Definition:
A data-driven approach to risk assessment that uses numerical methods and probabilistic models to evaluate potential impacts.
Key Components:
- Probability Distributions: Assigns probabilities to different risk outcomes.
- Monte Carlo Simulation: Uses random sampling to predict potential project results.
- Expected Monetary Value (EMV): Calculates financial exposure to risks.
Use Cases/Industries:
- Energy Infrastructure: Evaluating cost overruns in large-scale projects.
- Aerospace & Defense: Assessing the risk of delays in satellite launches.
- Finance: Determining the probability of market downturns affecting investments.
Advantages:
- Provides Objective Insights: Uses statistical methods for precise risk evaluations.
- Improves Budget Planning: Helps forecast financial exposure to risks.
Challenges:
- Requires Data Accuracy: High-quality input data is necessary for meaningful results.
- Complex Modeling Needed: Can be computationally intensive.
Related Terms:
Monte Carlo Simulation, Probabilistic Modeling, Data-Driven Decision Making
Example:
An offshore wind energy project used quantitative risk analysis to estimate the probability of supply chain disruptions delaying installation.
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Synonyms:
Numerical Risk Assessment, Statistical Risk Modeling, Data-Driven Risk Analysis
