High-level Overview of Project Risk Management (PRM)

Selection of adequate project risk management (PRM) methodologies should be based on deep understanding of PRM context of a project to avoid PRM context—PRM method mismatch. Various realizations of bias can impede the selection. All PRM methodologies can be grouped as either conventional (they follow traditional PRM process steps to identify, evaluate, address, monitor, etc.) and unconventional (they are focused mostly at risk assessments). All PRM methodologies—deterministic (scoring), probabilistic (Monte Carlo), parametric, etc—have limitations. Deterministic (scoring) methods can help with development of risk addressing although they are useless for development of project contingencies. Monte Carlo methods can be used for development of project contingencies only if they take into account all relevant components of project risk exposure consistently. Parametric methods suffer from using biased sampling—convenience and judgement sampling—that undermine their accuracy. Two emerging methodologies—system dynamics and artificial neural networks (ANN)—can be considered unconventional.