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Computational Analysis of Scoring Models for R and D Project Selection

Author

Listed:
  • John R. Moore, Jr.

    (Krannert School of Industrial Administration, Purdue University. Dr. Moore is now with the Graduate School of Business, Stanford University)

  • Norman R. Baker

    (School of Industrial Engineering, Purdue. Dr. Baker is now with the School of Industrial Engineering, Georgia Institute of Technology.)

Abstract

Several authors have proposed using scoring models for prescriptive analysis of the R and D project selection decision problem. This research indicates that these models do not meet with important practical requirements. For example, many authors recommend a multiplicative index, over an additive index, in order to generate a wide range of project scores. The additive index is shown to have important advantages over the multiplicative index. The most serious shortcoming in the models, however, is the relatively arbitrary fashion in which the models have been constructed and the failure of the model builders to recognize the impact of certain structural considerations on resulting project scores. Comparative analyses relating project rankings produced by scoring models to rankings produced by a profitability index and by a linear programming model demonstrate that the performance of a scoring model is highly sensitive to decisions made during the development of the model. Considerations such as (1) the underlying distributions of project data, (2) time preferences, (3) the number of ranking intervals or categories, and (4) the width of the intervals, all have important implications for final project scores and associated rankings.

Suggested Citation

  • John R. Moore, Jr. & Norman R. Baker, 1969. "Computational Analysis of Scoring Models for R and D Project Selection," Management Science, INFORMS, vol. 16(4), pages 212-232, December.
    • Handle: RePEc:inm:ormnsc:v:16:y:1969:i:4:p:b212-b232
    DOI: 10.1287/mnsc.16.4.B212
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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. Wang, Jue & Xu, Wei & Ma, Jian & Wang, Shouyang, 2013. "A vague set based decision support approach for evaluating research funding programs," European Journal of Operational Research, Elsevier, vol. 230(3), pages 656-665.
    3. Alexander Tkotz & Jan Christoph Munck & Andreas Erich Wald, 2018. "Innovation Management Control: Bibliometric Analysis Of Its Emergence And Evolution As A Research Field," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1-34, April.
    4. Greig, I.D., 1981. "Agricultural Research Management and the Ex Ante Evaluation of Research Proposals : A Review," Review of Marketing and Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 49(02), pages 1-22, August.

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