Understanding the Monte Carlo Analysis in Project Management

A good plan can help you in risk analysis, but it can not guarantee that your project will run smoothly.

If you are associated with any stage of software project development life cycle, you most probably agree with this quote. It is important to have a smartly constructed plan in place to complete a software project on time. In addition, it is essential to rethink that plan at many stages so as to make sure it works!

This ‘re-thinking’ involves manual and automated measures. It can help you stick to the project timeline and meet the client’s requirements. Risk Management and risk quantification are the two most crucial aspects of this ‘re-thinking’. 

Both of them make sure that a project manager diagnoses all the risks associated with the project well ahead of time and has all the resources & measures to counteract them. There are many automated tools that could offer great help like ProProfs Project Management Software, which is simple but very effective to use. This software offers a lot of functionalities and helps managers during the project life cycle.

There is a well-known technique that helps project managers to spot potential risks: Monte Carlo Analysis. So, let’s learn more about this technique, its benefits, shortcoming, etc. that make it such a popular choice.

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What is Monte Carlo – A Brief Overview

The Monte Carlo Analysis is a risk management technique, which project managers use to estimate the impacts of various risks on the project cost and project timeline. Using this method, one can easily find out what will happen to the project schedule and cost in case any risk occurs. It is used at various times during the project life cycle to get the idea on a range of probable outcomes during various scenarios.

Let us take an example to make things clearer.

Suppose you are estimating the timeline of a project and have come up with the best-case scenario and the worst-case scenario. If everything goes according to your plan, there will be no delays with respect to tasks. As a result, you will complete the project in 12 months.

However, if anything goes haywire, the project completion time will increase maximum upto 15 months. This the worst-case scenario as far as business growth is concerned. This is where Monte Carlo Analysis comes into the picture as it lets you find quantified estimates:

• Chances of completing the project in 12 months: 15%
• Chances of completing the project in 13 months: 50%
• Chances of completing the project in 14 months: 80%
• Chances of completing the project in 15 months: 100%

With the help of this quantified data, project managers not only get a clear idea about the project timeline but also communicate with higher-ups or clients regarding the progress and costs of the project. This slashes the odds of potential quarrels to a large degree and bolsters client relationships.

All in all, the Monte Carlo risk management technique offers great help while project scheduling & costing, and it also enables project managers to handle unrealistic demands and expectations of higher-ups and clients in the best possible manner. Now, you are likely to understand why the popularity of this technique is skyrocketing.

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Monte Carlo Analysis – How it Works

Let us understand this technique in a stepwise manner.

• All the tasks of a project are allotted and the data is fed to the Monte Carlo automation.
• Various timelines are shown by the tool such as the probabilities of completion of one task in a specific number of days (as discussed in the example given above).
• Now that the probable timelines of the various tasks are generated, a number of simulations are done on these probabilities. The number of Monte Carlo simulation project management ranges in a few thousand and all of them generate the end dates.
• Hence, the output of the Monte Carlo Analysis is not a SINGLE value, but, a PROBABILITY CURVE. This curve depicts the probable completion dates of various tasks and their probability values.
• This curve enables project managers to come up with the most probable and intelligent schedule of the project completion and submit a credible report of a project timeline to the clients and higher management.
• Similarly, the Monte Carlo project management technique is used to generate the costing or budget for a project.

By now you must have grasped why the Monte Carlo simulation in project management is the best technique to formulate the most credible project plans.

Project costs and project schedules are vulnerable to various types of risks, such as a lack of resources. Monte Carlo, a risk management technique, is the best way to tackle such types of risks. Hence, it is drawing the attention of more project managers with every passing day.

Now, let us check out the pros and cons of this technique.

Monte Carlo Analysis – Benefits

• Offers objective and insightful data for decision making
• Empowers the project managers to create a more practical project schedule and cost plan
• Better assessment regarding project milestones
• Better assessment of cost overruns and schedule overruns
• Better risk quantification

Monte Carlo Analysis – Limitations

• Initial estimation of the best-case scenario, the most likely scenario, and the worst-case scenario are done by the project manager.
• It works in the best possible manner if you provide pertinent values in the first place. So, the evaluating process can go all wrong if the erroneous data gets entered.
• The Monte Carlo simulation in project management works for an entire project, instead of individual tasks. So, everything has to be sorted out before using it.

Phew, it was quite a discussion on Monte Carlo Analysis, let us have a quick look at the various probability curves and their meanings in the next section.

Monte Carlo Analysis Probability Curves – Types and Meanings:

1. Bell Curve or Normal Curve:

The values in the middle of the curve have the maximum probability of occurrence.

2. Lognormal Curve:

It has asymmetric or skewed values that cannot go below zero and have unlimited positive potential. Stocks, oil reserves and real estate sectors have such curves.

3. Uniform Curve:

All values have equal odds of occurring. These curves are mostly found in future revenues and manufacturing cost scenarios.

4. Triangular Curve:

The manager inputs the best-case, worst-case, and most-likely values. The curve shows the most likely odds for the most probable values.

The PERT and Discrete curves are the other two types of curves. In PERT, values are not that much extreme, while you get individual probability in the Discrete Curve Distribution.

So, this brings us to the completion of our discussion on the Monte Carlo Analysis. We tried to cover up everything, be it benefits, limitations, working, or probability curves. We hope that it is helpful for all our readers and they start using the technique in a better and smarter manner for their project and risk management.

You can take a quick look at the various FAQs related to the Monte Carlo project technique in the following section.

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Frequently Asked Questions

Q. What is meant by the Monte Carlo simulation?

Monte Carlo simulation is a technique to find objective data regarding the project costs and schedules by submitting the following three values:

• The best-case scenario values
• The most likely scenario values
• The worst-case values

The simulation is a risk management technique, which allows project managers to communicate factual and more reliable scheduling and costing plans to clients and higher-ups.

Q. What is Monte Carlo famous for?

Monte Carlo is famous for being the best risk management technique in which various simulations are used to find out the odds of different outcomes in any project, which are otherwise very complex to determine, owing to random variables.  The Monte Carlo Analysis of widely used to evaluate the risks and uncertainty in forecasting and prediction models.

Q. What is the Monte Carlo method used for?

As stated above, the method is used to find out the odds of the occurrence of various events or risks or activities during the lifetime of a project when a large number of random variables makes it highly tough to grasp the overall picture.