We’re the difference between taking a guess and taking control. At Palisade, we turn the art of decision-making into a science that works for you.
In 1980, founder Sam McLafferty developed his own Monte Carlo simulator on an Apple II with a built-in modeling language. This innovation sowed the seeds for the Palisade’s genesis in 1984, right out of Sam’s basement. The first product was PRISM, a stand-alone Monte Carlo tool for the IBM PC.
From thereon, Palisade continued to steadily grow, developing its first @RISK software in 1987 for Lotus 1-2-3 for DOS. @RISK continues to be our flagship product today. The DecisionTools Suite followed, giving users a full set of integrated programs for risk analysis and decision making. The Suite was an immediate hit for academics, and has seen steady commercial adoption ever since.
Throughout the decades, Palisade has never stopped innovating. And while our technologies have changed with the times, our core values have stayed the same: to provide world-class risk solutions and customer service, giving analysts and decision-makers the tools to make the best decisions possible.
Our innovative software solutions create usable insights from uncertain situations. As the leading provider of risk and decision analysis software for three decades, Palisade enables companies and organizations to evaluate risk at any level and decide what step comes next.
With offices around the world, Palisade offers a truly global presence. We are proud of our global team, and our ability to offer sales and technical support for Palisade software. Learn more about our Management Team
And, with hundreds of thousands of decision makers using our products at top research universities and Fortune 500 companies, Palisade has a diverse client base that spans a broad range of industries and organizations.
Our flagship tools, @RISK and the DecisionTools Suite, bring powerful new analytics to Microsoft Excel and Project. By harnessing the power of Monte Carlo simulation, decision trees, optimization, and other techniques, Palisade’s products enable users to fully understand risks and make better decisions.
Palisade also offers personalized risk solutions, allowing for customized software applications that integrate @RISK and other Palisade technology with your organization’s models. Along with our cutting-edge software solutions, Palisade provides business consulting, model-building services, and on-site training designed around your organization’s specific needs and goals.
@RISK for Risk Analysis
From the financial to the scientific, anyone who faces uncertainty in their
quantitative analyses can benefit from @RISK. @RISK helps both Fortune 100
companies and private consultancies paint a realistic picture of possible
scenarios. This allows businesses to not only buffer risks, but also identify
and exploit opportunities for growth.
@RISK (pronounced “at risk”) is an add-in to Microsoft Excel that lets you analyze risk using Monte Carlo simulation. @RISK shows you virtually all possible outcomes for any situation—and tells you how likely they are to occur. This means you can judge which risks to take on and which ones to avoid—critical insight in today’s uncertain world.
- Works With Excel
- Avoid Pitfalls and Uncover Opportunities
- Plan Better Strategies
- Identify Factors Causing Risk
- Communicate Risk To Others
@RISK enables endless applications, including these in :
- Cash Flow & Financial Analysis
- Enterprise Risk Management
- Portfolio Optimization
- Cost Estimation
Features To Meet Your Needs
Monte Carlo Simulation
By sampling different possible inputs, @RISK calculates thousands of possible future outcomes, and the chances they will occur. This helps you avoid likely hazards—and uncover hidden opportunities.
100% Excel Integration
@RISK integrates seamlessly with Excel’s function set and ribbon, letting you work in a familiar environment with with results you can trust.
@RISK identifies and ranks the most important factors driving your risks, so you can plan strategies—and resources—accordingly.
Graphs and Reports
@RISK offers a wide variety of customizable, exportable graphing and reporting options that let you communicate risk to all stakeholders.
Extensive Modeling Features
With a broad library of probability distributions, data fitting tools, and correlation modeling, @RISK lets you represent any scenario in any industry with the highest level of accuracy.
Risk analysis is systematic use of available information to determine how often specified events may occur and the magnitude of their consequences.
Risks are typically defined as negative events, such as losing money on a venture or a storm creating large insurance claims. However, the process of risk analysis can also uncover potential positive outcomes. By exploring the full space of possible outcomes for a given situation, a good risk analysis can both identify pitfalls and uncover new opportunities.
