Data-driven risk modelling and quantitative risk assessment for Queensland infrastructure projects. We transform project uncertainty into defensible contingencies, realistic budgets, and informed investment decisions through Monte Carlo simulation and structured risk management.
Quantitative risk analysis transforms uncertainty from a vague concern into measurable, manageable parameters. Projects with rigorous risk modelling consistently achieve better cost and schedule outcomes than those relying on intuition or arbitrary contingencies.
Replace arbitrary percentage-based contingencies with data-driven, risk-justified calculations that reflect the specific risk profile of your project. Quantified contingencies withstand scrutiny from funding bodies, auditors, and project governance boards because they are based on transparent, repeatable analysis rather than subjective judgement.
Understand probabilistic completion dates at various confidence levels (P50, P80, P90) to set realistic schedule expectations and identify schedule contingency requirements. Schedule risk assessment reveals the activities and risks that drive programme uncertainty, enabling targeted management of schedule-critical issues.
Make investment and funding decisions based on a full understanding of risk exposure. Know the probability of achieving budget and schedule targets before committing resources, enabling better capital allocation across project portfolios and more confident stakeholder communication about project cost certainty.
Sensitivity analysis using tornado diagrams identifies the individual risks with the greatest impact on project outcomes, enabling focused mitigation efforts where they will deliver the most value. Rather than spreading limited management attention across all risks equally, quantitative analysis directs effort to the risks that genuinely matter.
Demonstrate rigorous, transparent risk analysis to stakeholders, project boards, and funding bodies. Probabilistic analysis builds confidence by showing that cost and schedule estimates have been developed with a clear understanding of uncertainty, and that contingencies are proportionate to the genuine risk exposure of the project.
Ongoing risk monitoring and model updates keep risk analysis current as project circumstances evolve. As risks are realised, mitigated, or retired, the model is updated to reflect the current risk profile, enabling proactive management decisions based on the latest understanding of project uncertainty rather than outdated assumptions.
Cenex follows a structured, five-phase risk management methodology that ensures comprehensive identification, rigorous quantification, and practical management of project risks.
We facilitate structured risk identification workshops with key project stakeholders to systematically identify risks across all categories: technical, commercial, environmental, regulatory, market, and delivery. Our facilitation methodology ensures comprehensive coverage and draws on the collective expertise of the project team.
Identified risks are assessed qualitatively using consistent likelihood and consequence scales to establish a prioritised risk register. This initial assessment guides the focus of quantitative analysis and ensures management attention is directed to the most significant risks.
We build probabilistic models using three-point estimates and appropriate probability distributions for uncertain variables. Base estimate items are assigned range estimates reflecting estimating uncertainty, while discrete risk events are modelled with probability of occurrence and impact distributions. The model captures correlations between related risk items.
We run Monte Carlo simulations with thousands of iterations to produce probabilistic cost and schedule outcomes. Results include S-curve probability distributions, confidence level tables (P50, P80, P90), and sensitivity analysis via tornado diagrams to identify the risks with greatest impact on project outcomes.
We deliver comprehensive risk analysis reports with clear recommendations for contingency allocation and risk mitigation strategies. We provide ongoing risk monitoring and model updates as the project progresses, ensuring analysis remains current and continues to inform project governance decisions throughout delivery.
From risk identification workshops through to Monte Carlo simulation and ongoing risk monitoring, Cenex provides the full spectrum of risk management services that infrastructure projects require.
Facilitated risk identification workshops that systematically identify project risks across all categories using structured techniques. Our experienced facilitators draw on diverse stakeholder perspectives to ensure comprehensive coverage, producing a prioritised risk register that forms the foundation for quantitative analysis and ongoing risk management throughout the project lifecycle.
Comprehensive probabilistic modelling of project cost and schedule using three-point estimates, probability distributions, and Monte Carlo simulation. Our QRA models capture both base estimate uncertainty (range estimates for individual cost items) and discrete risk events (probability of occurrence multiplied by impact), providing a complete picture of project uncertainty that informs contingency allocation and management decisions.
Running thousands of simulation iterations using @Risk and Primavera Risk Analysis to produce probabilistic cost and schedule outcomes. Our simulations generate S-curve probability distributions, confidence level tables, and statistical summaries that clearly communicate the range of possible outcomes and the likelihood of achieving specific budget and programme targets.
Calculation of appropriate contingency levels based on quantified risk exposure and organisational risk appetite. We determine defensible P50, P80, and P90 contingency values that replace arbitrary percentage-based allowances with data-driven calculations tied to the specific risk profile of each project. Our contingency analysis complies with TMR PCEM requirements and AACE International recommended practices.
