A Monte Carlo simulation considers a wide range of possibilities to help reduce uncertainty.

A Monte Carlo simulation is very flexible; it allows us to vary risk assumptions under all parameters and thus model a range of possible outcomes.

Monte Carlo simulation is a computerized mathematical technique to generate random sample data based on some known distribution for numerical experiments. This method is applied to risk quantitative analysis and decision making problems. This method is used by the professionals of various profiles such as finance, project management, energy, manufacturing, engineering, research & development, insurance, oil & gas, transportation, etc.

In engineering, Monte Carlo simulation involves using random number generators to simulate random effects. Simulating an event many times allows us to measure the variation just as we would if we took many samples of a real event.

Generally quite large simulations are required to give stable results. Monte Carlo simulation is an extremely useful and versatile technique for understanding variation in manufacturing processes and uncertainty in measurements.

In real simulations, random number generators are software functions within a computer program. These digital random number generators can produce any value from a given probability distribution and millions of values can be created in under a second. This can enable many complex effects to be simulated far more quickly than actual experiments could be performed.

Advantages or a Monte Carlo simulation approach are considerable and include:

• Provides statistical sampling for numerical experiments using the computer
• Provides approximate solution to mathematical problems
• Can be used for both stochastic and deterministic problems

Monte Carlo simulation has a reputation for being difficult, but software tools have made it easier – especially for manufacturing engineers.

Every input in your model is characterized by a mean and a variance. Identifying the correct probabilistic distribution may require a deeper knowledge of the way inputs behave. To simplify this, the triangular distribution may be used to simply indicate the minimum, maximum and the most probable values.

Want to learn more? Tonex offers Monte Carlo Simulation Training, a 2-day course that introduces participants to Monte Carlo simulation, a technique used to understand the impact of risk and uncertainty in engineering projects, project management, cost, and other forecasting models.

Monte Carlo Simulation Training course introduces fundamental issues in simulation-based analysis and Monte Carlo-based computing. Participants will learn about rigorous analysis and interpretation and an objective treatment of various approaches.