Attribute control chart

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Attribute control chart
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Attribute Control Chart are the charts depicting the go or no-go or count information, which includes the number of defective units, the number of defects in a unit, the number of complaints received from dissatisfied customers, and the bacteria count found in the food sample. Attribute control charts are suitable when related to attribute data (male and female) and when theoretical distributions almost fit the model. Another type of attribute is the conformance of the product/process with the target. The measurement of the data is typically based on continuous variables; for example, when the diameter of the sweets is over the limit, then the attribute is ‘defective’. Patterns and rules to indicate an out-of-control condition are similar for both variables and attribute control charts. Similar to the variable control chart, the attribute control chart consists of several types of charts, depending on the types of data and the purpose of the process control[1].

A key disadvantage of an attribute control chart is that it loses the opportunity to acquire lots of information during the transformation of continuous measurements to attributes[2].

Control Charts for Attribute Data

Attribute data is data that can't fit into a continuous scale, but instead is chunked into distinct buckets, like small/medium/large, pass/fail, acceptable/not acceptable, and so on. Although it is preferable to monitor and control products, services, and processes with more sensitive continuous data, there are times when continuous data is simply not available, and all you have is less sensitive attribute data. But don't despair, because certain control charts are designed specifically for attribute data to draw out starling information and allow you to control the behavior of your process.

With knowledge of only two attribute control charts, you can monitor and control process characteristics that are made up of attribute data. The two charts are the[3][4]

  • p (proportion nonconforming) and the
  • u (non-conformities per unit)charts. Like their continuous counterparts, these attribute control charts help you make control decisions. With their control limits, they can help you capture the true voice of the process.

Proportion Defective Chart (p-chart)

The proportion defective contour chart (p-Chart) is also known as a percent chart, a fraction nonconforming chart, a fraction defective chart, or simply as a p-chart. ASQ ( American Society for Quality) defines a p-chart as a „control chart for evaluating the stability of a process regarding the percentage ( or given classification occurs’. The p-chart is used to detect and identify the percentage defective in each subgroup[5].

Number Defective Chart (np-Chart)

An alternative to the p-chart is the np-chart. The number defective control chart is also known as an np-chart. Compared to the p-chart, ten np-chart is a control chart for assessing the stability of a process regarding the total number of units in a sample in which an event of a given classification occurs. It is sensitive to changes in the number of defective units in the measurement process. Similar to the p-chart, the „event of a given classification” is whether the unit being examined is conforming (acceptable) or nonconforming (defective). The basic of the np-chart is considered binomial[6]

Criteria to use the np-chart are as follows[7]:

  • The n items counted are the number or of items of those n items that fail to conform to the specification.
  • Assume that p is the probability that an item will fail to conform to the specification; the value of p must be similar for each of the n items in a single sample.


The count chart the number of nonconformities chart, which is also commonly known as the c-chart, is an attribute control chart applied to assess the stability of a process regarding the count of nonconformities occurring in a sample. It was applied to determine the variation in the number of defects a constant sample size[8].


The u-chart, which is also called the counting chart per unit, is almost similar to the c-chart, which assesses the stability of a process in terms of the count of events of a given classification occurring per unit in a sample. Compares to the c-chart, which uses a constant sample size, the U-chart allows for the application of variable sizes of samples[9].

Examples of Attribute control chart

  • Pareto Chart: Pareto charts are used to identify the most important issues or defects that need to be addressed in a product or process. This chart is used to show the relative importance of various defects or complaints in terms of their frequency or number. The Pareto chart is a combination of a bar graph and a line graph. The bars represent the frequency of each defect or complaint and the line represents the cumulative frequency of all the defects or complaints.
  • Dot Plot Charts: A dot plot chart is used to graphically display the counts of different attributes. It shows the frequency of each attribute in a separate column. The data points are represented as dots and the columns are used to separate the different categories. This type of chart is useful for comparing the relative frequencies of different attributes.
  • Scatter Diagram: A scatter diagram is used to illustrate the relationship between two variables, such as the number of defects and the number of customer complaints. The data points are plotted on a graph and a line is drawn to show the correlation between the two variables. This type of chart is useful for identifying potential causes and effects.

Advantages of Attribute control chart

Attribute control charts are a valuable tool for monitoring and analyzing processes, as they provide a clear visual representation of the data. The following are the advantages of using attribute control charts:

  • They are easy to construct and understand, as the data is displayed in a simple graphical format.
  • They are useful for detecting small shifts in the process over time, allowing for more efficient corrective actions to be taken.
  • They can be used to compare processes with each other to identify differences in performance.
  • They can be used to detect trends in the process, allowing for more accurate forecasting of future process performance.
  • They provide a more accurate picture of the process performance than other methods such as Pareto charts.
  • They can be used to identify special causes of variation, allowing for more targeted corrective action.

Limitations of Attribute control chart

Attribute control charts have several limitations, including the following:

  • Attribute control charts are limited in their ability to detect small shifts in the process mean, as slight changes in the process average may not be reflected in the chart.
  • Attribute control charts require a large sample size for reliable control.
  • Attribute control charts may not be appropriate for attribute data with a non-normal distribution, as the theoretical distributions used in the chart may not accurately reflect the data.
  • Attribute control charts are not suitable for detecting non-random patterns in the data, such as runs or trends, as they do not use any statistical test to detect these types of patterns.
  • Attribute control charts can be subject to misinterpretation when the data contains multiple attributes and the chart is not properly visualized.

Other approaches related to Attribute control chart

There are a few other approaches to attribute control charts that can help to monitor the quality of a process. These include:

  • Acceptance Sampling: This involves collecting a sample from a batch of products, inspecting them, and then deciding whether the batch meets the quality requirements.
  • Statistical Process Control (SPC): This is a tool for controlling and measuring the variability of a process. It helps detect and analyze any trends in the data and take corrective action when necessary.
  • Zero Defects: This is an approach to quality control which focuses on eliminating defects from the process. It helps to reduce the number of defects and defects per unit of output.
  • Quality Function Deployment (QFD): This is a methodology that helps to identify customer needs and develop products and services to meet them.
  • Quality by Design (QbD): This is an approach that focuses on designing a product with quality in mind. It helps to ensure that products are designed and built to meet customer expectations.

In summary, Attribute Control Charts are used to monitor and control quality and can be supplemented with other approaches such as Acceptance Sampling, Statistical Process Control (SPC), Zero Defects, Quality Function Deployment (QFD), and Quality by Design (QbD). These approaches can help to ensure that products are designed and built to meet customer requirements and help to reduce the number of defects.



  1. Griffith G. (1996), p.80
  2. Griffith G. (1996),p.80
  3. Gygi C. (ed.)(2010),p.235
  4. Griffith G. (1996)
  5. Lim S. (ed.)(2019),p.80
  6. Lim S. (ed.)(2019),p.81
  7. Lim S. (ed.)(2019),p.81
  8. Lim S. (ed.)(2019),p.81
  9. Lim S. (ed.)(2019)

Author: Sylwia Szrajber