Understanding Monte Carlo Simulation With An Example

What Is Monte Carlo Simulation?

The Monte Carlo method uses repeated random sampling to generate simulated data to use with a mathematical model. This model often comes from a statistical analysis, such as a designed experiment or a regression analysis.

Regression Equation:

With this type of linear model, you can enter the process input values into the equation and predict the process output. However, in the real world, the input values won’t be a single value thanks to variability. Unfortunately, this input variability causes variability and defects in the output. To design a better process, you could collect a mountain of data in order to determine how input variability relates to output variability under a variety of conditions. However, if you understand the typical distribution of the input values and you have an equation that models the process, you can easily generate a vast amount of simulated input values and enter them into the process equation to produce a simulated distribution of the process output

How Can Monte Carlo Simulation Help You?

With Companion by Minitab, engineers can easily perform a Monte Carlo analysis in order to:

• Simulate product results while accounting for the variability in the inputs,

• Optimize process settings,

• Identify critical-to-quality factors,

• Find a solution to reduce defects.