The Monte Carlo simulation is a technique to handle uncertainty.

Imagine that a new product should be launched. You define in advance
which parameters (or better variables) influence the success of the product and assign them a concrete value in the spreadsheet. Let us assume that the demand growth in a given year is assumed to be
between -3% and + 3%. In the most likely case you expect a growth rate of +2%,
which is entered in the spreadsheet. The possible cases between -3% and +3% reflect uncertainty. You can not say with confidence or certainty that the growth rate will be exactly +
2%.

This is exactly where the Monte Carlo simulation with MC FLO comes in. You define in advance how the
uncertain variables can behave by choosing effortlessly from a variety of possible distributions. For example you may choose the normal distribution, the PERT distribution
or, as shown in the picture above, the triangular distribution. If you already have data from the past and want to use it
for your calculations, you can use the built-in estimator to select a suitable distribution. With MC FLO you even have the opportunity to select
time series for forecasts.

A Monte Carlo simulation with MC FLO selects in continuation computer-aided and directly in the Excel spreadsheet hundreds or thousands of times an allowable value from the predefined
distribution function (say + 1.97%) and automatically calculates the associated result (such as the profit).
As a result, you not only get a single value ("point view"), but a variety of possible values ("bandwidths"), from which you can then make an informed decision.

The technique of Monte Carlo simulation is applicable to almost all
areas of daily life. Whether it's project
cost, finance, R & D, manufacturing or planning - wherever uncertainty takes place.

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