The engineer performed an experiment and used statistics to analyze process factors that could impact the insulating effectiveness of the product. Step-by-Step Example of Monte Carlo SimulationĪ materials engineer for a building products manufacturer is developing a new insulation product. I'll show you how to accomplish all of this right now! Simulate product results while accounting for the variability in the inputsĪlong the way, Companion interprets simulation results and provides step-by-step guidance to help you find the best possible solution for reducing defects.With Companion by Minitab, engineers can easily perform a Monte Carlo analysis in order to: Today, simulated data is routinely used in situations where resources are limited or gathering real data would be too expensive or impractical.

In the example we are about to work through, we'll change both the mean and standard deviation of the simulated data to improve the quality of a product. That's what Monte Carlo simulation is all about. You can also easily change these input distributions to answer 'what if' types of questions. 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 outputs. 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. Unfortunately, this input variability causes variability and defects in the output.

However, in the real world, the input values won’t be a single value thanks to variability. With this type of linear model, you can enter the process input values into the equation and predict the process output.

Suppose you study a process and use statistics to model it like this: This model often comes from a statistical analysis, such as a designed experiment or a regression analysis.

The Monte Carlo method uses repeated random sampling to generate simulated data to use with a mathematical model. Among the first-in-class tools in the desktop app is a Monte Carlo simulation tool that makes this method extremely accessible. How can you improve a real product with simulated data? In this post, I’ll help you understand the methods behind Monte Carlo simulation and walk you through a simulation example using Companion by Minitab.Ĭompanion by Minitab is a software platform that combines a desktop app for executing quality projects with a web dashboard that makes reporting on your entire quality initiative literally effortless. As someone who has collected and analyzed real data for a living, the idea of using simulated data for a Monte Carlo simulation sounds a bit odd.

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