From the number of hits I get on my blog, relating to posts dealing with the application of Monte Carlo Simulation in the context of Project Management, one might conclude that everybody is in it. I can’t discount the possibility that many of these hits are generated by punters attempting to increase their chances before getting into the nearest casino venue. But even with that in mind, I am still puzzled by this phenomena.

I don’t want to discourage anyone from acquiring further knowledge on this important topic. But, let’s face it, unless you have a certain set of pre-requisites in place, your likelihood of generating any credible and useful results out of such a simulation are practically zero.

Ask yourself the following question and, if your answer is ‘NO’, go back to basics.

Can you get credible three point estimates? Do you have access to credible historical data to support that? Do you have access to Subject Matter Experts (SMEs) who can assist in getting these credible estimates?

Back to basics? See my comments in http://quantmleap.com/blog/2011/01/earned-value-you-have-to-crawl-before-you-can-walk/.

Don’t just think about it – do it!

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One of the scheduling tools at the project manager’s disposal is the Critical Path Method, the outcome of which would be:

1. A list of all activities required to complete the project
2. The time (duration) that each activity will take
3. The dependencies between the activities.

With the above, the longest path of planned activities can be identified, and the “critical” activities (i.e those activities whose combined duration dictate how long the duration is planned to be) are identified.

There are a number of methodological issues with the Critical Path Method, one of which is that the view used to identified the critical path and the tasks it includes is a static view, correct at a point in time, and does not take into account the likelihood of any task on the project schedule taking longer (or shorter) than initially planned.

Let’s look at the following example:

Simply viewing at this schedule, without taking into account other supporting information could be misleading. One issue that could substantially affect the accuracy of this view is the availability (or lack) of resources (see elaboration on  this issue in my earlier post “The Critical Path Does not Tell You Everything You Need to Know About Your Project Constraints“). Another factor that needs to be examined is the level of comfort, or the likelihood of achieving the planned duration.

Having examined the above schedule, would you change your view of this project given the following additional information?

The table above introduces further information regarding the Optimistic, Pessimistic and Most Likely duration for each of these tasks. It clearly demonstrates that although tasks 2 – 5 share a Most Likely Duration, the level of risk associated with each is gradually increasing, from 5 days in task1 to 9 days in task 5.

Utilizing the above added information to better understand the risk dimension associated with the schedule requires the execution of a sensitivity analysis.

Sensitivity Analysis is defined as a “Simulation analysis in which key quantitative assumptions and computations (underlying a decision, estimate, or project) are changed systematically to assess their effect on the final outcome. Employed commonly in evaluation of the overall risk or in identification of critical factors, it attempts to predict alternative outcomes of the same course of action. In comparison, contingency analysis uses qualitative assumptions to paint different scenarios. Also called what-if analysis“.

In other words, a greater insight into the risks hidden behind the project schedule can be derived by adjusting the various tasks’ durations (within their PERT parameters) and confirming, for each such variation, whether a task is included or excluded from the critical path. Performing such adjustments a large number of times would result in an indication of how many times, or in what percentage of times, did any particular task appear on the critical path. The larger the frequency, the greater the need to examine the task and put in place the mechanism to ensure that the task does not negatively deviate from its scheduled duration.

Just to wrap this up, let’s look at the schedule from two additional, extreme perspectives:

The following is a Critical Path view of the project schedule should all tasks durations happen to match the best case duration estimates:

Next is the view of the project schedule should all tasks durations happen to match the worst case duration estimates:

Performing Sensitivity Analysis on the above project would result in a result similar to the one outlined below:

What the above diagram suggests is that when running a simulation with a large number of samples (lets say 1,000 times), then Task1 will appear on the Critical Path 100%, Task4 and Task5 – 39% of the times, etc.

Wouldn’t this be useful to know?

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A 2007 article by Young Hoon Kwak and Lisa Ingall, titled “Exploring Monte Carlo Simulation Applications for Project Management”*, examines the Monte Carlo Simulation method and its uses in the field of Project Management.

Apart from being a good reference document, where a brief history of this technique is being discussed and explained, this article provides a good review of various studies published around the benefits as well as the potential complexities associated with implementing this technique in real life situations.

