Results show that potential risks may lead to a possible cost overrun. According to the hypotheses experts made, the average cost overrun is estimated at 2890 €.
As for the value at risk (VaR), the cumulative function gives a VaR_{5%} of 7800 € which means that 95% of the smallest possible cost overruns are less than 7800 € or we have 5% chance to be greater than 7800 €.
Cost overrun above 13000 € represents less than 0,8% of the possible outcomes. We limited on purpose the plot range to 13000 € to have a better view on what is going on below this value.
Statscorer plots the frequencies of the possible cost overrun and the cumulative distribution function (in red)
Optionally, we could also measure with Statscorer the number of risks that may be realized simultaneously during the project. The file downloadable online does not include this 2^{nd} output but here is what we would get:
Number of simultaneous risks likely to be realized during the project.
We see that we have a probability of only 3,86% that no risk ever be realized.
On the opposite, the probability to have more than 6 simultaneous risks realized is less than the probability to have none of them.
Cases where the number of realized risks is greater than 9 (from 10 to 26) are negligible (less than 0,02 % of all cases), though possible.
In conclusion, Monte Carlo simulations can be valuable for risk and decision analysis involved by project management activity.
Results are all the more accurate as potential risks have been comprehensively identified and characterized regarding probability and gravity.
With results like those described here, it is possible for a project management team to put aside a reasonable budget especially intended for risks ("risk provision").
Note: a similar study would have been possible with delay instead of cost.
Monte Carlo method and stochastic modeling would have allowed us to set dependencies between risks, precisely like we would have done in MS Project with tasks, and estimate the total expected delay on a project.
