Effective SQC in a Manufacturing Plant with JMP?

[#1]

I am old school and believe that SQC is most effective when a physical control chart is in the hands of the operators of a plant.  In this way, the operator documents the exceptional variation, the causes for the variation are therefore captured, and improvement can commence from the learning.

Has JMP solved the problem with implementing SQC electronically that still enables capturing (and learning from) assignable cause variation?  I would prefer to use JMP for broad SQC implementation as my company has many JMP licenses.

[#2]

That is more difficult, but I see two possible approaches.

First, you could use the annotation tool. You see an out of control point, click the annotation tool and type in your note. The potential problems are that the note is not part of the dataset and the note can be moved so it is not "tied" to the data point.

The other approach is to add a column to the data table to hold the comments. Make that comment column be a label column. Now you can type in whatever they like so that the comment is linked to the data point and stored with the data. Then you could turn on the label for that row. That will make the comment appear on the chart. It has the added advantage that you turn the comment on or off easily. The downside is that there is an extra step to make the comment appear.

Finally, you MIGHT be able to somehow use the new Hover Label feature to do this. I honestly do not know enough about this feature yet to know how it might work in this scenario.