Taking a Q&A format, this short blog provides an overview of how Monte Carlo Analysis, the technique first used by scientists working on the atom bomb back in the 1940s, can help risk and project managers to assess the possible impacts of risks, see all the possible outcomes of decisions and through quantitative analysis, make optimal decisions that achieve the best possible project outcomes.
What is Monte Carlo Analysis?
Monte Carlo Analysis – also known as the Monte Carlo Method – is used to solve complex problems in a diversity of scenarios from science and maths to engineering, finance, and business applications. It is a predictive modeling technique that works on probability distributions of the most possible outcomes from decisions to be made, providing project managers with the means of assessing levels of risk quantitatively and making decisions based upon the risks involved.
What does Monte Carlo Analysis do?
The computerised technique provides a range of possible outcomes together with the probabilities that they will occur for any choice of action to be taken. The extreme possibilities at both ends of the scale, along with all of the potential consequences for decisions in between.
How does Monte Carlo Analysis perform risk analysis?
Risks are analyzed by creating models of possible results through the substitution of a range of values – known as a probability distribution – for any factor that holds uncertainty. Results are calculated and recalculated time and again, using a different set of random values from the probability functions in each instance. A Monte Carlo analysis could involve numerous recalculations, depending on the number of uncertainties and their specified ranges.
What are the benefits of the Monte Carlo method in risk analysis?
Probability distributions provide a realistic way of illustrating uncertainty in variables of a risk analysis. When compared to single-point estimate or deterministic analysis, the advantages include:
- Results that show what could happen and the likelihood of each outcome
- Graphical results, making it easier to share and communicate risk data with other stakeholders
- Clarity over the inputs that have the biggest impact on bottom-line results (sensitivity analysis)
- The ability to see the effects of vastly different scenarios – insight for risk professionals on which inputs had which values when particular outcomes occurred (scenario analysis)
- Greater accuracy in assessing the correlation of inputs – clearer representation of how should certain factors go up, others go up (or down) accordingly
How does Monte Carlo Analysis provide a more comprehensive view of risk?
When Monte Carlo Analysis is undertaken, values are sampled from the input probability distributions at random, with each set called an iteration. The outcome from each iteration is recorded and this process happens repeatedly to provide a probability distribution of possible outcomes. This offers risk professionals a far more comprehensive picture: a view of what could happen and how likely that outcome is.
With the benefit of quantified data, risk and project managers have a clearer view of project timelines and schedule and cost more effectively.
Why should risk professionals adopt Monte Carlo Analysis?
- Insightful data for decision making
- Underpins better project scheduling, resource, and cost control
- Easier assessment of project milestones
- Clearer assessment of overruns in schedule and budget
- More effective risk quantification
How can I undertake Monte Carlo Analysis within ERM or project risk management?
Monte Carlo Analysis can help risk and project teams to manage work that includes forecasting, estimating, and decision-making in scenarios of major uncertainty.