Monte Carlo Simulation
The JMP Profiler, with the Monte Carlo Simulator, can be used to optimize process performance in the presence of random variation. This enables you to estimate response distributions as a function of real-world random variation. Monte Carlo simulation is available from JMP Prediction Profilers using the Simulate red triangle option.
Example Setup
- Use the Column Properties > Spec Limits window for the response MODULUS to add a Lower Spec Limit = 500.
- Run the saved script RSM for 4 Responses to simultaneously fit models for the four responses.
- Scroll down to the Prediction Profiler, and select Optimization and Desirability > Maximize Desirability under the red triangle to find optimal settings for the three factors.
Using the Simulator
- Select Simulator from the Prediction Profiler red triangle menu.
- Choose to have the inputs Fixed or Random.
- Under the Simulator outline, Add Random Noise to all the responses.
- Click the button Simulate to simulate 5000 values for each response.
Simulation results:
- Histograms and summary statistics for the simulated values are displayed for each response.
- The Defect rate for MODULUS is .5%.
Tips:
- Set Response Limits to optimize responses, and set Specification Limits to produce defect rates.
- Select distributions for input variation and noise for the response to match real conditions.
- Click Make Table to simulate values to a data table with the specified number of rows (N Runs).
- Additional options are available under the red triangle for Simulator, including Simulation Experiment, which can optimize defect rates using Space-Filling Designs and Gaussian Process modeling.
- Right-click on the simulation results table and select Columns > PPM to report PPM values.
Visit Profilers > Simulator in JMP Help to learn more.