π-Economy (Jun 2022)
Integrated matric of risk measurement of R&D projects under conditions of uncertainty
Abstract
R&D projects are not only expensive, but also usually have a long implementation period, since they consist of various stages (phases). In addition, this type of scientific and technical activity is characterized by economic uncertainty and a high probability of obtaining unpredictable results and unforeseen losses. Thus, the implementation of R&D projects is associated with a high level of risk, and as a result the decision to implement such projects requires serious preliminary analysis and calculations. The purpose of this study is the process of risk assessment of R&D projects using the concept of CorporateMetrics. The value of CfaR (cash flow at risk) was chosen as a risk assessment measure: a quantitative assessment of the impact of external and internal risk factors on the cash flows of an R&D project under conditions of uncertainty. In the process of CFaR computational procedures, as a rule, the following tasks are solved: identification of risk factors that affect the cash flows of the project; determination of probabilistic distributions of their possible values for each risk factor; development of a financial model showing how one or another factor affects the cash flow; determination of probabilistic distributions of possible changes in cash flow. Thus, the CFaR indicator allows estimating the amount of the maximum possible losses on a certain planning horizon with a certain level of probability. Using CFaR, project management evaluates the negative change in projected cash flows from actual ones under the influence of various risk factors (inflation, demand, cost of resources, exchange rates, etc.). The result of the study is a description of the full cycle of the R&D project of a machine-building enterprise. A classification of R&D risks by cycle stages is proposed. A matrix of methods (measures) for assessing R&D risks has been developed. An algorithm for determining an integrated risk measure using the Monte Carlo method is proposed. A practical example of calculating the CFaR of a R&D project of a machine-building industry enterprise is considered. The resulting risk metric can be used to design integrated risk management systems of a high-tech enterprise, as well as to develop strategies for eliminating R&D risks in the context of digital transformation.
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