Definition:
A systematic process of evaluating uncertainties in project estimating using probability distributions and scenario modeling.
Key Components:
- Risk Probability Distributions: Assigning likelihoods to different project risks.
- Monte Carlo Simulation: Running multiple iterations to predict potential outcomes.
- Contingency Planning: Establishing risk buffers based on probability analysis.
Use Cases/Industries:
- Energy Infrastructure Projects: Assessing cost overrun risks in refinery development.
- Construction Planning: Forecasting schedule delays in large-scale projects.
- Aerospace & Defense: Evaluating risk exposure in high-cost technological developments.
Advantages:
- Provides a Range of Possible Outcomes: More accurate than traditional deterministic estimates.
- Enhances Decision-Making: Helps stakeholders plan for uncertainty.
Challenges:
- Computational Complexity: Requires specialized software and expertise.
- Data Sensitivity: Highly dependent on accurate input values.
Related Terms:
Risk-Based Estimating, Scenario Analysis, Sensitivity Testing
Example:
A nuclear power plant conducts a probabilistic risk assessment, identifying potential delays and cost escalations due to supply chain disruptions.
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Synonyms:
Risk Probability Modeling, Stochastic Analysis, Monte Carlo Simulation
