Monte Carlo Simulation (MCS) is a powerful technique that combines statistical concepts with computing power to analyze uncertainty in project costs. In this article, we explain how to apply MCS to improve contingency reserve estimation for a construction project using the Faina software tool.
What is Monte Carlo Simulation?
Monte Carlo Simulation involves creating a mathematical model of the system or process under study, defining random variables that represent the uncertainty of certain costs or factors. Then, random samples are generated for these variables, and the resulting system behavior is analyzed. Repeating this process many times (thousands of iterations) provides a distribution of outcomes representing different possible project scenarios.
Basic Cost Engineering Model
Imagine calculating the contingency reserve for a construction project that has a similar cost structure but values vary by region. Traditionally, a fixed percentage of the budget is set aside as contingency, but with MCS, we can do a much more tailored analysis.
To define cost distributions, ideally we use historical data, but when that's not available, expert judgment or supplier bid analysis can help. In our case, each cost element is modeled using a triangular distribution defined by three points: minimum, most likely, and maximum values.
Setting Up the Model in Faina
Each cost element was configured with its triangular distribution using Faina, ensuring parameters were correctly input. Summing these costs generates the total budget, which is then designated as the output of the MCS model.
We ran the simulation with 10,000 iterations to obtain a detailed distribution of the possible total project cost.
Interpretation of Results
Results are presented through charts such as histograms that allow comparison between the traditional budget (based on the most likely cost) and the expected value derived from the probabilistic model.
Two key concepts are recommended for determining contingency reserves:
- Expected Value: The mean of all simulation outcomes.
- 80th Percentile (P80): The cost that is not expected to be exceeded with 80% confidence.
The difference between the P80 and the expected value represents a justified contingency reserve for risks.
Conclusion
Monte Carlo Simulation with Faina offers a more robust and defensible methodology for calculating contingency reserves in projects with uncertainty. While the basic model presented uses a single distribution and does not consider unforeseen events, it provides a solid foundation that can be expanded to include different distributions and a risk register with low probability, high impact events.
This technique adds significant value to project management by providing a better understanding of risk and an objective basis for financial planning.
"Traditional deterministic budgets give you a false sense of certainty. Probabilistic budgeting reveals the full range of outcomes and helps you plan for what could actually happen—not just what you hope will happen."