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Project selection under uncertainty: dynamically allocating resources to maximize value Kavadias, Stylianos

By: Contributor(s): Material type: TextTextSeries: International Series in Operations Research & Management SciencePublication details: Boston Kluwer Academic Publishers 2004 Description: xi, 145 pISBN:
  • 9781402077036
Subject(s): DDC classification:
  • 658.404
Summary: "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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Item type Current library Call number Status Date due Barcode Item holds
Book Book Ahmedabad 658.404 K2P7 (Browse shelf(Opens below)) Available 164739
Total holds: 0

"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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