The article points out that one of the limitations of using this technique as being project managers’ discomfort with statistical approaches, as well as lack of thorough understanding of the method (see also my earlier post discussing  this issue in “Some Risk Management Related Thoughts“.

Discussing the process for utilizing Monte Carlo Simulation in the context of Time Management, the article suggests the following steps are commonly used:

  1. Utilize subject matter expertise to assign a probability distribution function of duration to each task or group of tasks in the project network;
  2. Possibly use Three-Point-Estimates to simplify this process, where an expert knowledge is used to supply the most-likely, worst-case and best-case durations for each task or group of tasks;
  3. Fit the above estimates to a duration probability distribution (such as Normal, Beta, Triangular, etc.) for the task;
  4. Execute the simulation and use the results to formulate expected completion date and required schedule reserve for the project.

Outlining the advantages of utilizing Monte Carlo Simulation applications in Project Management, the article points out that its primary advantage is in being an “extremely useful tool when trying to understand and quantify the potential effects of uncertainty in projects“. Clearly, not utilizing this technique, project managers lack a powerful tool that can result in not meeting the project’s schedule and cost targets. Better quantification of the necessary schedule and cost reserves can substantially reduce such risks.

The article highlights the importance of having access to expert knowledge and prior experience and detailed data from previous projects in order to mitigate the inherent issues of estimates uncertainly which would ultimately affect the quality of the simulation results. This is correct not just with respect to the three-point-estimates but also with respect to choosing the correct probability distributions with which to model these estimates.

An interesting point is raised when referring to an earlier study published  by Grave R. (2001. “Open and Closed: The Monte Carlo Model,” PM Network, vol. 15, no. 2, pp. 48-52) which discusses the merits of using different types of probability distributions for project task duration estimates. Grave suggests the use of open-ended distribution (the lognormal distribution) instead of using closed-ended distributions (such as the triangular distribution) when performing the Monte Carlo Simulations.

His logic is as follows: A closed-ended distribution (e.g. triangular distribution) does not consider the possibility  of a task duration completing BEFORE the best-case or AFTER the worst-case duration estimate. However, in real life projects, due to various constraints, it is possible for a task to complete before or after the best or worst scenarios.

When an open-ended distribution is used, the possibility for exceeding the upper limit of the task duration is recognized, thus making the simulation more realistic.

The article touches (although very briefly) on one of the aspects used within the context of Monte Carlo Simulation which is the Criticality Index (I will endeavor to provide a more detailed discussion about  this feature in a future post). In a nutshell, the Criticality Index is a reflection of the rate at which the task appears on the critical path of the project through the simulation iterations.

Overall an interesting article. If you are already using Monte Carlo Simulation as part of your portfolio of project tools, this article will encourage you to keep doing so. And if you’re not – you’ll need to ask yourself – WHY?

*see in http://home.gwu.edu/~kwak/Monte_Carlo_Kwak_Ingall.pdf

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Ok, I’m having a bit of a fun with this one.

Submit a copy of your project plan (in Microsoft Project format only at the moment) and I will send back to you a high level risk assessment of your project schedule’s based on a Monte Carlo Simulation.

There’s absolutely no catch. The reason I’m doing it is because it seems to me that many don’t yet understand the benefits of using this technique to better understand the risks that lie dormant within their project schedules.

If you are interested in this FREE offer fill in and submit the details below.

And by the way, I give you my guarantee that once I process your project schedule I will not make any further use of it and it will be erased/discarded and will not be used for any purpose other than attempting to provide you with this service.

To learn / read more about this topic check out the Related Posts listed below.

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To ensure the successful completion of a project, it is of utmost importance for the project manager to find ways to handle uncertainties that can pose potential risks for a project. Risk management is an iterative process. Risks can relate to any aspect of the project – be it the cost, schedule, or quality. The key to managing risks is to identify them early on in the project and develop an appropriate risk response plan.

To develop a Risk Response Plan, you need to quantify the impact of risks on the project. This process is known as quantitative risk analysis wherein risks are categorized as high or low priority risks depending on the quantum of their impact on the project. The Project Management Body of Knowledge (PMBOK) advocates the use of Monte Carlo analysis for performing quantitative risk analysis.

