Monte Carlo Modeling

Monte Carlo Modeling is a technique for managing risks. The basic idea is to run simulations of a risk event many, many times to allow a risk distribution to play out.

As an example, let's look at schedule risk, or the likelihood that your project will complete at any given time. Usually you provide ine estimate of effort and duration per activity and this drives toward one end date.

However, there is uncertainly, or risk, in each of the estimates that make up the workplan, so obviously there is not a 100% chance of meeting the estimated end date. Monte Carlo modeling gives you a way to calculate and describe this uncertainty.

Monte Carlo modeling starts off a little like the PERT estimating technique. Rather than give one estimate for the duration of an activity, you provide a series of estimates that represent the best case, most likely case and the worst case. For each of these cases, you also assign a probability.

For instance, there may be a 10% chance of hitting your best case estimate, an 80% chance of hitting your most likely estimate, and a 10% chance of the work extendinghit the worst case scenario. In other words there is a 90% cumulative chance the activity will be completed by the most likely scenario (10% + 80%) and a 100% cumulative chance that the work will be done by the worst case estimate (10% + 80% + 10%).

You don't need to determine the percent likelihood for points in between - just those three points. (Technically you can provide estimates for any and all probabilities.)

You then have to prepare these three estimates for each of the major work activities in your workplan. For example, you may estimate an activity to most likely take 60 hours, with a best case of 50 hours and a worst case of 90 hours. These three estimates might need to be prepared for dozens (or hundreds) of activities in the workplan.

When you are done, most workplan tools have a function to perform a Monte Carlo Simulation. Basically, the simulation models how the project will progress, and reaches an estimated end date. The project plan is then mapped out again, this time using differing probabilities, and therefore calculating a different end date.

The reason the model is run many times is so that the risk percentages have a chance to play put. For instance, in the example above, if the simulation was run 100 times, you would expect that each individual activity would hit the best case 10 times (10%), the worst case 10 times (10%) and the expected case 80 times (80%).

As the modeling tools randomly picks estimated values based on probabilities, many different project scenarios play out.

However, a basic pattern starts to emerge that allows you to estimate the most likely date that your project will end. With Monte Carlo schedule estimating, you no longer tell your manager that the project will be completed by a certain date. Instead, you are able to estimate the probability that you will be finished on any individual date. Your manager may request the estimated end date that is 80% likely to occur.

Although the example above used schedule risk, you can also use this technique for providing estimates for cost and effort as well. The good thing about the Monte Carlo Simulation is that if you provide activity estimates in ranges, most tools will perform all the statistical calculations automatically.

You just have to make sure that you have provided valid and reasonable estimates for the activities. You can see that the extra work required in the estimating process makes this a model to be used for projects that are very large or those that contain a lot of risk. Small and medium sized projects would probably not find value in this technique.

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