An Improved Approach to Technology Portfolio Prioritization Process Under Uncertainty

Abstract

Accurate technology modeling is a challenge, especially when it comes to revolutionary concepts. Absence of historical trends and experimental data for these concepts make it harder to predict precise effects. This situation makes it imperative to make use of subject area expert elicitation. This knowledge generally comes with subjective opinion about impact of technology on performance and market related metrics. These opinions from multiple subject matter experts may vary depending upon their past experiences and personalized preferences. In order to cater to difference of opinion from experts, uncertainty quantification on these inputs and its propagation to the performance and marketing metrics is very important. In addition to input uncertainties, technology interactions play a vital role when multiple technologies are selected simultaneously. There are some processes already in practice to deal with these interactions. These interactions and incompatibilities are currently modeled through technology impact matrices (TIM) and technology compatibility matrices (TCM). It however requires some further refinement. Generally these processes assume the impact of these technologies to be additive when portfolio of technologies are applied. In reality, these technologies are not additive in nature. This problem is addressed through introduction of Technology Synergy Matrices (TSM). In this paper, an evidence theory based TIM and TSM process is demonstrated within the context of an aircraft engine design problem. A representative set of candidate technologies and impacts are provided as examples. Once a combination of technologies is selected, an uncertainty propagation approach is used to evaluate the range of potential effects of the system. In the end, results are compared with those obtained from deterministic approach. The TSM, when used in conjunction with TIM, offers more accurate quantification of technology interactions and allow for technology nonlinearities. At the same time, uncertainty quantification enables the designer to capture the probabilistic Pareto-frontiers that allow the designer to select robust portfolio of technologies. This eliminates unnecessary assumptions while constructing deterministic TIM. Comparison of results from proposed methodologies with deterministic approach shows the design space previously unexplored due to limitations of existing practices.