A framework of risk response strategy selection considering the loss caused by risk propagation in the project portfolio

The successful implementation of project portfolio calls for effective risk management, in which how to select appropriate strategies to respond to upcoming risks, is called risk response strategy (RRS) selection. However, two deficiencies are discovered in current RRS selection research. The one is that researchers ignore the phenomenon of risk propagation and the risk losses it brings, which makes some risks with propagation potential underestimated in the process of risk evaluation. The other is that lacking approach to quantify the loss caused by risk propagation, which is not conducive to assessing risks and selecting RRSs from a quantitative perspective. Under this circumstance, this paper firstly proposes a framework of RRS selection considering the loss caused by risk propagation in the project portfolio. In the proposed framework, Bayesian network and fuzzy theory are used to measuring the probability of risk propagation between projects. Subsequently, the propagation process of risks among projects is generated, based on which the probability and time point of loss caused by each risk are calculated. Finally, the losses caused by risk propagation before and after applying risk response strategies are quantified, and an RRS selection model to maximize the recovered risk loss is constructed. The analysis of a case study demonstrates that 1) with the increase of available funds used to respond to risks, the recovered risk loss by each unit of fund declines; 2) before the implementation of projects, the decision-maker should select the RRSs that can recover more direct risk losses; 3) when projects have been implemented, decision-maker needs to focus on the indirect losses caused by risk propagation and select RRSs that can block the propagation of risks in the project portfolio.