dc.contributor.authorWithanage, Chathura
dc.date.accessioned2013-07-31T09:00:31Z
dc.date.accessioned2017-07-23T08:40:46Z
dc.date.available2013-07-31T09:00:31Z
dc.date.available2017-07-23T08:40:46Z
dc.date.copyright2013en_US
dc.date.issued2013
dc.identifier.citationWithanage, C. (2013). A value driven decision support framework for the front-end product design and development. Doctoral thesis, Nanyang Technological University, Singapore.
dc.identifier.urihttp://hdl.handle.net/10356/54748
dc.description.abstractValue-attribute models are used at the front-end phase of the customer driven product design to support the design decision making by eliciting customer preferences. Currently used models are not capable of handling the high dimensional technological product attribute data, plagued by the multicollinearity, with limited observations. Furthermore, they are not equipped to deal with the heteroscedasticity and the dynamic nature of the technological product market systems. As a result, especially, the technological products with longer design cycle times are heavily affected by model uncertainties coming in various faces. Robust, dynamic, value-attribute models are needed to accurately replicate the market systems, to overcome the deficiencies of the data, and ultimately, to predict the future product values for the front-end concept screening. Therefore, a strategic decision support framework is proposed in this thesis to integrate the hitherto overlooked time variant properties of preferences, into the front-end design decision making process. In the proposed framework, the Partial Least Squares Regression and Path Modelling techniques, robust soft modeling methods, are used as the main decision support tools. And, Customer Revealed Value, a perceived value estimation obtained from a demand-price analysis, is used as the design objective. There are four main contributions in this thesis. Firstly, a theoretical basis is provided for the multivariate modeling of the value-attribute relationship. Secondly, a robust Partial Least Squares algorithm is introduced to handle the heteroscedasticity presence in the market systems. Thirdly, a dynamic value-attribute model is formulated by combining Partial Least Squares and Time Series Forecasting techniques. Finally, a dynamic value-characteristic model is formulated by extending the earlier model by including higher or system level product characteristics, using Partial Least Squares Path Modeling. All the contributions are validated using the US automobile market data. And, the results of the case studies depict the potential of the framework as a design decision support method.en_US
dc.format.extent161 p.en_US
dc.language.isoenen_US
dc.subjectDRNTU::Engineering::Systems engineeringen_US
dc.subjectDRNTU::Engineering::Manufacturing::Product designen_US
dc.subjectDRNTU::Business::Marketing::Consumer behavioren_US
dc.subjectDRNTU::Engineering::Industrial engineering::Engineering managementen_US
dc.titleA value driven decision support framework for the front-end product design and developmenten_US
dc.typeThesis
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.contributor.supervisorPark Taezoon
dc.contributor.supervisorTruong Ton Hien Duc
dc.description.degreeDOCTOR OF PHILOSOPHY (MAE)en_US
dc.contributor.organizationDivision of Systems and Engineering Managementen_US


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