Supply Chain Applications using Jump
Hi , I'm a supply chain professional and I'm wondering if i can find some examples on how to employ JMP in handling Supply chain areas, like upstream , production scheduling, inventory management , ....etc
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view all learning resourcesHi , I'm a supply chain professional and I'm wondering if i can find some examples on how to employ JMP in handling Supply chain areas, like upstream , production scheduling, inventory management , ....etc
In a condition, I want to check if a value belongs to a list composed of several values. What is the simplest syntax ?
The scores plot has a circle inside the scatterplot. Does this represent a confidence interval or is it more for decorative purposes? If the former, can the confidence interval be changed?
How do I remove the arrows from the loadings plot in JMP14
Hi All
I am pretty new for the Jmp scripts and I need your help :).
I got my script that allow me to plot multiple graphs, now I have to share them as JPEG. One JPEG for each graph I have, the "Fit Each Value" table is not needed. The structure of the script is like:
Fit Group(
Bivariate(
Y( :Name( "name_01" ) ), ....
), ...
Hi , I'm a supply chain professional and I'm wondering if i can find some examples on how to employ JMP in handling Supply chain areas, like upstream , production scheduling, inventory management , ....etc
The scores plot has a circle inside the scatterplot. Does this represent a confidence interval or is it more for decorative purposes? If the former, can the confidence interval be changed?
How do I remove the arrows from the loadings plot in JMP14
Report( pc ) << save picture(
"C:\Users\cschleigh\Desktop\EOM OQC 5-9-22\JMP\Output Images\test.jpg"
);
Hi,JMP15, I would like to <<save picture for each graphs from a Process Capability report with multiple Process Variables, one .jpg per graph. PC = Process Capability(
Process Variables(
:Name( "BS-Blue FWHM, Median (Pixels)" ),
:Name( "BS-Green FWHM, Median (Pixels)" ),
:Name( "BS-Blue uDOF (nm)" )...
Dear JMP Professionals, I have a question regarding confounded data for RSM DoE. If I have a 4 factor optimization DoE with RSM, how can I identify, which factor effects are confounded with other effects. I know about the confounding matrix, buth How do I identify it when I have my final dataset collected an I analyze the data.Thanks a lot for your help!! Best Simon
Is there a better way rather than iteration of all the rows to validate values in column? Let's say before making something with the column I want to make sure that all values in column match specific Regex. Like so:!IsMissing(Regex(Column(myColumn)[1], "^\w+(\d{3})\w+$", "\1"))
I can iterate through each row to make sure that all of them match, but maybe there is a better way?
HI, How to find estimators of FGNLS and NLS (Seemingly Unrelated Regressions) Using JmpPro.# Note that it is present in SAS in the proc syslin
and it is in stat in the nlsur command Regards
Hello, I am trying to do a matched sample analysis across three different factors. I wanted to:1. Confirm that my sample would be what I pick for 'Block" when I do Fit Y by X when running Friedman's test2. Is there a post hoc analysis in JMP that I can use? I dont see Nemenyi or Conover as an optionThanks for your time.
Hello everyone,I am trying to make a table with the OOT (out of Trend) batches with their respective "atypical" values.I used for that the "script alarm" function as I could found some exemples on the JMP community. Here is my script: z_KSigmaZ =1.4;
// Création de la Carte de controle si N catégories > 5
Names Default To Here( 1 );
If(N Items(Associative Array(:"RNS/NS tordu (srg)")) > 5,
Gr
...
I'm using JMP 16.2.0 and I'm trying to automatically generate a graph builder with box plots and their mean values with the group X variable as the color. I'm using a bar chart with style=floating to display the mean value (inspired by this topic)My current graph:This is almost what I want, except for a few things.1. I'd like the Group X variable (Recipe) to be visible in the legend as well. One w...
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