Methodologies designed to support new product development are receiving increasing interest in recent literature. A significant percentage of new product failure is attributed to a mismatch between designed product features and consumer liking. A variety of methodologies have been proposed and tested for consumer liking or preference prediction, ranging from statistical methodologies e.g. multiple linear regression (MLR) to non-statistical approaches e.g. artificial neural networks (ANN), support vector machines (SVM), and belief rule-based (BRB) systems. BRB has been previously tested for consumer preference prediction and target setting in case studies from the beverages industry. Results have indicated a number of technical and conceptual advantages which BRB holds over the aforementioned alternative approaches. This thesis focuses on presenting further advantages and applications of the BRB methodology for consumer liking prediction. The features and advantages are selected in response to challenges raised by three addressed case studies. The first case study addresses a novel industry for BRB application: the fast moving consumer goods industry, the personal care sector. A series of challenges are tackled. Firstly, stepwise linear regression, principal component analysis and AutoEncoder are tested for predictors’ selection and data reduction. Secondly, an investigation is carried out to analyse the impact of employing complete distributions, instead of averages, for sensory attributes. Moreover, the effect of modelling instrumental measurement error is assessed. The second case study addresses a different product from the personal care sector. A bi-objective prescriptive approach for BRB model structure selection and validation is proposed and tested. Genetic Algorithms and Simulated Annealing are benchmarked against complete enumeration for searching the model structures. A novel criterion based on an adjusted Akaike Information Criterion is designed for identifying the optimal model structure from the Pareto frontier based on two objectives: model complexity and model fit. The third case study introduces yet another novel industry for BRB application: the pastry and confectionary specialties industry. A new prescriptive framework, for rule validation and random training set allocation, is designed and tested. In all case studies, the BRB methodology is compared with the most popular alternative approaches: MLR, ANN, and SVM. The results indicate that BRB outperforms these methodologies both conceptually and in terms of prediction accuracy.

Application of supply chain management is still relatively recent practice, but it's importance is growing over time. Today, in the era of customer centricity, in order to become competitive firms shouldn't avoid collaboration with it's supply chain partners. Purpose of my thesis was to find out how supply chain management can contribute to building relationship with customers, who are the basis of any existing company. First part summarizes knowledge about supply chain, compeptitive strategies, customers, types of supply chains and customer relationship management. Particular part is dedicated to customer service and how supply chain management can contribute to it's improving. It describes also what meaning has customer service for future competitiveness. In second part, I presented the case of Zappos.com. E-retailer, which aligning entire functional strategies and whole supply chain to common strategic purpose managed to build excellent relationship with it's customers.

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The aim of the diploma´s thesis is analysis of the scrap of the chosen process, using the tools of statistical process control and proposal of appropriate containment to reduce the scrap. The thesis describes all seven basic tools of quality and the greatest attention is aimed to Shewart's control charts. One chapter discusses three methods of problem solving in technical practice, while one of the described methods is used in the practical part of the thesis.

The master´s thesis contains a statistical analysis of a company financial risk. Within this analysis were determined the dominant economic and financial risk factors of a company and was made a description of selected inferential statistical methods adequate assessment of the state and time development of these indicators. On a PC was applied methodics developed for concrete data files with focusing on description of the expected risk development existence of a company. In conclusion were achievements evaluated and were determined another possible directions of solving similar problems.

