Cursory discussions with young project managers reveal a simple yet concerning fact. Most project managers are aware of the need to identify and manage project risks and most will be aware of the need to establish and publish a project risk register. That’s the good news. Where most inexperienced project managers fail is in their lack of understanding of the need to rigorously manage project risks arising inherently from their project schedule.

A short example can illustrate the issue quite clearly. Assume your project has 5 tasks, each estimated with a confidence level of 90%. Based on the above, would you say that your overall chances of meeting your project target delivery date are 90%? You might intuitively say ‘Yes’ but then you’ll be wrong. The correct answer is actually less than 60% (being the product of the following calculation: 0.9 x 0.9 x 0.9 x 0.9 x 0.9 = 0.59). So, in this example, if you were confidently managing your project, expecting a very high chance of meeting your deadline, you could be up for a surprise when some of the odds start playing against you.

The initial lesson from this example is that as a project manager you need to understand and manage the risks inherently built into your schedule. These risks require the least brainstorming or group discussions as they are directly and explicitly jump out of your project schedule and as such should be the most apparent ones.

The reality of most projects is that they will have a substantially larger number of tasks about them than the example above. Not only will the average project have substantially more tasks, each task is likely to have varying degrees of confidence levels, such that establishing an overall project scheduling risk factor will be far more complicated.

A simplistic approach for taking scheduling confidence into account is the PERT (also known as Three Point Estimate) technique. This technique uses three estimates to define an approximation of the activity’s duration (and cost). This technique works as follows: Determine your Optimistic (O), Pessimistic (P) and Most Likely (M) estimates for each activity. Having determined these parameters calculate the expected activity duration using a weighted average of the three parameters, based on the following formula:

Expected activity duration = (O + 4M + P) / 6.

The reason I referred to this approach as being “simplistic” is because despite its elegant look, it is statistically incorrect, due primarily to the fact that it assumes that the duration of each activity can be determined independently from all other activities, which is hardly ever the case.

This is the point where using Monte Carlo Analysis can come handy. Rather than using the simplistic approach suggested by the PERT technique, the Monte Carlo Analysis technique utilizes the three estimates to repeatedly simulate the project’s completion date, while taking into account the statistical likelihood that each activity’s duration will be somewhere on the continuum between the three estimates. The result of this analysis will not be a definitive answer, i.e. the answer will not be in the form of “based on the individual activity duration estimates, the project is expected to finish of date X”. Rather, the answer will be in the form of “based on the individual activity duration estimates, there is X% chance that the project will be complete on or before date Y”.

The chart below was produced using a Monte Carlo Simulation software, and highlights the type of outputs that such a tool will produce. Let’s examine the chart and understand its content. The overall purpose of the chart is to present the likelihood (or a better term – probability) of the project completing on any particular date. The left axis (Hits) can be used to review the number of times, during the simulation, that a particular date was identified as a potential completion date. The right axis (Cumulative Frequency) shows the total accumulative times that the project was determined to complete on or before a particular date. The bottom axis (Distribution) shows the identified potential completion dates, while the height of the bar associated with that date is determined by the number of times that the date has been identified by the simulation as being the project’s completion date.

In this example, having analyzed the project activity durations, the following statements can now be made:

  • Based on the individual activity duration estimates, there is 80% chance that the project will be complete on or before 21/05/02.
  • Based on the individual activity duration estimates, there is 50% chance that the project will be complete on or before 15/05/02.

By the way, if you notice the yellow arrow, pointing at 08/05/02, this is the date shown as the project completion date on the project plan. Now that we’ve performed the risk analysis we can determine that our chances of actually finishing the project on or before that date are just 15%!

As the name of this post suggests, there is much more to Monte Carlo Simulation software than what I’ve presented here. The above, however, highlights the fundamental need to consider project scheduling risks as if you don’t they WILL come back to haunt you.

Can you see how this type of analysis can help you better manage your project risks?

I value your comments, if you have any thoughts on the above please join in and share with others!

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