As the name suggests, it seems exciting to know what Monte Carlo simulation is. The term Monte Carlo simulation has its charisma, it sounds complicated. But in reality, it’s not complicated as we think. Let’s see if we can nail down the entire process in straightforward terms.

Risk analysis is a part of every decision we make in construction projects. We continuously face uncertainty and variability, and we always have new access to information that cannot accurately predict the future. Monte Carlo simulation, also known as Monte Carlo method allows you to see all the possible consequences of your decisions and evaluate the impact of risk, allowing for better decision making under uncertainty.

What is Monte Carlo Simulation?

In a straightforward language, Monte Carlo is the easiest way to understand how the inputs into a system may affect the outputs.

Monte Carlo simulation is a computerized mathematical technique that allows people to calculate risk in decision making and quantitative analysis. The method is widely used by the professionals in fields like project management, manufacturing, engineering, research, and development, etc.

Monte Carlo enables the decision-maker with a range of possible outputs and the probabilities that will occur for any choice of action.

The technique was first used by scientists working on the atom bomb and thus named as Monte Carlo, the Monaco resort town famous for its casinos. The simulation has also been used to model diversified physical and conceptual systems.

Let’s take an example here, let’s say you have coins put on a table. You count tails and heads and come up with a ratio that you plot on a graph. You then perform the same experiment 1000 times to see what the results and the chart look like. The entire process is Monte Carlo method. Now, if you programmed a computer to model the same process and ran the model 1000 times or more, the end graphing you get is termed as Monte Carlo simulation.

Monte Carlo Analysis

How does Monte Carlo Simulation Work?

Monte Carlo simulation performs risk analysis by building models of potential results, and also by replacing a range of values for any factor that has an integral uncertainty. It then evaluates results over and over, each time using a different set of random values from probability functions, depending upon the number of risks and the ranges defined for them. A Monte Carlo could involve thousands or more recalculations before they are complete.

A Monte Carlo simulation provides distribution of possible outcome values. By using probability distributions, variables can also have different assumptions about different outcomes that are occurring. The probability distributions are a more realistic way of describing uncertainty in variables of a risk analysis.

Benefits of Monte Carlo Simulations

A Monte Carlo simulation furnishes a project’s decision-maker with a scope of possible results, and also the probabilities of each outcome that might take place. A Monte Carlo simulation provides some advantages as follows:

• Probabilistic Results: In this, the result not only shows what could happen but how likely each outcome is to occur.
• Graphical Results: With the data generated by Monte Carlo simulation, it is facile to create graphs of different outcomes. Their chances of occurrence become significant for communicating findings to the other involved stakeholders.
• Sensitivity Analysis: In the Monte Carlo simulation, it is easy to see which inputs have the most significant effect on bottom-line results.
• Scenario Analysis: Using Monte Carlo simulation, the analysts can exactly see which inputs had what values together when a particular outcome occurs.