NP Chart (Number of Defectives)
Monitor the count of defective units when your sample size stays constant. The NP chart plots the actual number of defectives per subgroup, giving an intuitive view of quality with straightforward, constant control limits.
Create NP Chart →What is an NP Chart?
An NP chart is an attribute control chart that monitors the number of defective units per subgroup when the sample size is constant. Like the P chart, it classifies each unit as conforming or defective, but instead of a proportion it plots the raw count of defectives, which many find more intuitive.
Its requirement is a constant sample size. Because every subgroup contains the same number of units, the control limits do not need to be recalculated for each subgroup and remain constant across the chart, giving it a simpler appearance than the P chart's varying limits.
Like the P chart, the NP chart is based on the binomial distribution, since each unit is a pass or fail outcome. It answers whether the number of defectives is in statistical control, and it is the natural choice when sample sizes are fixed and a count is preferred over a proportion.
In plain terms: Same as a P chart, but for when your sample size never changes, and you'd rather see the actual number of bad units than a percentage. Because the sample size is fixed, the control limits are flat lines, making it a bit simpler to read.
Key Points
Count of Defectives
Plots the number of defective units per subgroup rather than a proportion, which many find more intuitive.
Constant Sample Size
Requires a fixed sample size, so the control limits stay constant across the chart.
Binomial Basis
Based on the binomial distribution, appropriate for pass/fail classification of units.
Key Formulas
Reading the Chart
With a constant sample size the limits are flat, so interpretation is straightforward: a point beyond the limits, or a non-random pattern such as a run or trend, signals a special cause affecting the number of defectives.
The NP chart counts defective units, not defects. A unit with several flaws still counts as one defective. If multiple defects per unit must be counted, a C or U chart is required instead.
Assumptions & Validation
Constant Sample Size
Every subgroup has the same number of units.
If violated: If sample size varies, use a P chart, which adjusts limits per subgroup.
Binary Classification
Each unit is defective or not.
If violated: To count multiple defects per unit, use a C or U chart.
Independent Units
Units are independent for the binomial model.
If violated: Address dependence between units.
⚠️ Check assumptions first
The NP chart requires a constant sample size; if your sample size varies from subgroup to subgroup, you must use a P chart instead, which recalculates limits for each subgroup. Like the P chart, it counts defective units, not individual defects, so a unit with several flaws is one defective. To count defects per unit, use a C or U chart.
When NOT to Use NP Chart
Variable Sample Size
When sample size changes between subgroups, use a P chart.
Counting Defects
To count multiple defects per unit, use a C chart (constant area) or U chart (variable area).
Continuous Data
For measured variable data, use an X-bar R, X-bar S or XmR chart.
Industry Applications
Fixed-Lot Inspection
Monitor the number of defective units when a constant number is inspected each period.
Production Sampling
Track defective counts from fixed-size samples pulled at regular intervals.
Process Control
Detect shifts in the number of defectives of a stable process.
Quality Reporting
Present quality as an intuitive count of defectives rather than a proportion.
Frequently Asked Questions
What is an NP chart used for?
An NP chart monitors the number of defective units per subgroup when the sample size is constant. Each unit is classified as conforming or defective, and the chart plots the raw count of defectives rather than a proportion. It is used to determine whether the number of defectives is in statistical control, and it is the natural choice when sample sizes are fixed and a count is preferred over a fraction.
When should I use an NP chart instead of a P chart?
Use an NP chart when the sample size is constant across all subgroups and you prefer to plot the actual count of defectives rather than a proportion. Its constant sample size gives it flat control limits, which are simpler to read. If the sample size varies between subgroups, you must use a P chart instead, because it adjusts the control limits to each subgroup's size.
Why does an NP chart require a constant sample size?
The NP chart plots the raw count of defectives, and the meaning of that count depends on how many units were inspected. If the sample size varied, a count of five defectives would not be comparable across subgroups of different sizes, and the control limits could not stay constant. Requiring a fixed sample size keeps the counts comparable and the limits constant, which is what makes the chart straightforward.
What is the difference between an NP chart and a C chart?
An NP chart counts defective units, where each unit is either defective or not, and is based on the binomial distribution. A C chart counts defects, individual nonconformities, within a constant inspection area, and is based on the Poisson distribution. The distinction is defectives versus defects: use the NP chart to count bad units and the C chart to count flaws, which may be several per unit.
Does the NP chart count defects or defectives?
The NP chart counts defectives, meaning defective units. A unit is classified simply as defective or not, so a unit with several flaws still counts as a single defective. If you need to count the individual defects on units, where one unit can contribute multiple counts, a C chart or U chart based on the Poisson distribution is the correct tool instead.
What signals an out-of-control process on an NP chart?
An out-of-control signal is a point beyond the control limits or a non-random pattern such as a run of points on one side of the center line, a trend, or a cycle, judged using standard control-chart rules. Any such signal indicates a special cause has affected the number of defectives, which should be investigated and removed to return the process to statistical control.
Monitor Defective Counts With Fixed Samples
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