Definition: A cost estimating technique that uses statistical relationships between historical data and project variables to predict future costs.
Key Components:
- Historical Data Analysis: Utilizing past project data to identify cost-driving parameters.
- Cost Estimation Relationships (CERs): Formulas that relate project characteristics to costs.
- Adjustments for Complexity: Modifying estimates based on project-specific factors.
Use Cases/Industries:
- Aerospace: Estimating costs based on weight and complexity of components.
- Construction: Predicting expenses using square footage and material types.
- Software Development: Forecasting costs based on lines of code or function points.
Advantages:
- Efficiency: Generates estimates quickly using established models.
- Consistency: Applies uniform criteria across multiple projects.
- Early-Stage Utility: Provides estimates when detailed information is scarce.
Challenges:
- Data Quality: Requires accurate and relevant historical data.
- Model Limitations: May not account for unique project nuances.
- Assumption Risks: Relies on the premise that future projects will mirror past ones.
Related Terms: Analogous Estimating, Cost Modeling, Quantitative Analysis
Example: A construction firm uses parametric estimating to predict the cost of a new office building by analyzing costs per square foot from similar past projects.
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Synonyms:
Statistical Estimating, Algorithmic Cost Modeling