Department of Statistics / MSc (Statistics) / The ripple e ect of a stock market crash due to extremal dependence is a global issue with key attention and it is at the core of all modelling e orts in risk management. Two methods of extreme value theory (EVT) were used in this study to model equity risk and extremal dependence in the tails of stock market indices from four African emerging markets: South Africa, Nigeria, Kenya and Egypt. The rst is the \bivariate-threshold-excess model" and the second is the \point process approach". With regards to the univariate analysis, the rst nding in the study shows in descending hierarchy that volatility with persistence is highest in the South African market, followed by Egyptian market, then Nigerian market and lastly, the Kenyan equity market. In terms of risk hierarchy, the Egyptian EGX 30 market is the most risk-prone, followed by the South African JSE-ALSI market, then the Nigerian NIGALSH market and the least risky is the Kenyan NSE 20 market. It is therefore concluded that risk is not a brainchild of volatility in these markets. For the bivariate modelling, the extremal dependence ndings indicate that the African continent regional equity markets present a huge investment platform for investors and traders, and o er tremendous opportunity for portfolio diversi cation and investment synergies between markets. These synergistic opportunities are due to the markets being asymptotic (extremal) independent or (very) weak asymptotic dependent and negatively dependent. This outcome is consistent with the ndings of Alagidede (2008) who analysed these same markets using co-integration analysis. The bivariate-threshold-excess and point process models are appropriate for modelling the markets' risks. For modelling the extremal dependence however, given the same marginal threshold quantile, the point process has more access to the extreme observations due to its wider sphere of coverage than the bivariate-threshold-excess model. / NRF

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<p dir="ltr">In our daily lives, we rely on a multitude of discrete products to meet our needs. Traditional product design approaches have primarily focused on economic and technical aspects, often overlooking the pressing environmental and social challenges facing society. Recognizing the limitations of our ecological systems to cope with the waste generated by our current industrial processes, there is a growing need to anticipate the potential consequences of product design across technical, economic, environmental, and social dimensions to pave the way for a sustainable future. One promising strategy within this context is the integration of sustainability principles into optimization-based design models that consider a product's entire life cycle. While there have been previous efforts to optimize product life cycles, a comprehensive exploration of optimization-based design methods with a focus on multiple objectives for discrete products is essential. This dissertation explores the integration of sustainability principles with optimization-based design by taking the electric traction motor used in electric vehicles as a case study. This complex and environmentally significant technology is ideal for investigating the tradeoffs and benefits of incorporating sustainability objectives into the design process.</p><p dir="ltr">The key tasks undertaken in this study are as follows:</p><ul><li>Development of a parametric design and optimization framework for a surface-mounted permanent magnet synchronous motor. In this task, a special emphasis is placed on reducing reliance on materials with a high supply risk, such as rare earth elements.</li><li>Creation of a parametric life cycle assessment model that combines life cycle assessment and optimization-based design to minimize a single-score environmental impact. This model offers insights into the environmental performance of product design and underscores the importance of minimizing environmental impact throughout a product's life cycle.</li><li>Integration of a life cycle costing model, incorporating techno-economic assessment and total cost of ownership perspectives, into the parametric life cycle assessment and optimization-based design models. This model is used to minimize levelized production and driving costs, shedding light on the trade-offs within this family of cost metrics and the optimization of manufacturing systems for motor production.</li><li>Proposal of a circular economy model/algorithm to assess the advantages of integrating the circular economy paradigm during the early design phase. All the mentioned objective functions are considered to study the impacts of applying the circular economy paradigm.</li></ul><p dir="ltr">The contributions of this research can be summarized as follows:</p><ul><li>Utilized a diverse array of analytical methodologies to parameterize the design process of a motor, incorporating the integration of Life Cycle Assessment (LCA) and Techno-Economic Analysis (TEA) models, as well as the incorporation of disassembly planning for informed decision-making in the early stages of design.</li><li>Proposed a generalized objective function denoted as the Supply Risk-equivalent (SR-eq.), aimed at mitigating the risks associated with the dependency on critical materials in product manufacturing.</li><li>Introduced a novel approach for visualizing non-dominated solutions within a multi-objective framework, with experimentation conducted on up to six distinct objectives.</li><li>Substantiated the significance of decarbonizing the electric grid while maintaining competitive cost structures, the importance of advancing non-destructive evaluation (NDE) procedures for assessing the condition of end-of-life (EoL) subassemblies, and optimizing the collection rate of EoL motors.</li></ul><p dir="ltr">Demonstrated that the optimization of technical metrics as surrogate indicators for economic and environmental performance does not necessarily yield designs that are concurrently optimal in economic and environmental terms.</p>