Risk analysis can be performed qualitatively or quantitatively. Qualitative risk analysis generally involves assessing a situation by instinct or “gut feel,” and is characterized by statements like, “That seems too risky” or “We’ll probably get a good return on this.” Quantitative risk analysis attempts to assign numeric values to risks, either by using empirical data or by quantifying qualitative assessments. We will focus on quantitative risk analysis.
Deterministic Risk Analysis
– “Best Case, Worst Case, Most Likely”
A quantitative risk analysis can be performed a couple of different ways. One way uses single-point estimates, or is deterministic in nature. Using this method, an analyst may assign values for discrete scenarios to see what the outcome might be in each. For example, in a financial model, an analyst commonly examines three different outcomes: worst case, best case, and most likely case, each defined as follows:
Worst case scenario – All costs are the highest possible value, and sales revenues are the lowest of possible projections. The outcome is losing money.
Best case scenario – All costs are the lowest possible value, and sales revenues are the highest of possible projections. The outcome is making a lot of money.
Most likely scenario – Values are chosen in the middle for costs and revenue, and the outcome shows making a moderate amount of money.
There are several problems with this approach:
- It considers only a few discrete outcomes, ignoring hundreds or thousands of others.
- It gives equal weight to each outcome. That is, no attempt is made to assess the likelihood of each outcome.
- Interdependence between inputs, impact of different inputs relative to the outcome, and other nuances are ignored, oversimplifying the model and reducing its accuracy.
Yet despite its drawbacks and inaccuracies, many organizations operate using this type of analysis.
Stochastic Risk Analysis - Monte Carlo Simulation
A better way to perform quantitative risk analysis is by using Monte Carlo simulation. In Monte Carlo simulation, uncertain inputs in a model are represented using ranges of possible values known as probability distributions. By using probability distributions, variables can have different probabilities of different outcomes occurring. Probability distributions are a much more realistic way of describing uncertainty in variables of a risk analysis. Common probability distributions include:
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. 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 are more likely to occur. 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 are more likely to occur. 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% change of negative verdict, 40% chance of settlement, and 10% chance of mistrial.
During a Monte Carlo simulation, values are sampled at random from the input probability distributions. Each set of samples is called an iteration, and the resulting outcome from that sample is recorded. Monte Carlo simulation does this hundreds or thousands of times, and the result is a probability distribution of possible outcomes. In this way, Monte Carlo simulation provides a much more comprehensive view of what may happen. It tells you not only what could happen, but how likely it is to happen.
Monte Carlo simulation provides a number of advantages over deterministic, or “single-point estimate” analysis:
- Probabilistic Results.
Results show not only what could happen, but how likely each outcome is.
- Graphical Results.
Because of the data a Monte Carlo simulation generates, it’s easy to create graphs of different outcomes and their chances of occurrence. This is important for communicating findings to other stakeholders.
- Sensitivity Analysis.
With just a few cases, deterministic analysis makes it difficult to see which variables impact the outcome the most. In Monte Carlo simulation, it’s easy to see which inputs had the biggest effect on bottom-line results.
- Scenario Analysis.
In deterministic models, it’s very difficult to model different combinations of values for different inputs to see the effects of truly different scenarios. Using Monte Carlo simulation, analysts can see exactly which inputs had which values together when certain outcomes occurred. This is invaluable for pursuing further analysis.
- Correlation of Inputs.
In Monte Carlo simulation, it’s possible to model interdependent relationships between input variables. It’s important for accuracy to represent how, in reality, when some factors goes up, others go up or down accordingly.
Monte Carlo Simulation in Spreadsheets
The most common platform for performing quantitative risk analysis is the spreadsheet model. Many people still unnecessarily use deterministic risk analysis in spreadsheet models when they could easily add Monte Carlo simulation using @RISK in Excel. @RISK adds new functions to Excel for defining probability distributions and analyzing output results.
@RISK simulations are calculated 100% within Excel, supported by Palisade sampling and statistics proven in over twenty years of use. Palisade does not attempt to rewrite Excel in an external recalculator to gain speed. A single recalculation from an unsupported or poorly reproduced macro or function can dramatically change your results. Where will it occur, and when? Palisade harnesses the power of multiple CPUs and multi-core processors to give you the fastest calculations. Correct results-and fast-using @RISK!