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dc.contributor.authorVan Horn, David
dc.date.accessioned2016-04-05T19:12:34Z
dc.date.available2016-04-05T19:12:34Z
dc.date.issued2013
dc.identifier.isbn9781267946935
dc.identifier.other1319305599
dc.identifier.urihttp://hdl.handle.net/10477/50457
dc.description.abstractThe evolution of design thinking has seen numerous challenges and advances in transforming information into knowledge for engineers to design systems, products, and processes. These transformations occur in three stages throughout a design process. In simple form, the early, middle, and late stages of a design process serve to develop an understanding of the customer's needs, arrive at the final concept of the design, and analyze and support the performance and usage profile of the deployed product, respectively. The quality and accuracy of the input information and the effectiveness of each transformation determine the success or failure of the product. Capturing good information and converting it to knowledge are two important tasks that have motivated a long history of research in design processes and tools. In this thesis, Design Analytics (DA) is proposed as a new paradigm for significantly enhancing the core information-to-knowledge transformations. The overall aim is to capture, store, and leverage digital information about artifacts, their performance, and their usage. The information is transformed into knowledge in each of the three stages using various analytics and cyber-enabled tools such as design repositories and concept generators. The ultimate result is better performing and functioning products. As web analytics has transformed how companies interact with consumers on the internet, DA is expected to transform how companies design products with and for consumers. An illustrative case study is performed to demonstrate some of the foundations of DA in the redesign of a refrigerator. Then, a second case study is performed using a tool to simulate refrigerator usage and investigate the challenges of analyzing a large, artificial data set when the underlying customer preferences are unknown prior to redesign.
dc.languageEnglish
dc.sourceDissertations & Theses @ SUNY Buffalo,ProQuest Dissertations & Theses Global
dc.subjectApplied sciences
dc.subjectAnalytics
dc.subjectConsumer product
dc.subjectCustomer preference
dc.subjectData
dc.subjectDesign
dc.subjectUsage profile
dc.titleDesign Analytics: Capturing, Understanding, and Meeting Customer Needs Using Product Usage Data
dc.typeDissertation/Thesis


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