Attribute control chart: Difference between revisions
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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<ref>Gygi C. (ed.)(2010),p.235</ref><ref>Griffith G. (1996)</ref> | 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<ref>Gygi C. (ed.)(2010),p.235</ref><ref>Griffith G. (1996)</ref> | ||
* p (proportion nonconforming) and the | * 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. | * 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)== | ==Proportion Defective Chart (p-chart)== | ||
Revision as of 20:40, 22 May 2020
| 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.
C-Chart
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].
U-chart
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].
References
- Gygi C. (ed.)(2010), Six Sigma For Dummies, John Wiley & Sons, New Jersey
- Griffith G. (1996), Statistical Process Control Methods for Long and Short Runs, ASQ Quality Press, USA
- Lim S. (ed.)(2019),Statistical Process Control for the Food Industry: A Guide for Practitioners and Managers, John Wiley & Sons, New Jersey
Footnotes
Author: Sylwia Szrajber
