Complete the following steps to interpret an NP chart. Key output includes the NP chart and the test results.

The NP chart plots the number of defective items (also called nonconforming units). The center line represents the average number of defectives. The control limits, which are set at a distance of 3 standard deviations above and below the center line, show the amount of variation that is expected in the number of defectives.

Red points indicate subgroups that fail at least one of the tests for special causes and are not in control. If the same point fails multiple tests, then the point is labeled with the lowest test number to avoid cluttering the graph. If the chart shows out-of-control points, investigate those points.

Out-of-control points can influence the estimates of process parameters and prevent control limits from truly representing your process. If out-of-control points are due to special causes, then consider omitting these points from the calculations. For more information, go to Specify subgroups to estimate parameters for NP Chart.

The chart shows that, on average, 25 of the delivery vehicles are out of service each day. The number of defective units for day 19 is out of control. The manager should identify any special causes that contribute to the unusually high number of defectives.

When you hold the pointer over a red point, you can get more information about the subgroup.

Investigate any subgroups that fail the tests for special causes. By default, Minitab conducts only Test 1, which detects points that fall outside of the control limits. However, if you conduct additional tests, then points can fail multiple tests. The Session window output shows exactly which points failed each test, as shown here.

These results show that subgroup 19 failed Test 1.

TEST 1. One point more than 3.00 standard deviations from center line.
Test Failed at points: 19
* WARNING * If graph is updated with new data, the results above may no longer
be correct.

When you use several tests at the same time, the sensitivity of the chart increases. However, the false alarm rate also increases, which can make you react to the test results unnecessarily.

For more information on each of the tests and when to use them, go to Using tests for special causes in control charts.