Definition:
A risk-informed estimating technique that uses three different cost or schedule estimates—optimistic, pessimistic, and most likely—to calculate a weighted average for project planning.
Key Components:
- Optimistic Estimate (O): Assumes best-case scenario with minimal delays or cost increases.
- Most Likely Estimate (M): Represents the most probable project outcome.
- Pessimistic Estimate (P): Accounts for worst-case scenario with maximum risks realized.
- Weighted Average Calculation: Uses formulas such as (O + 4M + P) / 6 for a probabilistic estimate.
Use Cases/Industries:
- Construction Projects: Forecasting variability in labor and material costs.
- Energy Sector: Assessing risks in fuel price fluctuations for power generation.
- Software Development: Estimating timelines based on past project delays and efficiencies.
Advantages:
- Risk-Adjusted Estimations: Accounts for uncertainty in project execution.
- More Realistic Budgeting: Reduces reliance on overly optimistic or conservative estimates.
- Better Stakeholder Communication: Helps explain cost and schedule variability.
Challenges:
- Requires Multiple Inputs: Needs thorough research to define three distinct estimates.
- Complexity in Application: Can be difficult to integrate into standard budgeting processes.
- Subjectivity in Estimates: Relies on expert judgment for probability weightings.
Related Terms:
Monte Carlo Simulation, Probabilistic Estimating, Risk-Adjusted Forecasting
Example:
A hydroelectric dam project applies three-point estimating to determine total construction costs, factoring in best-case labor efficiency, expected conditions, and worst-case environmental disruptions.
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Synonyms:
PERT Estimation, Triangular Distribution Method