Rosinger, W. (2013). A portfolio approach for reducing fuzziness in new product development [Master Thesis, Technische Universität Wien]. reposiTUm. http://hdl.handle.net/20.500.12708/159202
"Economic conditions indicate the rising importance of product innovation for a sustainable profitable company. However, average failure rates of above 50% of newly introduced products emphasize the inherent risks and, therefore, the necessity of establishing robust methods to enhance the chance to succeed. It is said that the most important activities, those in which the difference between successes and failures are the greatest, are the early activities in the product development process. This is exactly where this Master Thesis makes a contribution; it provides a framework for product idea evaluation and prioritization in order to bridge the gap between the idea generation activities and the structured development process of an automotive company. A new process model is developed by considering the characteristics of the early phase of product development, the so-called fuzzy front end, as well as the results of a literature research on established portfolio methods. The new framework introduces a three step portfolio process in order to achieve a high-value, balanced as well as strategically aligned portfolio of innovation projects simultaneously. The designed framework is validated and optimized within a pilot project where 138 product ideas are systematically screened and rated. The newly developed process model enables and accelerates decision-making by providing a well-structured process and by encouraging purposeful discussions. Furthermore, it is affirmed that the introduction of the proposed portfolio approach supports not only the development activities in the fuzzy front end stage, but evaluation and tracking of the entire product portfolio - especially with regard to aspects of strategic alignment, risk minimization and value maximization. In short, this Master Thesis introduces a framework in new product development for reducing uncertainty and ambiguity - which in this context is generally referred to as fuzziness."