Fuzzy Xbar and S Control Charts Based on Confidence Intervals
There have been changes since the companies have realized the important role of quality improvement in their success. If they are able to produce high quality products and satisfy demands, then they can survive in competitive global markets. Quality improvement applications aim to decrease variabili...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Çanakkale Onsekiz Mart University
2021-03-01
|
| Series: | Journal of Advanced Research in Natural and Applied Sciences |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/en/download/article-file/1615124 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832095404273958912 |
|---|---|
| author | Nilufer Pekin Alakoc |
| author_facet | Nilufer Pekin Alakoc |
| author_sort | Nilufer Pekin Alakoc |
| collection | DOAJ |
| description | There have been changes since the companies have realized the important role of quality improvement in their success. If they are able to produce high quality products and satisfy demands, then they can survive in competitive global markets. Quality improvement applications aim to decrease variability, which leads to less cost, production time, number of defects, scrap, rework and more customer satisfaction. Quality can be improved by reducing product variability. On the other hand, uncertainty or subjectivity is a part of many engineering and real life problems. However, these problems cannot be solved by traditional methods. This study focuses on constructing Xbar and S control charts in fuzzy environment. The approach is developed by considering the theoretical structure of the Shewhart control charts. The core of the approach depends on the combination of parametric interval estimation and fuzzy statistics. Control limits and samples are presented by fuzzy numbers which ensures to maintain fuzziness in control charts. An important property of the approach is that the fuzzy charts can be reduced to Shewhart control charts. A simulation study was conducted for the performance evaluation of fuzzy Xbar and S control charts. The proposed fuzzy control chart is sensitive to process mean shifts and variance changes, and outperforms the traditional control charts under the changes of variance. In addition, an example from the literature shows that the approach is an effective way of presenting fuzziness in the quality characteristics, which enables the approach to have high applicability to the real life problems. |
| format | Article |
| id | doaj-art-9801f854a6364d7e9c6b2ad18c71b769 |
| institution | Kabale University |
| issn | 2757-5195 |
| language | English |
| publishDate | 2021-03-01 |
| publisher | Çanakkale Onsekiz Mart University |
| record_format | Article |
| series | Journal of Advanced Research in Natural and Applied Sciences |
| spelling | doaj-art-9801f854a6364d7e9c6b2ad18c71b7692025-02-05T17:58:10ZengÇanakkale Onsekiz Mart UniversityJournal of Advanced Research in Natural and Applied Sciences2757-51952021-03-017111413110.28979/jarnas.890356453Fuzzy Xbar and S Control Charts Based on Confidence IntervalsNilufer Pekin Alakoc0American University of the Middle EastThere have been changes since the companies have realized the important role of quality improvement in their success. If they are able to produce high quality products and satisfy demands, then they can survive in competitive global markets. Quality improvement applications aim to decrease variability, which leads to less cost, production time, number of defects, scrap, rework and more customer satisfaction. Quality can be improved by reducing product variability. On the other hand, uncertainty or subjectivity is a part of many engineering and real life problems. However, these problems cannot be solved by traditional methods. This study focuses on constructing Xbar and S control charts in fuzzy environment. The approach is developed by considering the theoretical structure of the Shewhart control charts. The core of the approach depends on the combination of parametric interval estimation and fuzzy statistics. Control limits and samples are presented by fuzzy numbers which ensures to maintain fuzziness in control charts. An important property of the approach is that the fuzzy charts can be reduced to Shewhart control charts. A simulation study was conducted for the performance evaluation of fuzzy Xbar and S control charts. The proposed fuzzy control chart is sensitive to process mean shifts and variance changes, and outperforms the traditional control charts under the changes of variance. In addition, an example from the literature shows that the approach is an effective way of presenting fuzziness in the quality characteristics, which enables the approach to have high applicability to the real life problems.https://dergipark.org.tr/en/download/article-file/1615124confidence intervalsfuzzy numbersfuzzy set theoryfuzzy statisticsquality control chartsbulanık sayılarbulanık küme teorisibulanık istatistikkalite kontrol şemalarıi̇statistiksel aralıklar |
| spellingShingle | Nilufer Pekin Alakoc Fuzzy Xbar and S Control Charts Based on Confidence Intervals Journal of Advanced Research in Natural and Applied Sciences confidence intervals fuzzy numbers fuzzy set theory fuzzy statistics quality control charts bulanık sayılar bulanık küme teorisi bulanık istatistik kalite kontrol şemaları i̇statistiksel aralıklar |
| title | Fuzzy Xbar and S Control Charts Based on Confidence Intervals |
| title_full | Fuzzy Xbar and S Control Charts Based on Confidence Intervals |
| title_fullStr | Fuzzy Xbar and S Control Charts Based on Confidence Intervals |
| title_full_unstemmed | Fuzzy Xbar and S Control Charts Based on Confidence Intervals |
| title_short | Fuzzy Xbar and S Control Charts Based on Confidence Intervals |
| title_sort | fuzzy xbar and s control charts based on confidence intervals |
| topic | confidence intervals fuzzy numbers fuzzy set theory fuzzy statistics quality control charts bulanık sayılar bulanık küme teorisi bulanık istatistik kalite kontrol şemaları i̇statistiksel aralıklar |
| url | https://dergipark.org.tr/en/download/article-file/1615124 |
| work_keys_str_mv | AT niluferpekinalakoc fuzzyxbarandscontrolchartsbasedonconfidenceintervals |