To run a simulation, you must know the distribution and parameters for each input (X) and the equations that describe your process.
Equations can come from process knowledge or be based on a model that you created from a designed experiment (DOE) or regression analysis in Minitab.
If a model contains categorical factors, you can select the factor levels to include in the equation.
To compare the simulation results for the same Y variable using different factor levels, select the Y variable and the factor level to include in the equation, then import them. Repeat this process until you have selected all the factor levels to compare.
After you define the model, you are ready to run a simulation.
When you have a complex or a large simulation, you can create groups to define the model by function. For example, you might want to describe different actions or various parts' behavior within the simulation. With groups, you can categorize inputs and outputs to help you manage and organize your simulation.
Often, in Monte Carlo simulations, the simulated responses violate the assumption of normality. Therefore, Engage uses a nonparametric method to calculate capability in the simulation tool. The nonparametric method calculates the spread of the output distribution using the observed 0.135 and 99.865 percentiles of the simulated data, which is analogous to +/-3 sigma in a normal distribution.
Because there are no subgroups and no concept of long term and short term variation in the simulation context, Cpk and Ppk values are equivalent in the Engage Monte Carlo simulation. Choose , then select the label you prefer.
Based on the spread in the data and the specification limits you set in the model, Engage calculates PPL and PPU to find the corresponding Ppk.
Engage displays the results of the simulation, how your results compare to generally accepted values, and guidance for next steps.
Each time you repeat the simulation, the results will vary because the simulation is based on randomly selected values for the inputs.
After you analyze the results, you may want to return to the model and change inputs or outputs, then rerun it. This lets you test several possible scenarios so that you can get insight into the behavior of your system and make better decisions.
Watch a video to learn more about Monte Carlo simulations.