How to do a Monte Carlo Simulation in Excel

How to do a Monte Carlo Simulation in Excel

To perform a Monte Carlo simulation in Excel, you set up a model with random inputs for variables affecting your outcome, repeat the calculation across many iterations to simulate a range of outcomes, and then analyze these results to understand potential risks and uncertainties. Plugins like @RISK, Crystal Ball, RiskAMP, ModelRisk, and SimTools/RiskSim enhance Excel's capabilities, making it easier to conduct these simulations by providing tools for defining distributions, running simulations, and analyzing results. These add-ins vary in complexity and cost, catering to different user needs from basic simulations to advanced risk analysis.

Below is a simplified step-by-step guide to doing a Monte Carlo simulation in Excel for a basic project, such as estimating the potential sales revenue for a new product. After we describe how to do this in Excel, we provide a list of industry leading plugins to automate the process. Enjoy!

Define the Parameters

  • Identify the variables that will influence the outcome you're trying to simulate. For example, if you're estimating sales revenue, these might include the number of units sold, the price per unit, and variable costs per unit.

Set Up Your Excel Sheet

  • In one column, list your variables (e.g., Units Sold, Price per Unit, Cost per Unit).
  • Next to each variable, set up cells to input the assumptions for your simulation, such as average, minimum, and maximum values.

Generate Random Inputs

  • Use Excel's random number generation functions to simulate variations in each variable. For example, you can use =RAND() to generate a random number between 0 and 1, and then scale this to your variable's range.
  • For a more accurate simulation of real-world distributions, you might use =NORM.INV(RAND(),mean, standard_deviation) to generate inputs that follow a normal distribution.

Calculate Outcome Based on Random Inputs

  • Set up a formula that calculates the outcome you're interested in based on the random inputs. For the sales revenue example, this could be (Units Sold * Price per Unit) - (Units Sold * Cost per Unit).

Repeat the Simulation Multiple Times

  • To perform the Monte Carlo simulation, you need to repeat the calculation many times (e.g., 1,000 or 10,000 iterations) to see a range of possible outcomes. This can be done by copying your formulas down across many rows in Excel.
  • Alternatively, you can use a data table to automate the repetitions. Set up a column with your repeat index (1 to N where N is the number of simulations) and use the Data > What-If Analysis > Data Table feature to fill in the table based on your model.

Analyze the Results

  • Once you have all your simulated outcomes, you can analyze these to understand the potential range of your forecast. Common analysis includes calculating the average outcome, the standard deviation, and looking at percentile outcomes (e.g., what outcome is seen 90% of the time).
  • Excel's charting features can be useful here to create histograms or scatter plots of your simulation results.

Refine and Repeat as Necessary

  • Depending on your findings, you might want to refine your assumptions or the structure of your model and run the simulation again.

While Excel does not have built-in Monte Carlo simulation functionality like some dedicated statistical software, its versatility and accessibility make it a popular choice for conducting simple to moderately complex simulations. In the next section, we'll cover some software with plugins that can be used to do Monte Carlo simulations in Excel.

What are some Plugins for Monte Carlo Simulations in Excel?

There are several plugins and add-ins available for Excel that can facilitate Monte Carlo simulations, enhancing Excel's native capabilities with more powerful statistical and stochastic modeling tools. These plugins often provide a more user-friendly interface for defining distributions, running simulations, and analyzing results. Here are a few notable ones:

@RISK (Palisade Corporation)

  • @RISK is one of the most popular Excel add-ins for performing Monte Carlo simulations. It integrates directly into Excel and allows users to define distributions for variables easily, run simulations, and analyze results through graphs and statistics. It's widely used in various industries for risk analysis and decision-making.

Crystal Ball (Oracle)

  • Oracle's Crystal Ball is another leading Monte Carlo simulation tool designed for Excel. It offers sophisticated modeling, forecasting, and simulation capabilities, helping users to evaluate risk and uncertainty in their Excel models. It also includes features for optimization and time-series forecasting.


  • RiskAMP is a more affordable add-in for Excel that provides Monte Carlo simulation tools. It allows users to add probability distributions to any model and generate thousands of possible scenarios. RiskAMP is designed to be straightforward and easy to use, making it accessible for those new to Monte Carlo simulations.

ModelRisk (Vose Software)

  • ModelRisk is a comprehensive risk modeling software that integrates with Excel. It offers a wide range of probabilistic analysis features, including Monte Carlo simulation, decision trees, and optimization. ModelRisk is available in several versions, catering to different professional needs and budgets.

SimTools and RiskSim

  • SimTools and RiskSim are free add-ins that provide a basic set of tools for Monte Carlo simulation and risk analysis in Excel. While not as feature-rich as the other options, they offer a good starting point for those looking to explore simulation without a significant investment.

These plugins enhance Excel's capabilities by providing sophisticated tools for random number generation, distribution fitting, scenario analysis, and result interpretation. Some of these plugins offer trial versions too, which can be a good way to test their features and usability before making a purchase.

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