File Exchange > DataAnalysis >    Monte Carlo Simulation

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File Name:
Monte Carl...on.opx
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Origin 2016 SR1
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Simulate your process by supplying a transform equation and distribution parameters for input variables.

Screen Shot and Video:


  • Specify a transform equation for your process
  • Select Normal, LogNormal, Weibull, Exponential or Uniform distribution for your parameters
  • Generate output for a large number of runs, and examine Process Performance (Ppk) statistics
  • Perform sensitivity analysis to determine the effect of individual parameters, to improve or optimize your process


Download the file and drag-and-drop onto your Origin workspace. A "Monte Carlo Simulation" icon will appear in the Apps Gallery.
NOTE: This tool requires OriginPro.


  1. Click the App icon to open the dialog.
  2. Enter Input Variables, separated by "|" (do not use white space).
  3. Specify distribution type and parameters for each  Input Variable.
  4. Enter Output Variable Name.
  5. Specify the transform equation in Output Expression.
  6. Specify the Upper and Lower Spec Limits.
    Note that the "auto" values for the limits  are derived by performing 500 simulations of your model, and then by calculating the mean and SD of the output (USL = mean + 3 * sd, LSL = mean - 3 * sd).
    You can uncheck Auto to manually specify desired limits, or enter "--" to not use any limits.
  7. Specify the number of simulations to run.
  8. Optionally, select the Run sensitivity analysis directly box (You may opt to do this later from the performance results graph).
  9. Click OK to generate the report.
  10. Double-click on the embedded graph in the ResultGraph sheet. This opens the graph in a window from where you can publish the graph.
    Click on the Sensitivity Analysis button if you wish to to generate sensitivity results, and did not generate them already in step 8 above.
  11. Double-click on the embedded graph in the SensitivityGraph sheet. In the window that opens, there are options on the right side of the graph to control the sensitivity plot.


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