, it may be confusing to comment, especially if it is slightly below or above the limit values. In order to overcome this situation, it is suitable to use fuzzy numbers instead of crisp numbers. The purpose of this paper is to demonstrate how to create control limits of X¯-R control charts for a specified data set of interval type-2 fuzzy sets.Design/methodology/approachThere are methods in the literature, such as defuzzification, distance, ranking and likelihood, which may be applicable for interval type-2 fuzzy set. This study is the first that these methods are adapted to the X¯-R control charts. This methodology enables interval type-2 fuzzy sets to be used in X¯-R control charts.FindingsIt is demonstrated that the methods – such as defuzzification, distance, ranking and likelihood for interval type-2 fuzzy sets – could be applied to the X¯-R control charts. The fuzzy control charts created using the methods provide similar results in terms of in/out control situations. On the other hand, the sample points depicted on charts show similar pattern, even though the calculations are different based on their own structures. Finally, the control charts obtained with interval type-2 fuzzy sets and the control charts obtained with crisp numbers are compared.Research limitations/implicationsBased on the related literature, research works on interval type-2 fuzzy control charts seem to be very limited. This study shows the applicability of different interval type-2 fuzzy methods on X¯-R control charts. For the future study, different interval type-2 fuzzy methods may be considered for X¯-R control charts.Originality/valueThe unique contribution of this research to the relevant literature is that interval type-2 fuzzy numbers for quantitative control charts, such as X¯-R control charts, is used for the first time in this context. Since the research is the first adaptation of interval type-2 fuzzy sets on X¯-R control charts, the authors believe that this study will lead and encourage the people who work on this topic. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Enterprise Information Management Emerald Publishing

Different methods to fuzzy X¯-R control charts used in production

Loading next page...
 
/lp/emerald-publishing/different-methods-to-fuzzy-x-r-control-charts-used-in-production-5UrlY0zYcZ
Publisher
Emerald Publishing
Copyright
Copyright © Emerald Group Publishing Limited
ISSN
1741-0398
DOI
10.1108/JEIM-01-2018-0011
Publisher site
See Article on Publisher Site

Abstract

PurposeThe control charts are used in many production areas because they give an idea about the quality characteristic(s) of a product. The control limits are calculated and the data are examined whether the quality characteristic(s) is/are within these limits. At this point, it may be confusing to comment, especially if it is slightly below or above the limit values. In order to overcome this situation, it is suitable to use fuzzy numbers instead of crisp numbers. The purpose of this paper is to demonstrate how to create control limits of X¯-R control charts for a specified data set of interval type-2 fuzzy sets.Design/methodology/approachThere are methods in the literature, such as defuzzification, distance, ranking and likelihood, which may be applicable for interval type-2 fuzzy set. This study is the first that these methods are adapted to the X¯-R control charts. This methodology enables interval type-2 fuzzy sets to be used in X¯-R control charts.FindingsIt is demonstrated that the methods – such as defuzzification, distance, ranking and likelihood for interval type-2 fuzzy sets – could be applied to the X¯-R control charts. The fuzzy control charts created using the methods provide similar results in terms of in/out control situations. On the other hand, the sample points depicted on charts show similar pattern, even though the calculations are different based on their own structures. Finally, the control charts obtained with interval type-2 fuzzy sets and the control charts obtained with crisp numbers are compared.Research limitations/implicationsBased on the related literature, research works on interval type-2 fuzzy control charts seem to be very limited. This study shows the applicability of different interval type-2 fuzzy methods on X¯-R control charts. For the future study, different interval type-2 fuzzy methods may be considered for X¯-R control charts.Originality/valueThe unique contribution of this research to the relevant literature is that interval type-2 fuzzy numbers for quantitative control charts, such as X¯-R control charts, is used for the first time in this context. Since the research is the first adaptation of interval type-2 fuzzy sets on X¯-R control charts, the authors believe that this study will lead and encourage the people who work on this topic.

Journal

Journal of Enterprise Information ManagementEmerald Publishing

Published: Oct 8, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create folders to
organize your research

Export folders, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off