As a business leader, you are constantly navigating an environment of uncertainty. You need to make decisions about how to manage risks and allocate resources. It’s more important than ever to understand probabilistic thinking, and quantify uncertainty to guide your business decisions. The Monte Carlo simulation process can help you measure uncertainty and understand the likelihood of different types of risks.

With Monte Carlo simulation, you can gain clearer insights into how your business is likely to perform – in the short, medium or long term – based on multiple uncertain variables. This is a special type of risk and forecast analysis that does more than just help you make better-informed business decisions. Monte Carlo simulation actually shows you the full range of possible outcomes that can help you improve your chances of success along the way.

Let’s learn more about the Monte Carlo simulation process and see how it can benefit your organization.

What is Monte Carlo simulation?

Monte Carlo simulation is a mathematical technique that is used to estimate the possible outcomes of an uncertain event. It lets you play “what if” with uncertain variables, not just hard numbers. Think of it as “what are the chances I roll a 7 on the craps table,” but in a much larger sample size – thousands to millions of rolls of the dice.

Monte Carlo simulation was first developed in 1946 by John von Neumann and Stanislaw Ulam, mathematicians and physicists who were working as part of the Manhattan Project at Los Alamos National Laboratory. Because their team was working on nuclear bomb technologies that had never been attempted before, they needed to account for a high level of uncertain variables. The Monte Carlo simulation concept enabled these scientists to do repeated, random sampling to obtain a range of possible outcomes.

Although it’s named for Monte Carlo, the famous casino in Monaco, the Monte Carlo simulation is intended to be the opposite of gambling. Instead of taking a chance on blind luck, a Monte Carlo simulation can help you manage risks and avoid business threats. Monte Carlo simulation enables you to understand the probabilities of variable inputs and evaluate the potential risks (and rewards) of different courses of action.

How does Monte Carlo simulation benefit businesses? 

Monte Carlo simulation is a valuable forecasting tool for businesses because it helps create a richer understanding of risk and correlation of variable inputs. With Monte Carlo simulation, business leaders, business analysts, and project managers can figure out the probability of different types of outcomes and realistic scenarios.

Here are a few questions that Monte Carlo simulation can help you answer:

  • What happens to future interest rates, exchange rates, or commodity prices, based on changing market conditions?
  • What is the failure rate for a piece of machinery, depending on adverse weather conditions?
  • What are the chances of keeping a project on schedule and budget, based on certain risk factors?
  • What are the chances of a successful clinical trial for a new pharmaceutical that is being developed?
  • What locations should be explored for new sources of petroleum?
  • How much should be budgeted for advertising for a streaming video service based on expected customer acquisition costs and new customer sign-up rates?
  • If we raise prices for a subscription, what happens to customer retention and overall revenue?

Monte Carlo simulation is used across multiple industries and professional fields, including finance, insurance, manufacturing, healthcare, aerospace, construction, engineering, energy and utilities, transportation and logistics, and more.

Predict the future with Monte Carlo simulation and Quantitative Risk Analysis

Monte Carlo simulation is a powerful, versatile tool because it gives a wider range of possible outcomes, letting you see the full picture of your business’s likely future results. Typical spreadsheet forecasting can illustrate three scenarios: best, worst, and average case. But the real world is much more complex than these three simple outcomes, especially when navigating uncertain variables.

Monte Carlo simulation helps you avoid unpleasant surprises by exploring thousands of combinations of “what if” risk factors, so you can analyze a wider range of possible outcomes and obtain more accurate results. This forecasting tool gives you the most extreme scenarios (including the probable outcomes of the most conservative or the most risk-seeking, go-for-broke business decisions), and a bell curve of possible outcomes for more moderate decisions.

With Monte Carlo simulation, you’re not just analyzing risks, you’re visualizing outcomes. It gives you a more granular, nuanced understanding of the real-life scenarios that are likely to occur based on multiple variable inputs. The future is always unpredictable, but Monte Carlo simulation can help your firm adjust and align expectations for probable future outcomes.

