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.
Publisher: Springer-Verlag New York Inc.
Number of pages: 145
Weight: 430 g
Dimensions: 235 x 155 x 11 mm
Edition: 2004 ed.
From the reviews:
"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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