By Jeremy Benjamin | 13th November 2025 | Project Controls, Risk, Monte-Carlo, QSRA, QCRA
The Problem: It’s Either Too Much or Not Enough (and Always Wrong)
An arbitrary percentage is inherently flawed because it completely ignores the actual risk profile of the project.
- If the risk is low: (e.g., a repeat project, well-understood scope, stable team), a flat 10% is over-budgeting. That money is now locked away, making your bid less competitive or giving the impression that your project is unnecessarily expensive.
- If the risk is high: (e.g., brand-new technology, uncertain regulatory landscape, ambitious schedule), a flat 10% is a dangerous underestimation. When the inevitable, complex issues arise, you’ll burn through that cushion in weeks and find yourself scrambling for change requests.
The arbitrary amount is always wrong because it’s based on the budget size, not the actual unpredictability of the work.
Three Reasons to Ditch the Percentage Plague
1. It Kills Accountability
When a simple percentage is applied, the project team often views the contingency as a secondary budget—a secret stash of “free money.” There’s no incentive to actively manage or mitigate risks because they know the cushion is there. Effective risk management requires identifying and tracking specific threats; a blanket percentage just encourages complacency.
2. It Inflates the Budget (and the Final Cost)
A budget is a commitment. An inflated budget makes your project look more costly than it needs to be. Worse still, if the team knows there’s a 10% buffer, costs tend to expand to fill the available space. It’s human nature—the money is there, so why not opt for the slightly more expensive solution or extend a deadline just a little?
3. Stakeholders Demand Better Transparency
Modern financial governance and sophisticated project sponsors aren’t satisfied with “trust me, we added some extra.” They want to know: What specifically are we spending this contingency on? When you use a risk-based approach, you can tell them, “The $50,000 in contingency is specifically for the three-month lead time risk on the custom-made server component, and we will release that money if the component arrives on time.” This builds trust and demonstrates professional control.
The Modern Approach: Quantitative Risk Analysis
The professional, financially responsible, and frankly, correct way to determine contingency is to use a quantitative risk analysis.
While calculating the Expected Value for identified risks is a solid starting point, it has a critical flaw: it only provides the mean (average) expected cost and treats all risks as isolated events. In the real world, costs and durations vary constantly, and risks often compound one another.
To move beyond the mean and truly understand your exposure, you must employ Monte Carlo Analysis.
Why You Need Monte Carlo Simulation
Monte Carlo analysis is a powerful computational technique that models the possible outcomes of a complex system by simulating it thousands of times.
- Define Uncertainty: Instead of just a single cost or duration estimate for a task, you provide a range (e.g., Minimum, Most Likely, Maximum).
- Define Risks: You input your identified risks with their probabilities and potential cost/schedule impacts.
- Simulate: The software runs the project plan (schedule and budget) thousands of times (e.g., 10,000 iterations), randomly drawing values from the defined ranges and applying the risks based on their probability.
- Results: The output is a probability distribution showing all possible outcomes and the likelihood of achieving them.
This process moves your contingency calculation from a simple addition to a science, resulting in two essential deliverables: a Quantitative Schedule Risk Analysis (QSRA) and a Quantitative Cost Risk Analysis (QCRA).
The Quantitative Schedule Risk Analysis (QSRA)
The QSRA applies Monte Carlo simulation to the project schedule. It helps answer the question: “How much longer could this project actually take?”
- It identifies the critical path with the highest probability of delay.
- The output provides a P-value (Percentile value) for your finish date. For instance, a P80 finish date means there is an 80% probability that the project will finish on or before that date. This is your scientifically justified schedule contingency.
The Quantitative Cost Risk Analysis (QCRA)
The QCRA applies Monte Carlo simulation to the project budget. It helps answer the question: “How much money do we truly need to feel confident?”
- It accounts for the cumulative effect of both task uncertainty (cost ranges) and discrete risks (e.g., a supplier failing).
- The output provides a P-value for the final cost. A P80 cost gives you a justifiable budget, and the difference between the base estimate and the P80 figure is your calculated Cost Contingency Reserve.
The Final Takeaway: Confidence, Not Guesswork
By utilising a QSRA and QCRA powered by Monte Carlo simulation, the conversation with stakeholders shifts dramatically:
It’s no longer a negotiation over an arbitrary percentage, but a data-driven discussion about the confidence level the organisation is willing to accept. Are you comfortable with a P50 (50% chance of success)? Or do you require a robust P80 or P90 confidence level for a mission-critical project?
This modern approach transforms contingency from a risky, hopeful guess into a defensible, scientifically justified management reserve—the cornerstone of professional project controls.