Identification of the individual risks and uncertainties with the greatest impact on project outcomes using tornado diagrams and contribution analysis. Sensitivity analysis reveals which risks drive contingency requirements, enabling project teams to focus mitigation efforts and management attention on the uncertainties that genuinely influence project success rather than spreading resources thinly across all identified risks.
Probabilistic analysis of project programmes to determine the likelihood of achieving target completion dates. Integrated with Primavera P6 schedules via Primavera Risk Analysis, our schedule risk assessment models activity duration uncertainty and discrete schedule risk events to produce probabilistic completion date forecasts. Combined with our planning and scheduling services, this provides a complete view of programme uncertainty.
Development of practical risk treatment strategies including avoidance, mitigation, transfer, and acceptance for identified project risks. We assess the cost-effectiveness of proposed mitigation measures by modelling their impact on the risk profile, enabling project teams to invest in mitigation measures that deliver genuine value rather than implementing costly treatments for low-impact risks.
Regular risk review and model updates as the project progresses, maintaining current analysis that reflects evolving circumstances. As risks are realised, mitigated, or retired, the model is updated to provide current contingency forecasts and identify emerging risks. Ongoing monitoring ensures risk analysis remains a living management tool rather than a point-in-time document that quickly becomes outdated.
Answers to common questions about risk modelling, Monte Carlo simulation, and how quantitative risk assessment benefits infrastructure projects.
Quantitative risk assessment (QRA) is the process of numerically modelling the potential impact of identified project risks on cost and schedule outcomes. Unlike qualitative assessment, which ranks risks using likelihood and consequence ratings, QRA assigns probability distributions to uncertain variables and uses statistical techniques such as Monte Carlo simulation to calculate the combined effect of all risks. The output is a probability distribution showing the range of possible project outcomes, enabling project owners to understand the likelihood of achieving budget and schedule targets at various confidence levels (P50, P80, P90).
Monte Carlo simulation is a statistical technique that models uncertainty by running thousands of iterations of a project cost or schedule model, each time randomly sampling values from the probability distributions assigned to uncertain variables. The combined results of all iterations produce a probability distribution (S-curve) showing the likelihood of the total project cost or completion date falling at any given value. This enables informed decision-making about budgets, contingencies, and schedule targets based on quantified probabilities rather than single-point estimates.
P50, P80, and P90 refer to percentile values from a probabilistic analysis. P50 means there is a 50% probability that the actual outcome will be at or below this value (the median). P80 means 80% probability, and P90 means 90% probability. P50 is commonly used as the expected project cost, P80 is often adopted for project budgets as it provides reasonable confidence without excessive conservatism, and P90 may be used for funding approvals or where consequences of exceeding budget are severe. The appropriate confidence level depends on organisational risk appetite.
Quantitative risk assessment should be performed at key decision points throughout the project lifecycle. During the business case phase, QRA informs funding decisions. At detailed design, QRA refines contingency estimates. Pre-tender, QRA establishes realistic budget expectations. During construction, QRA is updated to reflect current risk exposure. The Department of Transport and Main Roads (TMR) in Queensland requires quantitative risk assessment as part of its PCEM framework for significant infrastructure investments.
Contingency is calculated as the difference between the base estimate and the target confidence level from the Monte Carlo simulation. For example, if the base estimate is $50 million and the P80 value is $58 million, the recommended contingency at P80 is $8 million. This replaces arbitrary percentage-based contingencies with a defensible, data-driven calculation reflecting the project's specific risk profile. Sensitivity analysis then identifies which risks contribute most to contingency, guiding mitigation efforts to reduce contingency through active risk management.
Cenex uses industry-leading tools including @Risk (by Palisade/Lumivero) for cost risk modelling integrated with Excel, and Oracle Primavera Risk Analysis for schedule risk assessment integrated with Primavera P6. We also use our proprietary Trinity platform for integrated cost-risk analysis. Our framework aligns with ISO 31000 and AACE International recommended practices. The choice of tool depends on project requirements, client preferences, and whether the analysis focuses on cost risk, schedule risk, or integrated cost-schedule risk modelling.
Our experienced risk analysts are ready to quantify your project uncertainty, validate contingency allocations, and provide the probabilistic analysis that informed investment decisions require. From Monte Carlo simulation to ongoing risk monitoring, we deliver the risk modelling expertise that Queensland infrastructure projects demand. Get in touch to discuss your requirements.