Introduction to Monte Carlo Simulation for PM
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- Опубликовано: 11 сен 2024
- This 10 min video presents the challenges of path convergence and the use of Monte Carlo simulation to quantify overall risk in Project Management and scheduling
#patjheffernan
The sum of the pessimistic path in the example adds to 21 (as noted in the text) and is erroneously reported in the table as adding to 17.5. This is a typo.
The 95-100% probability seems to be close to the "pessimitic" estimite in this example (and some other simple examples I have seen). Is this usually the case ? What confidence value do PMs use in real world projects ? Are they looking for 90-100% confidence ?
I don’t think it is actually possible to answer your question. The diverse nature of projects across the breadth of industries and circumstances change the variability of factors incredibly. I think you have to learn your industry and the nature of your projects to figure out what your risk tolerance is or should be.
@@PatJHeffernan I am looking for a real world example. So if someone has used this ; what was the industry , what was the confidence value they used , and how far off the pessimistic number was the value ?
As noted below.
could you explain the utilization of this in terms of point in time of a project lifecyle, should you run a simulation during time estimations or? By this, even the most pessimistic scenario (17,5 months) in the simulation turns out to be "optimistic" - 8% chance of completion. How would you go about the informations received from this simulation?
HI. The simulations are best used on groups of tasks or phases or the entire project. It is not useful in determining the estimates for a single task. The actual probability of completing parallel paths on time is potentially lower than is apparent by looking at the shortest path and this is good information to have. Use the information to inform/plan your schedule, resource deliveries or risk mitigations.