ABSTRACT
This chapter provides an overview of advanced topics in project risk management, going beyond conventional risk management practices to enhance project success and performance.
Starting with Root Cause Analysis (RCA) is a fundamental technique used to identify the underlying causes of risks that impact project success.
Quantitative Risk Analysis, using numerical probabilities and impact values, enables the calculation of expected monetary value (EMV) and conducting sensitivity analysis. A key technique within this topic is “Monte Carlo Simulation (MCS),” which models project outcomes through simulations based on probability distributions of uncertain variables.
MCS is a widely used technique in risk management to aids decision-making by modeling and analyzing uncertainties in complex systems.
MCS involves generating random samples for uncertain input variables, each drawn from their respective probability distributions. These samples are then used to simulate the behavior of the system, allowing analysts to observe various potential outcomes based on different combinations of input values.
