All risk management standards, guides and process descriptions note that risk identification is a key component of a robust risk management framework. Further, an effective risk identification process should identify all types of risks from all sources across the entire scope of the program/enterprise activities. However, no document or solution provides sufficient guidance for identifying a comprehensive set of risks – a risk management baseline. Further, risk identification as it is practiced today is a subjective, ad hoc, non-comprehensive and non-repeatable process resulting in continuing failures and overruns in all types of programs.
A large analysis of hundreds of programs, their risks and outcomes was performed to address this shortcoming. A common set of risks emerged from this study, called the Risk Identification Analysis. This analysis, its conclusions, and the tool developed from those conclusions, Program Risk ID, are discussed in this paper.
There are numerous methods dedicated to supporting the decision-making process in the field of road infrastructure management. These methods are very effective in developing economies. In the case of a lack of infrastructure, they enable the determining of the most essential needs, the prediction of the consequences of decisions undertaken and the comparison of possible options. However, in developed economies the level of saturation by road infrastructure is usually high. Existing methods often cannot be effectively employed in order to support the decision-making process because all necessary infrastructure indicated by these methods has already been built. The differences in the consequences of possible decisions are very subtle and it is very difficult to carry out analyses which unequivocally indicates the optimal solutions.
This paper presents the usage of machine learning as a tool in supporting the decision-making process in this area. The employment of machine learning enables predicting the consequences of analyzed decisions in examples where traditional methods do not produce satisfactory results.