Managing the public sector research & development portfolio selection process: a case study of quantitative selection and optimization

Abstract

This thesis presents a detailed process and model describing how public sector organizations can implement a research and development (R&D) portfolio optimization strategy to maximize the cost-adjusted benefit metric of a portfolio while simultaneously seeking to maintain and improve a national strategic technologic advantage. The model is applied to an R&D dataset from the FY 17 Naval Research Program (NRP) at the Naval Postgraduate School. The process presented follows a framework incorporating proposal filtering for initial selectivity, proposal weighting based on defined criteria and alignment with the organization's mission and purpose, proposal value and risk determinations, and concludes with portfolio optimization. The optimization's objective function sought to maximize the sum of a portfolio's cost-adjusted benefit calculation, subject to remaining within the NRP's research and development budget. The model effectively resulted in predominantly selecting proposals with medium, high and very high probabilities of success in the risk category and valuations predominantly in the medium, high and very high range. The completion of this thesis has provided a new perspective on R&D selection strategies for public sector investment and highlighted the challenges of placing a value on a public sector R&D proposal.