Monte Carlo simulation software

Choose from Normal, Lognormal, Triangular, PERT, Discrete, and 35+ other distributions. @RISK automatically fits distributions to your historical data with statistical goodness-of-fit ranking.

Tornado charts, scatter plots, and regression analysis identify which inputs have the greatest impact on your outputs — so you know exactly where to focus your risk management.

@RISK uses Latin Hypercube sampling to produce accurate results with fewer iterations — sampling more efficiently across the full range of each distribution than standard Monte Carlo sampling.

Model interdependent variables accurately. When one input moves, correlated inputs move with it — reflecting real-world relationships that single-point estimates ignore entirely.

Every simulation run is reproducible and documented. @RISK maintains a full record of model inputs, distribution assumptions, and results — ready for internal review, audit, or regulatory submission.

No data exports. No parallel tools. @RISK installs as an Excel add-in and works directly inside your existing models — adding simulation capability without changing your workflow.

@RISK is a Monte Carlo simulation add-in for Microsoft Excel. It replaces fixed input values in your spreadsheet with probability distributions, runs thousands of simulation iterations, and produces a full probability distribution of outcomes — not a single point estimate.   

Every simulation runs directly within your existing Excel model and is fully reproducible and auditable. Risk analysts use @RISK to quantify uncertainty in cost estimates, financial forecasts, project schedules, reserve calculations, and capital investment decisions.

AI tools like ChatGPT, Anthropic’s Claude, and Microsoft Copilot can help explain Monte Carlo concepts, generate simulation code, and summarize outputs. What they cannot do is run a validated, reproducible Monte Carlo simulation inside your Excel model. @RISK performs the computation. AI tools support communication.

Used together — with RISK running the simulation and AI helping interpret and report results —risk analysts move faster without sacrificing rigor.  For organizations in regulated industries, @RISK's audit trail and documented methodology are non-negotiable. An AI-generated output has no provenance. A simulation run in @RISK does.

@RISK extends Excel’s native calculation engine with a validated Monte Carlo simulation framework. You define uncertainty by replacing fixed input values with probability distributions—either selecting the best-fit distribution for your data or using @RISK’s automatic distribution fitting. Then, you run the simulation.

@RISK recalculates your model thousands of times, each iteration drawing a new set of random values from your input distributions. Instead of a single answer, you get a full probability distribution of outcomes—showing not just what could happen, but how likely each outcome is.

Or “bell curve.” The user simply defines the mean or expected value and a standard deviation to describe the variation about the mean. Values in the middle near the mean are most likely to occur. It is symmetric and describes many natural phenomena such as people’s heights. Examples of variables described by normal distributions include inflation rates and energy prices.

Values are positively skewed, not symmetric like a normal distribution. It is used to represent values that don’t go below zero but have unlimited positive potential. Examples of variables described by lognormal distributions include real estate property values, stock prices, and oil reserves.

All values have an equal chance of occurring, and the user simply defines the minimum and maximum because they have no knowledge of which values are more likely than others. Examples of variables that could be uniformly distributed include manufacturing costs or future sales revenues for a new product.

The user defines the minimum, most likely, and maximum values. Values around the most likely value have a higher chance of occurring. Variables that could be described by a triangular distribution include past sales history per unit of time and inventory levels.

The user defines the minimum, most likely, and maximum values, just like the triangular distribution. Values around the most likely value have a higher chance of occurring. However, values between the most likely and extremes are more likely to occur than the triangular; that is, the extremes are not as emphasized. An example of the use of a PERT distribution is to describe the duration of a task in a project management model.

The user defines specific values that may occur and the likelihood of each. An example might be the results of a lawsuit: 20% chance of positive verdict, 30% chance of negative verdict, 40% chance of settlement, and 10% chance of mistrial.

Portfolio optimization, derivatives pricing, credit risk, cash flow analysis, and investment decision modeling under uncertainty.

Reserves estimation, capital project risk, commodity price volatility, and integrated energy development cost modeling.

Project schedule and cost risk, contingency analysis, earned value forecasting, and infrastructure investment appraisal.

Loss reserve estimation, catastrophe modeling, pricing under uncertainty, and capital adequacy analysis.

Supply chain risk, production cost variability, supplier reliability modeling, and inventory optimization under demand uncertainty.

ost, schedule, and reliability risk for mission-critical programs-, from development through lifecycle operations.

Purchase the Professional or Industrial version and run simulations today.

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