cancel
Showing results for 
Show  only  | Search instead for 
Did you mean: 
Choose Language Hide Translation Bar
Senguttuvan
Level I

JMP yield % not matching with Power BI and Excel

Hi All, Good Day.

Refer below tables showing the gaps between excel, power BI and the JMP with the same set of raw data (JMP % mostly not comparable to Excel and power BI). I hv attached the raw JMP file as well. Could anyone advice how to make JMP yield % is comparable to excel and Power BI. I am using ver 17. tks

Senguttuvan_0-1689580899499.png

 

Monthly

Excel Yield

Power BI Yield

JMP Yield (Ave)

Delta %

in  JMP

Jan

98.79%

98.79%

98.26%

-0.53%

Feb

98.77%

98.77%

98.40%

-0.37%

Mar

98.91%

98.91%

98.49%

-0.42%

Apr

98.36%

98.36%

96.37%

-1.99%

May

99.34%

99.34%

97.73%

-1.61%

3 REPLIES 3
WebDesignesCrow
Super User

Re: JMP yield % not matching with Power BI and Excel

As a user of Excel Pivot Table to calculate yield through aggregate sum (Power BI should be similar).

You have to put a summary table or tabulate to add the formula column "sum of good die before ORT/ sum of total inspected qty".

JMP use average yield (which is not same with the formula above)

 

If you need daily report through JMP, I suggest you automate it using JSL scripting or workflow.

jthi
Super User

Re: JMP yield % not matching with Power BI and Excel

In JMP it would be best to create Summary table and perform calculations and plot from that.

jthi_2-1689583807852.png

jthi_3-1689583839468.png

You can also calculate the yield in the original table using Col Sum()s with correct byVars (month (and year)) and then use that for plotting.

jthi_0-1689583682901.png

jthi_1-1689583756139.png

(the provided JMP table did have incorrect data types so the results might be incorrect and I'm not sure which formula to use for yield calculation in this case)

-Jarmo
Ressel
Level VI

Re: JMP yield % not matching with Power BI and Excel

This should also help for clarification. Check the two cells to the very right in the attached sheet. These show you the differences between the two possible mean values: 98.79% vs 98.27%