portfolio planning
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In research portfolio planning contexts, an estimate of research policy and project synergies/tradeoffs (i.e. covariances) is essential to the optimal leveraging of institution resources. The data by which to make such estimates generally do not exist. Research institutions may often draw on domain expertise to fill this gap, but it is not clear how such ad hoc information can be quantified and fed into an optimal resource allocation workflow. Drawing on principal components analysis, I propose a method for “reverse engineering” synergies/tradeoffs from domain expertise at both the policy and project level. I discuss extensions to other problems and detail how the method can be fed into a research portfolio optimization workflow. I also briefly discuss the relevance of the proposed method in the context of the currently toxic relations between research communities and the donors that fund them.

AbstractInnovation portfolio management (IPM) aims at selecting ideas with regard to their potential for innovation and measuring them considering customer and business value. The evaluation of benefits and risk is especially challenging for disruptive innovation (DI) due to their characteristics such as low comparability to existing technologies and uncertain customer reactions. This paper highlights the lack of approaches to managing DI in IPM and addresses it through a framework that expands the understanding of value-orientation in IPM, allowing for the inclusion of DI.

Technology commercialization needs a large amount of financial resources and governments in developed and developing countries play a critical role in resource allocation to the technology commercialization, especially through “Technology Development Funds (TDFs)”. But, because of resource limitations, determining high priority technologies with higher impact on the country’s innovative performance and the optimal resource allocation to technology development is very important for science and technology policymakers. “Technology portfolio planning” has been developed and applied in this regard. Accordingly, a two-phase decision-making framework has been proposed. At the first phase, the priorities of technology fields are determined by using the best-worst method (BWM) and at the second phase, a two-stage stochastic technology portfolio planning model is developed by considering technological projects' risks and export market, as one of the important factor in the “Global Innovation Index” (GII) ranking. It also has been considered technology fields’ priorities, staged-financing, moratorium period, reinvestment strategy, and technology readiness levels (TRL) in allocating financial resources to technological projects. The main advantages of our proposed model are considering uncertainty and early signaling about underperforming technological projects. Due to the uncertain nature of the problem, our solution methodology is based on the Sample Average Approximation (SAA). In order to demonstrate the applicability of this model, a real case study and its computational results are presented.