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Analytical Effectiveness of Mathematical Models for R&D Project Selection

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  • William E. Souder

    (University of Pittsburgh)

Abstract

Four mathematical programming models for R&D project selection and funding are developed, based on similar models and results in the literature. Project selection and funding decision planning data for thirty actual R&D projects are collected and the models are used with these data to ascertain which of the available projects to select and how much to spend on them. The portfolios prescribed by each model are compared with a prorata benchmark allocation, an ex post optimum allocation based on the success/fail project outcomes and the allocations actually used by management. The results indicate that these models may be effective decision aids when used at appropriate project life cycle periods, with the choice of the particular model form being influenced by the manager's objectives and viewpoints of the project selection problem.

Suggested Citation

  • William E. Souder, 1973. "Analytical Effectiveness of Mathematical Models for R&D Project Selection," Management Science, INFORMS, vol. 19(8), pages 907-923, April.
    • Handle: RePEc:inm:ormnsc:v:19:y:1973:i:8:p:907-923
    DOI: 10.1287/mnsc.19.8.907
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    Cited by:

    1. Scobie, Grant M., 1984. "Investment in Agricultural Research: Some Economic Principles," Economics Working Papers 232447, CIMMYT: International Maize and Wheat Improvement Center.
    2. Christoph H. Loch & Stylianos Kavadias, 2002. "Dynamic Portfolio Selection of NPD Programs Using Marginal Returns," Management Science, INFORMS, vol. 48(10), pages 1227-1241, October.
    3. Oral, Muhittin & Kettani, Ossama & Cinar, Unver, 2001. "Project evaluation and selection in a network of collaboration: A consensual disaggregation multi-criterion approach," European Journal of Operational Research, Elsevier, vol. 130(2), pages 332-346, April.
    4. Sophie Hooge & Armand Hatchuel, 2008. "Value indicators and monitoring in innovative PDM: A grounded approach," Post-Print hal-00696974, HAL.
    5. Seifert, Ralf W. & Tancrez, Jean-Sébastien & Biçer, Işık, 2016. "Dynamic product portfolio management with life cycle considerations," International Journal of Production Economics, Elsevier, vol. 171(P1), pages 71-83.
    6. Ringuest, Jeffrey L. & Graves, Samuel B. & Case, Randy H., 2004. "Mean-Gini analysis in R&D portfolio selection," European Journal of Operational Research, Elsevier, vol. 154(1), pages 157-169, April.

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