Monte Carlo Simulation
Calculate Many Different Outcomes and Their Probabilities of Occurrence with Monte Carlo Simulation Software
Monte Carlo Analysis Probabilistically Assesses the Impact of Risk
Risk and forecast analysis is part of every decision you make. We are constantly faced with uncertainty, ambiguity, and variability. And even though we have unprecedented access to information, we can’t accurately forecast the future. Monte Carlo simulation (also known as the Monte Carlo method) lets you see all possible outcomes of your decisions, including the actual probabilities each will occur. This lets you quantitatively assess the impact of risk, allowing for more accurate forecasting and, ultimately, better decision-making under uncertainty.
What is Monte Carlo Simulation?
The Monte Carlo method is a computerized mathematical technique that allows people to quantitatively account for risk in forecasting and decision-making. At its core, the Monte Carlo method is a way to use random samples of parameters to explore the behavior of a complex system. A Monte Carlo simulation is used to handle an extensive range of problems in a variety of different fields to understand the impact of risk and uncertainty.
A Way to Account for Risk
Monte Carlo Simulations have assessed the impact of risk in stock prices, project management, AI, and many other real-life scenarios.The Monte Carlo method provides a number of advantages over predictive models with fixed inputs, such as the ability to conduct sensitivity analysis or calculate the correlation of inputs.
A Forecast Analysis Tool That Works in Many Fields
The technique is used for forecasting, which takes into account risk, uncertainty and variability. Project managers and decision-makers use the Monte Carlo Simulation tool to estimate the impacts of various risks on the project cost and project timeline. Using this method, one can easily find out what will happen to the project schedule and cost in case any risk occurs. The Monte Carlo Simulation is used in many different fields, including:
- Finance & Banking
- Energy & Utilities
- Manufacturing & Consumer Goods
- Construction & Engineering
- Insurance & Reinsurance
- Logistics & Transportation
- Environmental Conservation
- Aerospace & Defense
- Healthcare & Pharmaceuticals
- Agriculture & Food Safety
- Consulting & Legal
- Entertainment, Sports & Media
- Mining & Minerals
- Technology & Telecom
Use cases run the gamut and include cash flow analysis, capital investments, reserves estimation, pricing, cost estimation, project management, product pipeline analysis, portfolio optimization, supply chain risk, and more.
A Range of Outcomes
Monte Carlo simulation furnishes the decision-maker with a range of possible outcomes and the probabilities they will occur for any choice of action. It shows:
- the extreme possibilities
- the outcomes of going for broke and for the most conservative decision
- along with all possible consequences for middle-of-the-road decisions
History of Monte Carlo Simulation
The technique was first used by scientists working on the atom bomb; it was named for Monte Carlo, the Monaco resort town renowned for its casinos. Since its introduction in World War II, Monte Carlo simulation has been used to model a variety of physical and conceptual systems.
How Monte Carlo Simulation Works
Monte Carlo simulation performs risk analysis by building models of possible results by substituting a range of values—called a probability distribution—for any factor that has inherent uncertainty. It then calculates results over and over, each time using a different set of random values from the input probability distributions. Depending upon the number of uncertainties and the ranges specified for them, a Monte Carlo simulation could involve thousands or tens of thousands of recalculations before it is complete. The result of a Monte Carlo simulation is a range – or distribution – of possible outcome values. This data on possible results enables you to calculate the probabilities of different outcomes in your forecasts, as well as perform a wide range of additional analyses. Monte Carlo simulation software builds a spreadsheet model that lets you evaluate your plan numerically, allowing you to change the numbers, ask ‘what if’ and see the results.
By using probability distributions for uncertain inputs, you can represent the different possible values for these variables, along with their likelihood of occurrence. Probability distributions are a much more realistic way of describing uncertainty in variables of a risk analysis, making Monte Carlo simulation far superior to common “best guess” or “best/worst/most likely” analyses.
To use Monte Carlo simulation, you need to build a qualitative model of your business activity, plan or process. The best way to do this is creating a spreadsheet model using Microsoft Excel and using Palisade’s @RISK Analysis software. Analyze the results of your simulation by using the mean, percentiles, standard deviation, in addition to charts and graphs. Palisade’s Monte Carlo simulation software will help you interpret your data and is backed by 24/7 technical support and assistance.
Common Probability Distributions Include
Random Sampling Versus Best Guess
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. 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. This allows you to identify and mitigate factors which cause the most risk.
- 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 go up or down, others go up or down accordingly.
An enhancement to Monte Carlo simulation is the use of Latin Hypercube sampling, which samples more accurately from the full range of values within distribution functions and produces results more quickly.