Modelling and Quantitative Cost Risk Analysis Results

Are Incorrect or Unexpected

 

Overview

Risk Analyser (PRA) applies probabilistic modelling to evaluate uncertainty in project cost and schedule outcomes.

Quantitative Cost Risk Analysis combines base estimates, uncertainty ranges, and risk data to produce confidence-based results. 

• Unexpected outputs typically indicate configuration, data, or interpretation issues rather than licensing or access problems.

 

Solution

1. Confirm the correct cost or schedule cells are referenced in the model.
2. Verify uncertainty ranges and probability distributions are correctly defined.
3. Check that risks are linked to the appropriate cost or schedule elements.
4. Confirm confidence levels are interpreted correctly (for example, what 80% represents).
5. Re-run the analysis after adjustments to confirm results are consistent.

 

Troubleshooting

• If confidence values appear unusually high or low, review uncertainty inputs.
• If incorrect data is referenced, verify cell mappings and imported data.
• If results differ between runs, confirm no model inputs are changing unintentionally.

 

Best Practices

• Validate models using simple baseline scenarios before increasing complexity.
• Use consistent naming and structure for cost and schedule inputs.
• Document modelling assumptions for future reference.