Lumenaut
Monte Carlo Risk Simulation Package
The Lumenaut Monte Carlo
Simulation package provides a range
of tools that enable the user to easily
and quickly build interactive monte
carlo risk simulation models natively
in Excel.
If you have an existing
Excel model or building one from scratch,
it is simple to place a Monte Carlo
Simulation on top of this. There are
no special formulas required, just point
and click on the individual cells you
wish to mark in the analysis and set
the parameters.
Monte Carlo Analysis is
increasingly being used in all fields
and industries where a better appreciation
of the actual likelihood of obtaining
certain results such as profitability
(Business Analysis, Project Risk Assessment),
Tolerances (Manufacturing and Six Sigma),
failures (engineering), for example,
is required.
Example of a simple financial
model
The
basic Model, Bob wishes to examine the
likely profitability of his new product
idea. So he first builds his financial
predictive model in Excel. However,
all he gets out of this model is a single
answer what he needs is an ideal of
the possible range of profits he can
make and their probabilities.
Bob decides to use Monte Carlo analysis
as this will enable him to ascribe probability
distributions to his input variables
Using the Risk Input Form,
below, he chooses suitable probability
distribution for his input parameters.
These cells are marked in green by Lumenaut.
The
following statistical distributions
are available:
- Beta
- Binomial
- Exponential
- Extreme Value
- Gamma
- Geometric
- Lognormal
- Logistic
- Negative Binomial
- Normal
- Poisson
- Triangular
- Uniform
- Weibul
Using
the Output Form, opposite,
he selects the output cells in the model,
in this case profitability, shown in
orange above by Lumenaut.
It is possible to give each individual
output cell an easy to identify name.
Finally the model can be run via the
Run Simulation Form, it is
possible to set the number of iterations
for the simulation from 1,000 to millions.
The speed of the simulation can be
adjust.
Individual names can be given to the
result sheet.
The results of the simulation are printed
in a result sheet.
Probability distribution for the output
variables, in green.
The frequency distribution table opposite.
Univariate statistics Mean, Median,
Mode, Standard Deviation, Variance,
Mean Standard Error, Variance, Range,
Max , Min , Skewness and Kurtosis.
The input distributions are shown in
blue.
Option to conduct sensitivity analysis
and export raw simulation data.
.
.
