This is a valid question for managers looking to do scenario analysis for their projects. There are several approaches to solve this, nevertheless Monte Carlo simulation is the best all-around technique for generating said scenarios.
We can use Monte Carlo methods to quantitatively assess the risk factor of a project. In this case, the main output of the simulation presents the range of possible outcomes against the probability of each value being achieved. This is usually shown as a cumulative probability plot like the following:
Here, after running n number of trials to calculate possible outcomes, the graph shows that the potential variation in total project cost is $0.7 million against a target budget value of $1.3M. The variation comes from the range of possible values between the 5th percentile – $1.1M – and the 95th percentile – $1.8M- .
The graph also shows that the probability of meeting the project cost target of $1.3 million is 25%, with a 75% of exceeding the budget. The analysis predicts an expected outcome of $1.425 million, which is an overspend of $0.125 million or ~+10%. With this data we can determine the values of total project cost that correspond to chosen confidence levels; for example there would be an 85% chance of meeting a revised budget of $1.6 million. This allows us to make risk-informed decisions trading off increased cost (+ $0.3 million, from our original $1.3 million target) against increased probability of success, i.e. from 25% to 85%.
In general, this type of analysis helps us determine how risky is the project by asking:
- What is the potential range of variation in outcome?
- Total potential range = $0.7 million (= 54% of project value)
- Best case scenario = $1.1 million (–15%)
- Worst case scenario = $1.8 million (+38%)
- How likely is this project to succeed?
- Probability of meeting $1.3 million target = 25%
- Expected value = $1.425 million (+10%)
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