What is Monte Carlo Analysis?

Monte Carlo analysis involves determining the impact of the identified risks by running simulations to identify the range of possible outcomes for a number of scenarios. A random sampling is performed by using uncertain risk variable inputs to generate the range of outcomes with a confidence measure for each outcome. This is typically done by establishing a mathematical model and then running simulations using this model to estimate the impact of project risks. This technique helps in forecasting the likely outcome of an event and thereby helps in making informed project decisions.

While managing a project, you would have faced numerous situations where you have a list of potential risks for the project, but you have no clue of their possible impact on the project. To solve this problem, you can consider the worst-case scenario by summing up the maximum expected values for all the variables. Similarly, you can calculate the best-case scenario. You can now use the Monte Carlo analysis and run simulations to generate the most likely outcome for the event. In most situations, you will come across a bell-shaped normal distribution pattern for the possible outcomes.

Let us try to understand this with the help of an example. Suppose you are managing a project involving creation of an eLearning module. The creation of the eLearning module comprises of three tasks: writing content, creating graphics, and integrating the multimedia elements. Based on prior experience or other expert knowledge, you determine the best case, most-likely, and worst-case estimates for each of these activities as given below:

Tasks Best-case estimate Most likely estimate Worst-case estimate
Writing content 4 days 6 days 8 days
Creating graphics 5 days 7 days 9 days
Multimedia integration 2 days 4 days 6 days
Total duration 11 days 17 days 23 days

The Monte Carlo simulation randomly selects the input values for the different tasks to generate the possible outcomes. Let us assume that the simulation is run 500 times. From the above table, we can see that the project can be completed anywhere between 11 to 23 days. When the Monte Carlo simulation runs are performed, we can analyse the percentage of times each duration outcome between 11 and 23 is obtained. The following table depicts the outcome of a possible Monte Carlo simulation:

Total Project Duration Number of times the simulation result was less than or equal to the Total Project Duration Percentage of simulation runs where the result was less than or equal to the Total Project Duration
11 5 1%
12 20 4%
13 75 15%
14 90 18%
15 125 25%
16 140 28%
17 165 33%
18 275 55%
19 440 88%
20 475 95%
21 490 98%
22 495 99%
23 500 100%

This can be shown graphically in the following manner:

What the above table and chart suggest is, for example, that the likelihood of completing the project in 17 days or less is 33%. Similarly, the likelihood of completing the project in 19 days or less is 88%, etc. Note the importance of verifying the possibility of completing the project in 17 days, as this, according to the Most Likely estimates, was the time you would expect the project to take. Given the above analysis, it looks much more likely that the project will end up taking anywhere between 19 – 20 days.

Benefits of Using Monte Carlo Analysis

Whenever you face a complex estimation or forecasting situation that involves a high degree of complexity and uncertainty, it is best advised to use the Monte Carlo simulation to analyze the likelihood of meeting your objectives, given your project risk factors, as determined by your schedule risk profile. It is very effective as it is based on evaluation of data numerically and there is no guesswork involved. The key benefits of using the Monte Carlo analysis are listed below:

  • It is an easy method for arriving at the likely outcome for an uncertain event and an associated confidence limit for the outcome. The only pre-requisites are that you should identify the range limits and the correlation with other variables.
  • It is a useful technique for easing decision-making based on numerical data to back your decision.
  • Monte Carlo simulations are typically useful while analyzing cost and schedule. With the help of the Monte Carlo analysis, you can add the cost and schedule risk event to your forecasting model with a greater level of confidence.
  • You can also use the Monte Carlo analysis to find the likelihood of meeting your project milestones and intermediate goals.

Now that you are aware of the Monte Carlo analysis and its benefits, let us look at the steps that need to be performed while analysing data using the Monte Carlo simulation.

Monte Carlo Analysis: Steps

The series of steps followed in the Monte Carlo analysis are listed below:

  1. Identify the key project risk variables.
  2. Identify the range limits for these project variables.
  3. Specify probability weights for this range of values.
  4. Establish the relationships for the correlated variables.
  5. Perform simulation runs based on the identified variables and the correlations.
  6. Statistically analyze the results of the simulation run.

