This is Part I of my blog on Application of Monte Carlo Simulation Method/Technique in Project Management series. In Part I (this article), I will provide you a very high-

So, what is Monte Carlo Simulation (MCS)?

In rather simplistic terms, the method is similar to an opinion poll or election poll, where a carefully selected statistical sample is used to predict the behaviour of a large group where the sample is originally taken from. MCS allows us to repeatedly solve a model by randomly generated values of its variables based on their respective range or probability distributions. In practice, sources of true random numbers are rare and do not produce the volume of numbers required for practical purposes. The solution is to use pseudorandom numbers, which are a sequence of numbers on the range (0, 1) that can be easily generated by a computer algorithm and satisfactorily mimic the behaviour of true randomness. Therefore, MCS is basically a statistical sampling experiment on a computer that provides “approximate” solutions to a problem. The technique is widely used in risk analysis in numerous fields including Engineering, Physics, Finance/Business, Chemistry, Biology, Medicine, Operational Research etc.

The term “Monte Carlo” is named after one of the administrative area (quarter/ward) of Monaco where the famous Monte Carlo casino is located. Although there are several isolated applications of the method reported much earlier in the history, the practical and systematic use of the method as a full-fledged numerical method capable of addressing complex problems first started during the World War II. John von Neumann and Stanislaw Ulam successfully applied the technique in 1940s in the Manhattan Project to develop nuclear weapon.

In personal life as well as in business environment, if given the choice between making the decision under greater or less uncertainty, every rational person would choose the latter. Almost all important decisions in life and business are made under some sort of uncertain conditions. This is a reality. In fact, realism about uncertainty is essential to avoid excessive risk. Overestimating uncertainty can lead to delay and lost opportunity – a costly mistake. While underestimating uncertainty is dangerous. It is a form of hubris that leads to disaster. MCS provides a rational way to quantify uncertainty.

The simulation method is well suited for problems that are analytically intractable and for which solution is time-consuming, costly, or impractical. However, MCS has some inherent drawbacks: it doesn’t give exact solutions and simulation results are only as good as the model and inputs used.

Now let’s discuss some of the MCS software/tools that are currently available in the market. In this regard, I want to emphasize that I am neither endorsing any tool listed here nor I am highlighting the differences and/or similarities how these tools build and solve a model. I am not an expert on these tools either. However, I used most of them in a few occasions. The table below lists some of the MCS tools (in alphabetic order) that are popular among professionals and academics:

Software/Tool Vendor Platform Price Point
1 @Risk Palisade Excel add-in US$1295 (standard edition)

US$1695 (professional edition)

US$2295 (industrial edition)

single user licenses

2 Crystal Ball Oracle Excel add-in C$1342/User (basic edition)

single user licenses

3 ModelRisk VOSE Risk Software Excel add-in €750 (Standard edition)

€900 (professional edition)

€1100 (industrial edition)

Single user licenses for one year

4 Risk Solver Pro FrontlineSolvers US$1,245.00

Single user licenses for one year

5 Devize Minitab Cloud US$250/user/yr
6 RiskAMP Structured Data LLC Excel ad-in US$130 (student & individual edition)

US$250 (professional edition)

Single user licenses

7 MonteCarlito Excel ad-in Free

It is worthwhile to mention here that all tools listed above have a “try before you buy” (free trial) option. Usually the trial period can run for maximum of one month. As I mentioned earlier, I am not endorsing any tool here. I listed them here only to give an idea to the readers what is out there in the market. It is up to the individual users to select a specific tool based on their need, preference, skill level, and price point. This section of the article should serve as a starting point for such a user. Users should also remember that the effectiveness and usefulness of a tool would depend on their clear understanding of the method and the validity of the model and inputs. Having a specialized tool is great, but always remember the wise saying “a fool with a tool is still a fool”.

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