XLSTAT: Attribute control charts in Excel

 

Background

Control charts by attributes analyze non-conforming products or defects to monitor product quality before delivery or at reception. Inspections are done on defined-sized units (e.g., one TV per unit, or 24 peaches per crate).

XLSTAT offers four attribute charts:

• P chart: tracks the proportion of non-conforming units.

• NP chart: tracks the number of non-conforming units.

• C chart: tracks the number of defects per inspection unit (fixed size).

• U chart: tracks the number of defects per inspection unit (variable size).

P/NP charts focus on defective units (e.g., faulty TVs, crates with bruised peaches), while C/U charts focus on total defects (e.g., defective transistors in a TV, bruised peaches per crate).

 

Dataset for Example

The data are from [Pyzdek Th. (2003); The six sigma Hanbook Revised and expanded, McGraw Hill, New York] and correspond to 30 inspections for bruised peaches of 1 crate of a production process. In this example a crate consists of 1000 peaches. For this example the NP chart will be used.

 

Setting Up

• Open XLSTAT.

• Click on the Quality menu/ Statistical Process Control menu and select the Attribute charts feature.

• The dialog box pops up.

• In the General tab, select the chart type, choose the NP Chart, and the data. The group size in this example is 1000. 

• Click on the OK button to launch the computations.

• The results are displayed on a new Excel sheet.

 

Interpreting the Results

The first result is the estimated mean.

The initial table shows the NP chart control limits, followed by data for bruised peaches and group size. For each group, it includes the central line (CL), lower and upper control limits (LCL, UCL), and zone A and B limits.

The NP chart follows. The values seem to be randomly dispersed around the control limit. The control chart lets us come to the conclusion that the process is “statistically under control”.

The next table provides the details for the special cause rules. Only “No” can be read in the table. The data is of good quality with respect to this issue. This can be reflected in the NP control chart. The group means values are always between the lower and upper control limits.

Furthermore, it is interesting to know if the data follow a normal distribution. Several tests are provided by this feature. We introduce here the results for the Jarque-Bera test  . The p-value (0.314) is higher than 5%. So, we can conclude that the Hypothesis H0 of normal distribution can be accepted.

In the Q-Q plot we see that the data are close to the first bisector line. We suppose that the data is normally distributed.

At the end of the report, the run chart is displayed. We see that the measurements inside a group have a wide range, lying between 23 and 37. The values are within the control limits.
 

 

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