Each of the above listed steps of the Monte Carlo simulation is detailed below:

  1. Identification of the key project risk variables: A risk variable is a parameter which is critical to the success of the project and a slight variation in its outcome might have a negative impact on the project. The project risk variables are typically isolated using the sensitivity and uncertainty analysis.

    Sensitivity analysis is used for determining the most critical variables in a project. To identify the most critical variables in the project, all the variables are subjected to a fixed deviation and the outcome is analysed. The variables that have the greatest impact on the outcome of the project are isolated as the key project risk variables. However, sensitivity analysis in itself might give some misleading results as it does not take into consideration the realistic nature of the projected change on a specific variable. Therefore it is important to perform uncertainty analysis in conjunction with the sensitivity analysis.

    Uncertainty analysis involves establishing the suitability of a result and it helps in verifying the fitness or validity of a particular variable. A project variable causing high impact on the overall project might be insignificant if the probability of its occurrence is extremely low. Therefore it is important to perform uncertainty analysis.

  2. Identification of the range limits for the project variables: This process involves defining the maximum and minimum values for each identified project risk variable. If you have historical data available with you, this can be an easier task. You simply need to organize the available data in the form of a frequency distribution by grouping the number of occurrences at consecutive value intervals. In situations where you do not have exhaustive historical data, you need to rely on expert judgement to determine the most likely values.
  3. Specification of probability weights for the established range of values: The next step involves allocating the probability of occurrence for the project risk variable. To do so, multi-value probability distributions are deployed. Some commonly used probability distributions for analyzing risks are normal distribution, uniform distribution, triangular distribution, and step distribution. The normal, uniform, and triangular distributions are even distributions and establish the probability symmetrically within the defined range with varying concentration towards the centre. Various types of commonly used probability distributions are depicted in the diagrams below:



  4. Establishing the relationships for the correlated variables: The next step involves defining the correlation between the project risk variables. Correlation is the relationship between two or more variables wherein a change in one variable induces a simultaneous change in the other. In the Monte Carlo simulation, input values for the project risk variables are randomly selected to execute the simulation runs. Therefore, if certain risk variable inputs are generated that violate the correlation between the variables, the output is likely to be off the expected value. It is therefore very important to establish the correlation between variables and then accordingly apply constraints to the simulation runs to ensure that the random selection of the inputs does not violate the defined correlation. This is done by specifying a correlation coefficient that defines the relationship between two or more variables. When the simulation rounds are performed by the computer, the specification of a correlation coefficient ensures that the relationship specified is adhered to without any violations.
  5. Performing Simulation Runs: The next step involves performing simulation runs. This is typically done using a simulation software and ideally 500 – 1000 simulation runs constitute a good sample size. While executing the simulation runs, random values of risk variables are selected with the specified probability distribution and correlations.
  6. Statistical Analysis of the Simulation Results: Each simulation run represents the probability of occurrence of a risk event. A cumulative probability distribution of all the simulation runs is plotted and it can be used to interpret the probability for the result of the project being above or below a specific value. This cumulative probability distribution can be used to assess the overall project risk.

Summary

Monte Carlo simulation is a valuable technique for analyzing risks, specifically those related to cost and schedule. The fact that it is based on numeric data gathered by running multiple simulations adds even greater value to this technique. It also helps in removing any kind of project bias regarding the selection of alternatives while planning for risks. While running the Monte Carlo simulation, it is advisable to seek active participation of the key project decision-makers and stakeholders, specifically while agreeing on the range values of the project risk variables and the probability distribution patterns to be used. This will go a long way in building stakeholder confidence in your overall risk-handling capability for the project. Moreover, this serves as a good opportunity to make them aware of the entire risk management planning being done for the project.

Though there are numerous benefits of the Monte Carlo simulation, the reliability of the outputs depends on the accuracy of the range values and the correlation patterns, if any, that you have specified during the simulation. Therefore, you should practice extreme caution while identifying the correlations and specifying the range values. Else, the entire effort will go waste and you will not get accurate results.

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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!

Here is a list of relevant articles you might want to read:

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