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Affinity diagram

What is it?

An affinity diagram is the organized output from a brainstorming session. It is one of the seven management tools for planning. The diagram was created in the 1960s by Kawakita Jiro and is also known as the KJ method.

The purpose of an affinity diagram is to generate, organize, and consolidate information concerning a product, process, complex issue, or problem. Constructing an affinity diagram is a creative process that expresses ideas without quantifying them.

The affinity diagram helps a group to develop its own system of thought about a complex issue or problem. A group can use an affinity diagram at any stage where it needs to generate and organize a large amount of information. For example, members of a leadership team may use the diagram during strategic planning to organize their thoughts and ideas. Alternatively an improvement team can use the diagram to analyze the common causes of variation in its project. The diagram is flexible in its application and is easy to use.

What does it look like?

A completed affinity diagram is shown below. In the example, a bakery has recently expanded its business and opened a chain of retail outlets. A number of problems have arisen and the management team, involved with the retail outlets, has met to discuss the problems. The issues are complex so they have decided to complete an affinity diagram.

When is it used?

Use an affinity diagram when you can answer “yes” to all of the following questions:

  1. Is the problem (or issue) complex and hard to understand?
    If the problem or issue is relatively simple or easy to understand, a cause-and-effect diagram may be more appropriate.
  2. Is the problem uncertain, disorganized, or overwhelming?
    Complex issues often feel overwhelming due to their size.
  3. Does the problem require the involvement and support of a group?
    The process a group goes through to make an affinity diagram helps the group develop its own system of thought concerning the problem and builds consensus among the members.

Getting the most

  1. Choose a facilitator.
    The facilitator is responsible for leading the group through the steps to make the affinity diagram. It is beneficial to have a facilitator experienced in making affinity diagrams.
  2. State the issue or problem.
    Before beginning, the group should state the issue or problem to be addressed. It is often useful to state the problem in the form of a question. In the example, the question is, “What are the problems associated with our expansion into retail outlets?” It is essential that the group understands the aim of the session.
  3. Brainstorm and record ideas.
    Next, brainstorm ideas concerning the issue statement. Brainstorming for ideas to make an affinity diagram uses a mixture of traditional brainstorming and the Crawford slip method. In traditional brainstorming, individuals generate ideas, which they voice in turn. Ideas are given by each person in the group until no one has anything else to add. In the Crawford slip method, ideas are recorded on index cards, slips of paper, or sticky notes, in silence. There is no verbal exchange. Brainstorming for the affinity diagram uses a mixture of these two approaches.

The above article is an excerpt from the “Operational Definition” chapter of Practical Tools for Continuous Improvement Volume 2 Statistical Tools.

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Attributes data (counts)

What is it?

Attributes data is data that can be classified and counted. There are two types of attributes data: counts of defects per item or group of items (nonconformities ) and counts of defective items (nonconforming).

How is it used?

Attributes data is analyzed in control charts that show how a system changes over time. There are two chart options for each type of attributes data. These attributes control charts, and more, can be created easily using software packages such as SQCpack.

What type of attributes data do I have?

Counts of defective items (noncomforming)

What is it?

Nonconforming data is a count of defective units. It is often described as go/no go, pass/fail, or yes/no, since there are only two possible outcomes to any given check. It is also referred to as a count of defective or rejected units. For example, a light bulb either works or it does not. Track either the number failing or the number passing.

How is it used?

Nonconforming data is analyzed in p-charts and np-charts. Chart selection is based on the consistency of the subgroup size:

  • If the number inspected is always or usually the same, use an np-chart.
  • If the number inspected varies with each subgroup use a p-chart.

Count of defects per item (noncomformities)

What is it?

Nonconformities data is a count of defects per unit or group of units. Nonconformities can refer to defects or occurrences that should not be present but are. It also refers to any characteristic that should be present but is not. Examples of nonconformities are dents, scratches, bubbles, cracks, and missing buttons.

How is it used?

Nonconformities data is analyzed in u-charts and c-charts. Chart selection is based on the consistency of the subgroup size:

  • If the number inspected is always or usually the same, use a c-chart.
  • If the number inspected varies with each subgroup use a u-chart.

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Capability analysis: Can a process produce output within spec?

A process that is in control is not necessarily producing an output that meets customer or engineering requirements. To find out if your process is capable of producing outputs that are in spec, you should perform capability analysis.

Capability analysis is a set of calculations used to assess whether a system is statistically able to meet a set of specifications or requirements. To complete the calculations, a set of data is required, usually generated by a control chart; however, data can be collected specifically for this purpose. Easily create control charts and perform capability analysis using software like SQCpack.

Specifications or requirements are the numerical values within which the system is expected to operate, that is, the minimum and maximum acceptable values. Occasionally there is only one limit, a maximum or minimum. Customers, engineers, or managers usually set specifications. Specifications are numerical requirements, goals, aims, or standards. It is important to remember that specifications are not the same as control limits. Control limits come from control charts and are based on the data. Specifications are the numerical requirements of the system.

All methods of capability analysis require that the data is statistically stable, with no special causes  of variation  present. To assess whether the data is statistically stable, a control chart  should be completed. If special causes exist, data from the system will be changing. If capability analysis is performed, it will show approximately what happened in the past, but cannot be used to predict capability in the future. It will provide only a snapshot of the process at best. If, however, a system is stable, capability analysis shows not only the ability of the system in the past, but also, if the system remains stable, predicts the future performance of the system.

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Capability analysis for attributes

ttributes capability measures are taken directly from attributes control charts. No additional calculations are required.

