The simulation uses a mathematical model of the system, which allows you to
explore the behavior of the system faster, cheaper, and possibly even safer
than if you experimented on the real system.
The simulation provides expected values based on equations that define the
relationship between the inputs (X) and outputs (Y). These may be known
equations, or they may be based on a model that you created from a designed
experiment (DOE) or regression analysis in
Upon completion of the initial simulation,
Workspace displays a
histogram and summary statistics, including expected output values and an
estimate of their variability. If you provide specification limits, the results
also include process performance metrics.
the following analysis methods to help you further improve the results of the
Optimization: Identifies optimal settings for the inputs that you
a range of values for each input to find settings that meet the defined
objective and lead to better performance of the system.
Analysis: Identifies the inputs whose variation have the most
impact on your key outputs. Use this method along with your process knowledge
to identify the inputs that can be adjusted to make improvements.
answers the following questions.
- What distribution best fits my
input data? What values can I expect for my outputs?
- How capable is my process or
product, given uncertainty in the input parameters?
- What are the optimal settings
to achieve my goal?
- How does the variation in the
inputs affect the variation in the response?
- Identify the equations,
y=f(x), that explain the relationship between the inputs and outputs. Equations
can come from process knowledge or from a statistical analysis.
- Define the distribution of
each input variable. If you do not know which distribution to use,
examine historical data in a
CSV file and recommend a possible distribution.
- Run a Monte Carlo
- Perform a parameter
- Perform a sensitivity
For more information, go to
Add a Monte Carlo simulation.