Neuware - Project Selection Under Uncertainty is the result of a five-year research program on the selection of projects in New Product Development (NPD). Choosing the New Product Development portfolio is of critical importance in today's business environment. The NPD portfolio has considerable strategic effect on the 'middle term' success of a business. This book takes a step in developing a theory that addresses the need for quantitative prioritization criteria within the broader strategic context of the R&D portfolios. Its foundation lies in mathematical theory of resource-constrained optimization with the goal to maximize quantitative returns. The book seeks to broaden the portfolio discussion in two ways. First, simplified models - appropriate for the data-poor NPD context - are developed, which attempt to illuminate the structure of the choice problem and robust qualitative rules of thumb, rather than detailed algorithmic decision support. Such robust rules can be applied in the R&D environment of poor data availability. Second, the annual portfolio review is not the only important choice in resource allocation. In addition, the book discusses how ideas might be pre-screened as they emerge, and how projects should be prioritized once they are funded and ongoing. N� de ref. del art�culo 9781402077036

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T�tulo: Project Selection Under Uncertainty : ...

Editorial: Springer Us Jan 2004

A�o de publicaci�n: 2004

Encuadernaci�n: Buch

Condici�n: Neu

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Project Selection Under Uncertainty is the result of a five-year research program on the selection of projects in New Product Development (NPD). Choosing the New Product Development portfolio is of critical importance in today's business environment. The NPD portfolio has considerable strategic effect on the "middle term" success of a business.
This book takes a step in developing a theory that addresses the need for quantitative prioritization criteria within the broader strategic context of the R&D portfolios. Its foundation lies in mathematical theory of resource-constrained optimization with the goal to maximize quantitative returns. The book seeks to broaden the portfolio discussion in two ways. First, simplified models - appropriate for the data-poor NPD context - are developed, which attempt to illuminate the structure of the choice problem and robust qualitative rules of thumb, rather than detailed algorithmic decision support. Such robust rules can be applied in the R&D environment of poor data availability. Second, the annual portfolio review is not the only important choice in resource allocation. In addition, the book discusses how ideas might be pre-screened as they emerge, and how projects should be prioritized once they are funded and ongoing.

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"This book is rooted in the mathematical theory of resource constrained optimization with the goal of maximization quantitative turns ... . It attempts to broaden the portfolio discussion in two ways. First, simplified models appropriate for a new product development context are described where the lack of precise data is typical. Second, not only the annual portfolio review is discussed, but also what should be done with ideas as they emerge, and how projects should be prioritized once they are funded and ongoing." (Klaus Ehemann, Zentralblatt MATH, Vol. 1056 (7), 2005)

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