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 Minitab.
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.
Workspace provides the
following analysis methods to help you further improve the results of the initial
simulation.
- Parameter Optimization: Identifies optimal settings for the
inputs that you can control. Workspace searches a range of values for each
input to find settings that meet the defined objective and lead to better
performance of the system.
- Sensitivity 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.
A
Monte Carlo Simulation 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?
How-to
- 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, Workspace can examine
historical data in a CSV file and recommend a
possible distribution.
- Run a Monte Carlo simulation. Go
to Add a Monte Carlo Simulation
- Perform a parameter optimization.
Go to Perform a parameter optimization
- Perform a sensitivity analysis.
Go to Perform a sensitivity analysis
