Quantitative Risk Analysis

Quantitative Risk Analysis is a process that numerically analyzes the probability and impact of identified risks on project objectives. It provides a quantitative approach to decision-making under uncertainty. In this analysis, numerical values are assigned to both probability and impact. Probability represents the likelihood of a risk occurring (expressed as a percentage or decimal), while impact quantifies the effect on project objectives (typically in terms of cost, schedule, or other measurable parameters). Key techniques include: 1. Expected Monetary Value (EMV) Analysis: Calculates the average outcome by multiplying the probability by the impact for each risk and summing the results. 2. Monte Carlo Simulation: Uses computer models to simulate project outcomes thousands of times, considering risk probabilities and impacts to show the range of possible results. 3. Sensitivity Analysis: Determines which risks have the most potential impact by examining how variations in risk parameters affect project outcomes. 4. Decision Tree Analysis: Maps possible decisions and outcomes to calculate EMV and identify optimal paths. 5. Cost Risk Analysis: Focuses specifically on how risks might affect project costs. 6. Schedule Risk Analysis: Evaluates potential schedule impacts using techniques like PERT (Program Evaluation and Review Technique). Quantitative Risk Analysis produces tangible outputs like: - Probability distributions of project outcomes - Confidence levels for meeting objectives - Prioritized risk lists based on numerical impact - Contingency reserves recommendations (time and cost) This analysis requires quality data, mathematical models, and often specialized software. It's typically performed on high-priority risks identified during Qualitative Risk Analysis or for projects requiring precise risk estimates. The results enable project managers to make data-driven decisions about risk responses, contingency planning, and resource allocation.

Quantitative Risk Analysis is a process that numerically analyzes the probability and impact of identified risks on project objectives. It provides a quantitative approach to decision-making under un…

Concepts covered: Probability Distributions, Monte Carlo Simulation, Expected Monetary Value (EMV) Analysis, Sensitivity Analysis, Influence Diagrams, Fault Tree Analysis, Program Evaluation and Review Technique (PERT), Correlation and Dependency Modeling, Decision Tree Analysis