Analytical Effectiveness of Mathematical Models for R&D Project Selection

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.

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