Interval-valued Uncertainty Capability Indices with South African Industrial Applications

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dc.contributor.advisorGuo, Renkuanen_ZA
dc.contributor.authorGyekye, Kwame Boakyeen_ZA
dc.date.accessioned2015-05-28T04:11:46Z
dc.date.available2015-05-28T04:11:46Z
dc.date.issued2014en_ZA
dc.descriptionIncludes bibliographical references.en_ZA
dc.description.abstractSince the advent of statistical quality control and process capability analysis, its study and application has gained tremendous attention both in academia and industry. This attention is due to its ability to describe the capability of a complex process adequately, simply (i.e. using a unitless index) and also in some instances to compare different manufacturing processes. However, the application of statistical quality control has come under intense criticism, notably in one car manufacturing industry where the actual number of non-conforming units considerably exceeded expectation, although probabilistic control measures were in place. This failure led to a large recall of their vehicles and also left a dent on the image of the company. One of the reasons for this unfortunate instance is that in classical quality control measures, human judgement is ignored and since in process engineering there is considerable expert intuition in decision making, this element cannot be undermined. Hence the research study applies the uncertainty theory proposed by Baoding Liu (2007) to enable us to incorporate human judgement into process capability analysis. The major findings of the thesis is that the uncertain process capability indices under an uncertainty environment are interval-valued and their relevant characteristics. The study further developed the "sampling" uncertainty distributions and thus the "sampling" impacts on the newly defined uncertain process capability indices under Liu's uncertain normal distribution assumptions. In order to reach the main purpose of the thesis, a thoroughgoing literature review on probabilistic process capability indices is necessary.en_ZA
dc.identifier.apacitationGyekye, K. B. (2014). <i>Interval-valued Uncertainty Capability Indices with South African Industrial Applications</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Statistical Sciences. Retrieved from http://hdl.handle.net/11427/12947en_ZA
dc.identifier.chicagocitationGyekye, Kwame Boakye. <i>"Interval-valued Uncertainty Capability Indices with South African Industrial Applications."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2014. http://hdl.handle.net/11427/12947en_ZA
dc.identifier.citationGyekye, K. 2014. Interval-valued Uncertainty Capability Indices with South African Industrial Applications. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Gyekye, Kwame Boakye AB - Since the advent of statistical quality control and process capability analysis, its study and application has gained tremendous attention both in academia and industry. This attention is due to its ability to describe the capability of a complex process adequately, simply (i.e. using a unitless index) and also in some instances to compare different manufacturing processes. However, the application of statistical quality control has come under intense criticism, notably in one car manufacturing industry where the actual number of non-conforming units considerably exceeded expectation, although probabilistic control measures were in place. This failure led to a large recall of their vehicles and also left a dent on the image of the company. One of the reasons for this unfortunate instance is that in classical quality control measures, human judgement is ignored and since in process engineering there is considerable expert intuition in decision making, this element cannot be undermined. Hence the research study applies the uncertainty theory proposed by Baoding Liu (2007) to enable us to incorporate human judgement into process capability analysis. The major findings of the thesis is that the uncertain process capability indices under an uncertainty environment are interval-valued and their relevant characteristics. The study further developed the "sampling" uncertainty distributions and thus the "sampling" impacts on the newly defined uncertain process capability indices under Liu's uncertain normal distribution assumptions. In order to reach the main purpose of the thesis, a thoroughgoing literature review on probabilistic process capability indices is necessary. DA - 2014 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2014 T1 - Interval-valued Uncertainty Capability Indices with South African Industrial Applications TI - Interval-valued Uncertainty Capability Indices with South African Industrial Applications UR - http://hdl.handle.net/11427/12947 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/12947
dc.identifier.vancouvercitationGyekye KB. Interval-valued Uncertainty Capability Indices with South African Industrial Applications. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Statistical Sciences, 2014 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/12947en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Statistical Sciencesen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherStatistical Sciencesen_ZA
dc.titleInterval-valued Uncertainty Capability Indices with South African Industrial Applicationsen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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