After you collect this information, you are ready to run a simulation.
If you're not sure which distribution to choose and you have historical data from the system, choose Use data to decide. Then, browse to the CSV file that contains data. Be sure that the data represents expected future performance. Workspace automatically finds the best-fitting distribution. For more information, see "Choose a distribution" in this topic.
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.
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.
In Monte Carlo simulations, it is typical for simulated responses to violate the assumption of normality. Therefore, Workspace uses a nonparametric method to calculate capability in the simulation tool because it works for both normal and nonnormal data. 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 Workspace Monte Carlo simulation. You can choose which label to display in the simulation results. Choose and select the label you prefer.
Based on the spread in the data and the specification limits you set in the model, Workspace calculates ppl and ppu to find the corresponding Ppk. As with typical capability calculations, Workspace takes the distances between the process "center" and each spec and divides by the spread of the distribution to get ppl and ppu. The smallest value is chosen to represent the capability.
Workspace 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, and then rerun it. This lets you test several "what if" scenarios so that you can get insight into the behavior of your system and make better decisions.