The Capability Analysis (Between/Within) generates a process capability report containing various capability indices using both between-subgroup and within-subgroup variation. It is useful when variation in the data is due to both within and between subgroups. This procedure also generates a histogram of the data with the overall and within normal curves. This procedure assumes the data is from a normal distribution.
1. Choose ProcessMA > Quality Tools > Capability Analysis > Capability Analysis (Between/Within).
2. In Variable, select the column containing the data.
· For constant subgroup size:
- Check Constant size.
- In Subgroup, enter the subgroup size.
· For non-constant subgroup size:
- Uncheck Constant size.
- In Subgroup, select the column containing the subgroup codes.
3. In Lower specification limit, enter the lower specification limit (at least one specification limit is required).
4. In Upper specification limit, enter the upper specification limit (at least one specification limit is required).
Optional
5. In Historical mean, enter the population mean or estimates from historical data, otherwise it will be estimated by the sample mean.
6. In Historical sigma (Within), enter the within-subgroup sigma, population standard deviation or estimates from historical data, otherwise it will be estimated from the data.
7. In Historical sigma (Between), enter the between-subgroup sigma, population standard deviation or estimates from historical data, otherwise it will be estimated from the data.
8. In Target, enter the target value. It will be used to calculate Cpm.
9. In Sigma tolerance, enter the interval (number of standard deviations) to calculate the capability statistics, otherwise 6 standard deviations will be used.
10. In Method (Subgroup size=1), select the method of estimating sigma when subgroup size is 1.
· Average moving range – Estimate based on the average of the moving range.
· Median moving range – Estimate based on the median of the moving range
11. In Method (Subgroup size>1), select the method of estimating sigma when subgroup size is greater than 1.
· Rbar - Estimate based on the average of the subgroup ranges.
· Sbar -Estimate based on the average of the subgroup standard deviations.
· Pooled standard deviation - Estimate based on pooled standard deviation.
12. In Moving range length, enter the number of observations used to calculate the moving range.
13. In Display capabilities in, choose Cp, Pp is you want to display capability statistics and Z, Sigma, if you want to display benchmark Z statistics.
14. In Display performance in, choose Parts per million if you want to display the expected and observed defects in parts per million, or choose Percentage if you want to display them in percents.
Note To select a column of data into a textbox, double-click on any of the column names shown in the list on the left of the dialog box while in the textbox.
Variable: Numeric.
Subgroup (For non-constant subgroup size): Text or Numeric; Must contain equal number of data points as the Variable.
Subgroup (For constant subgroup size): Integer; Must be at least 2.
Historical mean: Numeric.
Historical sigma (Within): Numeric.
Historical sigma (Between): Numeric.
Target: Numeric.
Sigma tolerance: Numeric.
Moving range length: Integer.
You work in manufacturing plant and you need to make sure that the diameter of the parts made is 300mm ± 3mm to meet technical specifications. You collected five samples each day for the last 30 days. You want to perform a capability study to evaluate if the process is capable of meeting the specifications.
1. Open worksheet ProcessMA > Tools > Data Files > Control.xls.
2. Choose ProcessMA > Quality Tools > Capability Analysis > Capability Analysis (Between/Within).
3. In Variable, select L – Diameter.
4. Check Constant size.
5. In Subgroup, enter 5.
6. In Lower specification limit, enter 297.
7. In Upper specification limit, enter 303.
8. In Target, enter 300.
9. Click OK.
Interpretation
The process does not meet the lower and upper specification limits. The process needs to be improved to reduce variation and also centered on the target.