How does Frontline Solvers’ Monte Carlo simulation capability empower your team to measure uncertainty?

Analytic Solver® includes a full-featured Monte Carlo simulation capability. Unlike other Excel-based simulation products, this feature enables you to play “what if” with uncertain values as easily as you do with ordinary numbers. Each time you run a simulation, thousands of trials are executed, and a full range of simulation results and statistics will be displayed on the spreadsheet.

Here’s what makes Analytic Solver® Monte Carlo simulation more powerful than other software solutions:

  • Up to 100x faster: Our Monte Carlo simulation capability uses Frontline’s Polymorphic Spreadsheet Interpreter technology to achieve breakthrough simulation speeds – up to 100 times faster than normal Excel-based Monte Carlo simulation. 
  • 80 analytic and custom distributions: Analytic Solver supports 80 analytic and custom distributions (continuous and discrete), and 80 statistics, risk measures and Six Sigma functions. It supports rank order correlation and copulas, time series simulation, distribution fitting, multiple random number generators, sampling methods with variance reduction, and powerful capabilities for multiple parameterized simulations.
  • Powerful graphics to assess uncertainty: Analytic Solver generates vivid charts of probability distributions, output frequency charts, sensitivity (“Tornado”) charts, scatter plots, and Overlay, Trend, and Box-Whisker charts, in two or three dimensions. You can customize chart colors, titles, legends, grid lines, and markers, resize and rotate charts, and print charts or use them in PowerPoint presentations.
  • Works with @RISK in the Cloud: If you’re a user of Lumivero’s @RISK Monte Carlo simulation, you can use Analytic Solver to convert your @RISK model to Analytic Solver, and run your @RISK model in Analytic Solver cloud version in Excel for the Web, Microsoft Teams, and even Power BI. Try it – it just works!

How does the Analytic Solver® Monte Carlo simulation process work?

The Monte Carlo simulation process consists of the following steps:

  1. Generate a random sample for the uncertain variables in your model. If you specify (say) 1,000 Monte Carlo trials per simulation, then 1,000 randomly chosen values will be generated for each uncertain variable.
  2. Adjust the random samples for all the uncertain variables that are related to others, via rank order correlation or copulas, to respect these relationships.
  3. For each of 1,000 Monte Carlo trials, the simulation recalculates your model with the right sample values in each uncertain variable cell. In your Excel formulas, the PSI Distribution function in each uncertain variable cell returns the correct sample value for that trial.
  4. On each Monte Carlo trial, monitor and save the calculated value of each uncertain function in your model. For 1,000 trials, there will be 1,000 saved values for each uncertain function.

When the simulation process is complete, Analytic Solver® uses the 1,000 saved values of each uncertain function to calculate statistics and percentiles, draw frequency distributions, scatter plots, and other charts, and compute values for each PSI Statistic function call in your model.

How much training do Line of Business teams need to use the Analytic Solver Monte Carlo simulation?

To use the Analytic Solver Monte Carlo simulation capability, your team will need to be able to build a quantitative model of the relevant business activity, business plan, or operational process that is being analyzed. This could be a simple Excel-based spreadsheet model. Your team will also need to understand basic probability and statistics concepts, such as mean, standard deviation, percentiles, and probability distributions.

Advanced assistance for your team

If you need assistance, our fully detailed tutorials and reference guides can be accessed via the Help menu within Analytic Solver®. And we will soon offer a forthcoming, built-in AI Chatbot that can answer your questions, having “learned everything” in our 2,300 pages of advanced documentation! We also provide technical support and our team is always here to answer questions if needed along the way.

See how the Analytic Solver® Monte Carlo simulation capability can help you get a clearer picture of risk analysis and forecasting. Ready to try it for free? Request your Free 15-Day Trial!

Not sure which license is best for your analyst? Never fear – a member of our team will contact you within 48 hours of your request to make sure you have the tools, examples, and help you need to start using Monte Carlo Simulation to conquer risk and uncertainty in your business.