A traditional sensitivity analysis involves testing a limited number of scenarios (e.g. base, upside and downside). Each scenario is a set of predefined inputs. This approach shows the outcomes of the model at various perspectives, but does not give a precise likelihood of a particular result to happen.

In contrast, the Monte Carlo method tests a large number (several hundreds or thousands) of ‘scenarios’ in which the inputs are drawn as random numbers. The results of the model (gross profit, IRR etc.) are also represented by ranges of numbers. Analyzing statistical patterns of those ranges the analyst can determine mathematically the chances of an output being within a specific range or being higher or lower than a certain threshold.

In this publication I am sharing a technique of Monte Carlo analysis in Excel. My approach is based on standard Excel functions and data tables without macros. The accompanying model performs essential Monte Carlo simulation: drawing random numbers of certain distribution types, making correlations, interpreting the outcomes.

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