Monte Carlo simulation quantifies uncertainty through random sampling, helping forecast outcomes and improve risk-aware decisions. Microsoft Excel makes simulation accessible with tools like @RISK, and lets users model and visualize thousands of scenarios directly in spreadsheets. Accurate setup matters, so define risks clearly, validate data, and choose proper probability distributions to ensure reliable insights.
The 2020s have been defined by uncertainty. From global pandemics to shifting markets and the rapid rise of AI, unpredictability has become the norm. This uncertainty makes forecasting more crucial than ever.
Monte Carlo simulation is one forecasting tool that helps account for uncertainty, even in high-stakes situations. By modeling thousands of potential outcomes, Monte Carlo simulation helps decision-makers understand the full range of possibilities—and how likely each will occur.
The technique can be applied to nearly any industry or challenge, including:
Fortunately, a technique this versatile doesn’t require a dedicated computer system. With Monte Carlo simulation software, like Lumivero’s @RISK, you can design and run Monte Carlo simulations within Microsoft Excel—using the same spreadsheets and formulas you already know.
This article offers a brief guide to building Monte Carlo simulations in Excel and offers links to specific examples for various industry applications.
Monte Carlo simulation is a statistical modeling technique that takes thousands of random samples from within defined ranges and behaviors to create a probabilistic forecast of events.
While it doesn’t remove uncertainty, it helps you quantify it. This enables more confident planning, better resource allocation, and improved decision-making under uncertainty. For a more detailed explanation of how Monte Carlo simulation works, read "An introduction to Monte Carlo simulation."
Excel is a useful platform for running Monte Carlo simulations, primarily due to its widespread use across industries. By integrating @RISK as an Excel add-in, you can apply Monte Carlo methods without learning an entirely new system.
Benefits include:
With @RISK, you can design, run, and visualize Monte Carlo simulations in minutes—right inside Excel.
This section provides a step by step guide to building a Monte Carlo simulation in Excel.
Consider the following when setting up your simulation model in Excel with @RISK. In this process, you will run Monte Carlo simulations to analyze uncertain variables and explore possible outcomes.
Identify the risk variables you want to explore based on what you are trying to understand. These risk variables will be treated as random variables in the simulation, representing the sources of uncertainty in your model. For example, your manufacturing company might want to forecast revenue for a product line that relies on a supply chain with variable availability.
You’ll then need to choose which variables are dependent (the variables you are trying to predict for—in this case, revenue) and the independent variables (the risks—in this case, the various supply chain factors).
These random variables will be assigned random inputs during each simulation run, allowing the model to capture a range of possible outcomes and better assess risk and uncertainty.
The next step is to assign each risk independent variables selected above a probability range which defines the possible outcomes and their likelihood, as well as a potential impact (for example, how much the risk affects revenue).
For some scenarios, uniform distributions can be used when all outcomes within a specified range are equally likely; in this case, the minimum and maximum values set the boundaries of the distribution. For normal distributions, the mean value is a key parameter that defines the center of the distribution and represents the most probable outcome.
Depending on what you are trying to model, you may already have a pre-existing risk register or other datasets that contain these defined ranges. You’ll want to pull this information into an Excel spreadsheet.
Use the @RISK tools within Excel to define your model’s distribution. This can vary depending on the scenario you are trying to model, but it should include the mean (average) and standard deviation.
To find specific examples by industry, check out the article, "Monte Carlo Simulation Examples." For model inspiration, explore Lumivero’s “Big Book of Models: The Ultimate Guide to Analytical Models for Smarter Business Decisions.”
Now that you have defined your risks, their probabilities, and the model parameters, it is time to start running Monte Carlo simulations in Excel. @RISK lets you run your Monte Carlo simulation hundreds or thousands of times.
Increasing the sample size, or the number of simulation runs, can help you more accurately estimate the range of possible outcomes. Running more simulations improves the reliability and stability of your results, especially when dealing with complex models or highly variable inputs. You can also set a stop value in your data table to specify the number of simulation iterations to perform. A higher number of simulations gives you better-defined results, but will take longer to run.
@RISK will produce an output tab where you can review your results. The simulation generates random observations, which can be visualized as a distribution. When plotting the results of a normal distribution, the data is displayed along the x axis, forming a bell-shaped normal curve. You should be able to determine the most likely scenarios, identify the most common risks, and also see which risks pose the greatest threat to your project or forecast.
@RISK offers a wide range of charting tools to help you produce visualizations of simulation results for a variety of stakeholders without having to leave Excel. You can also generate reports comparing different scenarios, such as varying the sales price, to analyze its impact on expected profit and better understand the outcomes of your simulations.
For insights into the various visualizations you can create with Lumivero software, download “The Big Book of Visualizations.”
While @RISK makes developing Monte Carlo simulation models within Excel simple, care must be taken to ensure the model is valid.
Keep these best practices in mind:
By following these principles, you’ll build models that not only look right but truly inform better decisions.
When made accessible by Excel-based tools like @RISK and DecisionTools Suite, Monte Carlo simulation allows for intelligent foresight and actionable strategic planning. While it won't eradicate risk, it helps map the terrain of risk to allow for better strategic planning.
Ready to start making better decisions using Monte Carlo simulation software? Request a demo of @RISK today!