The capability for a p-chart is the average proportion of nonconforming items (p-bar). The capability for an np-chart  is the average number of nonconforming items generated by the system (np-bar). The capability for a c-chart is the average number of nonconformities per subgroup (c-bar). The capability for a u-chart  is the average number of nonconformities per unit (u-bar).

A weakness in capability estimates for attributes data is that they do not suggest why a system is either capable or not. For instance, there is no way of knowing whether the system is incapable because it is not centered, it is too close to a specification limit, or it exhibits too much unit-to-unit variation. Further studies must be done to learn how to improve the system.

Capability analysis for attributes

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Any point lying outside the control limits

This is the quickest and easiest test for system stability. Look above the upper control limit and below the lower control limit to see whether any points fall in those regions of the chart. If you are looking at a chart pair (X-bar and R, X-bar and s, or X and MR), look at both charts.

Points falling outside the control limits may be the result of a special cause that was corrected quickly, either intentionally or unintentionally. It may also point to an intermittent problem. The chart below shows two points outside the control limits.

See also:
>> Analyze for special causes of variation
>> Any point lying outside the control limits
>> 7 or more points in a row above or below the center line
>> 7 or more points in one direction
>> Any nonrandom pattern

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How do I compare the Cp/Pp and Cpk/Ppk?

Assume the original target is a Cpk or Ppk of 1.0

If Cpk or Ppk is less than 1.0 If Cpk or Ppk is greater than 1.0
If Cp or Pp is less than 1.0 Variation in the process should be reduced. Not mathematically possible. Check for an error in calculations.
If Cp or Pp is greater than 1.0 The process should be centered within its specifications. Fine tune and improve the process continuously. Increase the Cpk target.

See also:
>> Cpk
>> Cp
>> Cr
>> Cpm
>> Ppk
>> Pp
>> Pr
>> Capability indices

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Capability indices: Cp

The Cp index is used to summarize a system’s ability to meet two-sided specification limits (upper and lower). Like Cpk, it uses estimated sigma and, therefore, shows the system’s potential to meet the specifications. However, it ignores the process average and focuses on the spread. If the system is not centered within the specifications, Cp alone may be misleading.

The higher the Cp value, the smaller the spread of the system’s output. Cp is a measure of spread only. A process with a narrow spread (a high Cp) may not meet customer needs if it is not centered within the specifications.

If the system is centered on its target value, Cp should be used in conjunction with Cpk to account for both spread and centering. Cp and Cpk will be equal when the process is centered on its target value. If they are not equal, the smaller the difference between these indices, the more centered the process is.

See also:
>> How do I compare the Cp/Pp and Cpk/Ppk?
>> Cpk
>> Cr
>> Cpm
>> Ppk
>> Pp
>> Pr
>> Capability indices

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Capability indices: Cpk

Cpk is a capability index that tells how well a system can meet specification limits. Cpk calculations use estimated sigma and, therefore, shows the system’s “potential” to meet specifications. Since it takes the location of the process average into account, the process does not need to be centered on the target value for this index to be useful.

If Cpk is 1.0, the system is producing 99.73% of its output within specifications. The larger the Cpk, the less variation you will find between the process output and specifications.

If Cpk is between 0 and 1.0, not all process output meets specifications.

If the system is centered on its target value, Cpk should be used in conjunction with the Cp index. Cpk and Cp will be equal when the process is centered on its target value. If they are not equal, the smaller the difference between these indices, the more centered the process is.

See also:
>> How do I compare the Cp/Pp and Cpk/Ppk?
>> Cp
>> Cr
>> Cpm
>> Ppk
>> Pp
>> Pr
>> Capability indices

Quality Advisor

Cpk or Ppk: Which should you use?

Your customer has asked you to report the Cpk of the product you are sending. You know that to compute the Cpk, you need to have the product specifications, and that you need to have the mean and sigma. As you gather the information, someone asks, “Which sigma do they want?”

You know that Cpk is calculated by dividing by 3 sigma. But which sigma should you use, estimated or calculated? Which is correct? Which would you report? Naturally, most of us would use the sigma that makes the Cpk look the best. But the sigma that makes the Cpk look best may not accurately reflect what you or your customer need to know about the process.

Confusion over calculating Cpk by two different methods is one reason that a new index, Ppk, was developed. Ppk uses the calculated sigma from the individual data.

Sigma of the individuals:

Given that Ppk uses the calculated sigma, it is no longer necessary to use the calculated sigma in Cpk. The only acceptable formula for Cpk uses the estimated sigma.

Estimated sigma:

Given that Ppk uses the calculated sigma, it is no longer necessary to use the calculated sigma in Cpk. The only acceptable formula for Cpk uses the estimated sigma.

In 1991, the ASQC/AIAG Task Force published the “Fundamental Statistical Process Control” reference manual, which shows the calculations for Cpk as well as Ppk. These should be used to eliminate confusion about calculating Cpk.

So which value is best to report, Cpk or Ppk? Although they show similar information, they have slightly different uses.

Estimated sigma and the related capability indices (Cp, Cpk, and Cr) are used to measure the potential capability of a system to meet customer needs. Use it when you want to analyze a system’s aptitude to perform.

Actual or calculated sigma (sigma of the individuals) and the related indices (Pp, Ppk, and Pr) are used to measure the performance of a system to meet customer needs. Use it when you want to measure a system’s actual process performance.

Once you determine which capability index you will use, it can easily be calculated using software such as SQCpack.