Values around the most likely are more likely to occur. The user defines the minimum, most likely, and maximum values, just like the triangular distribution. 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.
Examples of variables that could be uniformly distributed include manufacturing costs or future sales revenues for a new product. Examples of variables described by lognormal distributions include real estate property values, stock prices, and oil reserves.Īll values have an equal chance of occurring, and the user simply defines the minimum and maximum. It is used to represent values that don’t go below zero but have unlimited positive potential. Values are positively skewed, not symmetric like a normal distribution. Examples of variables described by normal distributions include inflation rates and energy prices. It is symmetric and describes many natural phenomena such as people’s heights. Values in the middle near the mean are most likely to occur. Or “bell curve.” The user simply defines the mean or expected value and a standard deviation to describe the variation about the mean. Probability distributions are a much more realistic way of describing uncertainty in variables of a risk analysis.Ĭommon probability distributions include: Monte Carlo simulation produces distributions of possible outcome values.īy using probability distributions, variables can have different probabilities of different outcomes occurring. 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.
It then calculates results over and over, each time using a different set of random values from the probability functions. Monte Carlo simulation performs risk analysis by building models of possible results by substituting a range of values-a probability distribution-for any factor that has inherent uncertainty. Since its introduction in World War II, Monte Carlo simulation has been used to model a variety of physical and conceptual systems. 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. 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. Monte Carlo simulation furnishes the decision-maker with a range of possible outcomes and the probabilities they will occur for any choice of action. The technique is used by professionals in such widely disparate fields as finance, project management, energy, manufacturing, engineering, research and development, insurance, oil & gas, transportation, and the environment. Monte Carlo simulation is a computerized mathematical technique that allows people to account for risk in quantitative analysis and decision making.
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