Automating Gauge R&R On JMP for Large Number of Variables

[#1]

Hello, 

I'm currently running Gauge R&R report through JMP, and the steps I do is to use Variability/Attribute Gauge to generate the Gauge R&R table with measurement sources and variation. I'm using the steps below: 

The issue is that after I run the variability chart, I need to manually generate the gauge RR table below: 

After that, I use combined data table feature on the measurement source table to export the gauge RR tables for the selected variables.

The issue is that I have hundreds of variables and for each variable it takes up to maybe three minutes to generate the gauge RR table. 
This is really time inefficient and I would like to ask if there is a JSL code or other way to generate the gauge R&R table for given tests and export it to excel in more efficient way.

Your help is very much appreciated. 

[#2]

If I remember correctly these assume you have set your limits to column properties  How to Generate Structured Gauge R&R Reports Across Multiple Tests (with Summary Tables) has also link to some other methods. 

[#3]

Thanks @jthi This is very helpful. I see in the example it has Site as the X variable, in my case I have in that sequnce, DIB_id, Site, and then Part_id. I assume that code should be like this in my case:

Names Default To Here(1);

dt = Open("$SAMPLE_DATA/Semiconductor Capability.jmp");

var = dt << Variability Chart(
	Y(:NPN1, :PNP1),
	Model("Main Effect"),
	X(:Dib_id),
	X(:SITE),
	X(:Part_id),
	Historical Sigma(0),
	Invisible
);
var << "Gauge R&R"n();
wait(0);

result_ref = Report(var)[OutlineBox("Gauge R&R")];
wait(0);

dt_result = result_ref[TableBox(1)] << Make Combined Data Table;

var << close window;

Does that sound right? Thanks a lot! 

[#4 · Accepted solution]

Build the variability chart manually once and modify the script as needed. If I remember correctly all of them will go to same X

X(:Dib_id, :Site, :Part_id)

Make sure they are in correct order to get the correct model (also make the required changes to the Model).