Parametric Estimating — Example Models (Process)
A beginner's walkthrough of a parametric contingency model applied to a process-industry project — scoring the systemic risk factors, running them through the model, and reading out a contingency. Built on AACE International RP 43R-08.
Why process industries?
Parametric contingency models were pioneered on process-industry projects — refineries, chemical plants, LNG, pharmaceuticals — and that's where they're most mature. These projects share enough structure (defined phases, repeatable equipment, decades of cost history) that the relationship between a project's characteristics and its overrun can be reliably modelled. RP 43R-08 provides example models for exactly this setting.
Scoring the systemic factors
A typical process-industry parametric model rates the project on several factors, each on a defined scale (here, a simple 1 = best to 5 = worst). The model's fitted coefficients then weight each factor's contribution to contingency:
| Systemic factor | What it measures | Score (1–5) |
|---|---|---|
| Scope definition | Completeness of scope & engineering at estimate | 3 |
| Process technology | How proven vs novel the process is | 2 |
| Project complexity | Number of systems, interfaces, constraints | 4 |
| Project size / location | Scale and site difficulty | 3 |
| Team & data quality | Experience and quality of cost basis | 2 |
Worked example: a process plant
Take a $50M process plant with the factor scores above. An illustrative model assigns each factor a contingency contribution; summing them gives the total. (Real models use validated regression coefficients — these are round numbers for teaching.)
$50M process plant — parametric contingency build-up
Worked example| Base allowance (all projects) | 4% |
| Scope definition (score 3) | +5% |
| Process technology (score 2) | +2% |
| Project complexity (score 4) | +5% |
| Project size / location (score 3) | +1% |
| Team & data quality (score 2) | +1% |
| Predicted contingency | 18% → $9.0M |
The model predicts 18% contingency, or $9.0M on the $50M base. Notice what drove it: scope definition and complexity together contributed 10 of the 18 points — the systemic factors doing exactly what Lesson 12 said they would.
Lessons from the example
Use a model validated on projects like yours, score the factors honestly and ideally independently, and present both the total and the factor contributions so decision-makers see what's driving the number. Treat the result as the systemic-risk contingency, and reconcile it against expected-value or Monte Carlo estimates of the discrete risks — when the two diverge a lot, that gap is itself worth investigating.
Nine things to remember
- Parametric models are most mature on process-industry projects.
- You score the project on systemic factors, not individual risks.
- Key factors: scope definition, technology, complexity, size, team/data.
- Worked result: 18% contingency → $9.0M on a $50M plant.
- Scope definition and complexity dominated the contribution.
- The scoring is where the judgment — and bias — lives.
- Read the factor contributions — they point at how to reduce contingency.
- Use a model validated on similar projects and score independently.
- Reconcile against line-item methods — big gaps are worth investigating.
Glossary
- Base allowance
- Contingency every project carries.
- Coefficient
- Fitted weight turning a score into contingency.
- Contribution
- Each factor's share of total contingency.
- Factor score
- The rating given to each systemic factor.
- Process technology
- How proven vs novel the process is.
- Reconciliation
- Comparing methods' results for consistency.
- Scope definition
- Completeness of scope and engineering.
- Systemic risk factor
- A scored project characteristic driving contingency.