The workflow for monitoring a multivariate process with sub-grouped data is similar to the one for individual data. See Example of a Multivariate Control Chart. You create an initial control chart to save target statistics and then use these targets to monitor the process.
1.
Open the Aluminum Pins Historical.jmp sample data table, located in the Quality Control folder.
2.
Select Analyze > Quality and Process > Control Chart > Multivariate Control Chart.
3.
Select all of the Diameter and Length columns and click Y, Columns.
4.
Select subgroup and click Subgroup.
5.
Multivariate Control Chart for Sub-Grouped Data, Step 1
1.
2.
Save the new data table as Aluminum Pins Targets.jmp.
1.
Open the Aluminum Pins Current.jmp sample data table, located in the Quality Control folder.
2.
Select Analyze > Quality and Process > Control Chart > Multivariate Control Chart.
3.
Select all of the Diameter and Length columns and click Y, Columns.
4.
Select subgroup and click Subgroup.
5.
Click Get Targets.
6.
Open the Aluminum Pins Targets.jmp table that you saved.
7.
Multivariate Control Chart for Sub-Grouped Data, Step 3
Multivariate Control Chart for Sub-Grouped Data, Step 3 shows indications of instability at subgroups 4-7, 9-11, 18, and 20. This result implies that these observations do not conform to the historical data from Aluminum Pins Historical.jmp, and that the process should be further investigated. To determine why the process was out of control at these points, you might want to examine individual univariate control charts or perform another univariate procedure.
1.
Open the Gravel.jmp sample data table, located in the Quality Control folder.
2.
Select Analyze > Quality and Process > Control Chart > Multivariate Control Chart.
3.
Select Large and Medium and click Y, Columns.
4.
5.
Change Point Detection for Gravel.jmp