Interval-valued Uncertainty Capability Indices with South African Industrial Applications

 

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dc.contributor.advisor Guo, Renkuan en_ZA
dc.contributor.author Gyekye, Kwame Boakye en_ZA
dc.date.accessioned 2015-05-28T04:11:46Z
dc.date.available 2015-05-28T04:11:46Z
dc.date.issued 2014 en_ZA
dc.identifier.citation Gyekye, K. 2014. Interval-valued Uncertainty Capability Indices with South African Industrial Applications. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/12947
dc.description Includes bibliographical references. en_ZA
dc.description.abstract 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. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Statistical Sciences en_ZA
dc.title Interval-valued Uncertainty Capability Indices with South African Industrial Applications en_ZA
dc.type Master Thesis
uct.type.publication Research en_ZA
uct.type.resource Thesis en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Science en_ZA
dc.publisher.department Department of Statistical Sciences en_ZA
dc.type.qualificationlevel Masters
dc.type.qualificationname MSc en_ZA
uct.type.filetype Text
uct.type.filetype Image
dc.identifier.apacitation Gyekye, 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/11427/12947 en_ZA
dc.identifier.chicagocitation Gyekye, 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/11427/12947 en_ZA
dc.identifier.vancouvercitation Gyekye 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/11427/12947 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


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