Risk Response Strategies Selection over the Life Cycle of Project Portfolio

Abstract

The successful implementation of project portfolios (PP) calls for effective risk management, in which selecting optimal risk response strategies help to reduce the impact of risk. Project portfolio risks (PPRs) exhibit causality and time dependency over the life cycle, which result in cumulative effects over time. By accounting for these risk correlations, risk response could be more effective in reducing expected losses than risk independence assumption. To support effective and sustainable risk management, this study proposes a novel risk response method that integrates the dynamic Bayesian network (DBN) model and reward–risk optimization model to select risk response strategies for different stages of the PP life cycle. The proposed method supports a more comprehensive analysis of risk contagion paths by opening the black box of the risk propagation paths during the PP life cycle. In this method, the PPRs, as the DBN nodes, are first identified, considering the project’s interdependency. Second, DBN analysis is used to assess PPRs by visually modeling the causality and life cycle correlation among risks. Then, the reward–risk optimization model is built to determine risk response strategies for each stage of the life cycle under the constraints. Finally, the proposed method selects risk response strategies for different stages of the PP life cycle. The findings reveal that the risk response effects are maximized if the risks are responded to in earlier stages. Moreover, the findings contribute to helping managers choose the optimal risk response strategies consistent with the risk response budget. As the effect of the strategy depends on the actual situation of the PP, the factors affecting the response effect of the strategies are recommended for further study.