Excel RANDARRAY and Monte Carlo Simulation in 2026: Build Powerful What-If Models Without Any Code

Uncertainty is a fact of life in business. Revenue forecasts, project timelines, risk assessments, and pricing models all involve variables that are not known with certainty. In the past, building a probabilistic model to account for this uncertainty required either expensive specialist software or some fairly advanced coding. In 2026, Excel's RANDARRAY function, combined with dynamic array spilling and Copilot-assisted analysis, puts Monte Carlo simulation within reach of any skilled Excel user. This guide will show you how.

What Is Monte Carlo Simulation and Why Does It Matter?

Monte Carlo simulation is a technique borrowed from the world of statistics and finance. Instead of plugging a single value into a model (like 'we assume sales growth of 10%'), you run the model hundreds or thousands of times using random values drawn from a realistic distribution of possible outcomes. The result is not a single answer, but a range of possible outcomes with associated probabilities.

This is far more honest than a single-point estimate. It shows you the best case, worst case, and most likely range of outcomes, along with how likely each scenario is. In 2026, this kind of probabilistic thinking is increasingly valued in finance, project management, supply chain, and risk management.

Understanding RANDARRAY: Excel's Random Number Generator

The RANDARRAY function generates a dynamic array of random numbers. Its syntax is:

=RANDARRAY([rows], [columns], [min], [max], [whole_number])

The parameters are:

rows: How many rows of random numbers to generate.

columns: How many columns to generate.

min: The minimum value in the range (default 0).

max: The maximum value in the range (default 1).

whole_number: TRUE for integers, FALSE (default) for decimals.

For example, =RANDARRAY(1000, 1, 0.05, 0.20) generates 1000 random decimal values between 5% and 20%, which you might use to represent a range of possible revenue growth rates.

Like other volatile functions (RAND, NOW, TODAY), RANDARRAY recalculates every time the worksheet changes. Press F9 to force a manual recalculation, or use a data table to capture the results of multiple runs.

Building a Simple Monte Carlo Revenue Model

Let us walk through a concrete example: estimating next year's revenue under uncertainty.

Step 1: Define Your Assumptions

Start by setting up a table of assumptions. Assume your base revenue is $5 million, revenue growth rate varies between 5% and 25%, and cost ratio varies between 60% and 75%. These ranges come from historical data and management judgment.

Step 2: Generate Random Scenarios

In cell B5, enter: =RANDARRAY(1000, 1, 0.05, 0.25). This creates 1000 random growth rate scenarios.

In cell C5, enter: =RANDARRAY(1000, 1, 0.60, 0.75). This creates 1000 random cost ratio scenarios.

Step 3: Calculate Revenue and Profit for Each Scenario

In cell D5, enter: =5000000*(1+B5#). This calculates projected revenue for each of the 1000 scenarios using the spill reference from B5.

In cell E5, enter: =D5#*(1-C5#). This calculates profit for each scenario.

Step 4: Analyze the Distribution

Now you have 1000 revenue and profit scenarios. Use standard Excel functions to understand the distribution:

=MIN(E5#) to find the worst-case profit

=MAX(E5#) to find the best-case profit

=AVERAGE(E5#) to find the expected (mean) profit

=PERCENTILE(E5#, 0.10) to find the 10th percentile (value exceeded 90% of the time)

=PERCENTILE(E5#, 0.90) to find the 90th percentile

Visualizing Monte Carlo Results with a Histogram

Numbers alone do not communicate uncertainty as effectively as a chart. Excel 2026's histogram chart type is perfect for visualizing Monte Carlo output:

Select the column of profit values (E5#).

Go to Insert > Charts > Statistical > Histogram.

Excel automatically bins the values and creates a frequency distribution chart.

Right-click the horizontal axis and select Format Axis to adjust the bin width to a meaningful level.

The resulting histogram shows the shape of your outcome distribution. A bell-shaped curve suggests normally distributed outcomes; a skewed shape tells you that extreme outcomes in one direction are more likely than the other.

Using NORM.INV for More Realistic Distributions

A limitation of simple RANDARRAY(min, max) is that it generates uniformly distributed random numbers, meaning every value between min and max is equally likely. Real-world variables often follow a normal (bell curve) distribution, where values near the mean are more likely than extreme values.

To generate normally distributed random numbers in Excel, combine NORM.INV with RAND:

=NORM.INV(RANDARRAY(1000,1), mean, standard_deviation)

For example, if your growth rate has a mean of 12% and a standard deviation of 4%, you would enter:

=NORM.INV(RANDARRAY(1000,1), 0.12, 0.04)

This creates 1000 random growth rates that cluster around 12% and taper off toward the extremes, much like real business outcomes.

Using Excel Copilot to Set Up and Interpret Your Model

Excel Copilot in 2026 can assist with Monte Carlo setups in several ways. You can describe your model in natural language and ask Copilot to build the formulas, or you can ask Copilot to analyze the output of your simulation:

'Create a Monte Carlo simulation with 1000 runs for project duration based on a normal distribution with mean 90 days and standard deviation 15 days.'

'What is the probability that profit exceeds $500,000 based on the values in column E?'

'Create a chart showing the distribution of outcomes in column E.'

Copilot translates these requests into the right combination of RANDARRAY, NORM.INV, COUNTIF, and chart commands, dramatically reducing the time needed to set up a functional simulation model.

Locking Results with a Data Table

One drawback of RANDARRAY is that it recalculates every time the worksheet updates, meaning your results change constantly. To capture a snapshot of results for reporting, use Excel's Data Table feature (What-If Analysis > Data Table) to run the simulation a fixed number of times and capture each run's output. This gives you a stable dataset for your analysis rather than a constantly shifting one.

Conclusion: Embrace Probabilistic Thinking in Excel

Monte Carlo simulation with RANDARRAY represents a shift from deterministic to probabilistic thinking, and it is a shift that pays dividends in any field where uncertainty matters. With Excel 2026's dynamic array spilling, Copilot assistance, and built-in histogram charts, you can build a working probability model in under an hour.

Start with the simple uniform distribution model described in this guide, then graduate to NORM.INV for more realistic inputs. Run at least 1000 iterations for stable results, and always visualize the distribution with a histogram. Your stakeholders will appreciate the honest, range-based forecast far more than a single-number guess.

Have you tried Monte Carlo simulation in Excel before? Tell us about your use case in the comments below.