Himani Azmi

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Attribute charts are a kind of control chart to display information about defects and defectives. They help you visualize the enemy–variation!

Just like the name would indicate, Attribution Charts are for attribute data–data that can be counted. In other words, the data that counts the number of defective items or the number of defects per unit. At the same time, variable charts like  X̅ and R charts are used for measurable quantities such as length, weight, and height.

If your process can be measured in attribute data, then attribute charts can show you exactly where in the process you’re having problems, if any.

Defect:

A defect is a non-conformity that does not meet the customer’s requirements. A product or service may have one or more defects but is defective only if defects prevent the product from functioning.

Defects are the subsets of defective and can be categorized as minor, medium, and major based on criticality. Often non-conformity is used to signify the defects. For example, dimension differences or the failure of visual, safety, and functional requirements, etc.

Defect analysis is carried out based on the Poisson distribution (evaluates the rate of defects in the process) using the following methods.

Defective:

A product or service that has one or more defects and it is not suitable for use. In other words, a product or service is defective if the defect(s) existing in it affects its functionality.

Often non-conforming is used to signify the defectives—each product or service experiences only two choices, i.e., defective or not.

Using the following methods, a defective analysis is carried out based on Binomial distribution (evaluates the proportion of defectives in the process).

The control chart is a graph used to study how a process changes over time. A control chart always has a central line for the average, an upper line for the upper control limit, and the lower line for the lower control limit. The control limits are ±3σ from the centerline.

Selection of the appropriate control chart is very important in control charts mapping, otherwise ended up with inaccurate control limits for the data.

Attribute charts are used for charting either-or conditions over time for either static sample sizes (ex 10 samples every week) or varying sample sizes.

Six Sigma certification exams like to throw curveballs about how and when to apply certain attribute charts to different situations. Here’s a quick way for you to determine which chart to use in which situation.

There are four types of Attribute Charts:

Assuming that one or more defects in a product make the product entirely defective, you can use the following guide to pick which one to use. For example, If the Quality inspector monitors 100 bottles of information every shift, then it is a constant lot size. Similarly, if the quality inspection monitors 10% of bottle information from the production, the lot size varies based on the number of bottles produced on that particular shift.

There are four main attribute charts. Let’s take a close look at each.

p chart is also known as the control chart for proportions. It is generally used to analyze the ratios of non-conforming or defective items in a process.  It uses a binomial distribution to measure the proportion of defective or non-conforming units in a sample.

p chart is one of the quality control charts used to assess trends and patterns in counts of binary events (e.g., pass, fail) over time. p charts are used when the subgroups are not equal in size and compute control limits based on the binomial distribution.

Step 1) Calculate each subgroup’s non-conformities rate= np/n

Step 2) Compute centerline p̅ = total number of defectives / total number of samples =Σnp/Σn

Step 3) Find Control Limits: Calculate the upper control limit (UCL) and lower control limit (LCL). If LCL is negative, then consider it as 0. Since the sample sizes are unequal, the control limits vary from sample interval to sample interval.

Step 4) Plot the graph with the proportions on the y-axis, Lots on the x-axis: Draw the centreline (p̅) and control limits (UCL and LCL). Interpret the data to determine whether the process is in control.

np chart is also known as the control chart for defectives (d-chart). It generally monitors the number of non-conforming or defective items in the measurement process.  It uses binomial distribution to measure the number of defectives or non-conforming units in a sample.

np chart is one of the quality control charts used to assess trends and patterns in counts of binary events (e.g., pass, fail) over time. np chart requires that the sample size of each subgroup be the same and compute control limits based on the binomial distribution.

Step 1) Count the number of defectives in each sample

Step 2) Compute p̅ = total number of defectives / total number of samples =Σnp/Σn

Step 3) Calculate centreline np̅ = total number of defectives/no of lots = Σnp/k

Step 4) Calculate the control limits

Step 5) Plot the graph with proportion on the y-axis and the number of samples on the x-axis: Draw the centreline (np̅) and control limits (UCL and LCL). Interpret the data to determine whether the process is in control.

c chart is also known as the control chart for defects (i.e., counting the number of defects). It generally monitors the number of defects in consistently sized units.

c chart is one of the quality control charts used to track the number of defects in a product of a constant size

Step 1) Count the number of defects in each sample

Step 2) Compute centreline c̅ = total number of defects / number of samples =Σc/k

Step 3) Calculate the control limits

Step 4) Plot the graph with the number of defects on the y-axis and lots on the x-axis: Draw the centreline (c̅) and control limits (UCL and LCL). Interpret the data to determine whether the process is in control.

u chart is also known as the control chart for defects per unit chart. It is generally used to monitor the count type of data where the sample size is greater than one.

u chart is one of the quality control charts used to monitor the number of defects per unit of variable sample size.

Step 1) Calculate the number of defects per unit in each lot.

Step 2) Compute centreline u̅= total number of defects / number of samples =Σc/Σn

Step 3) Calculate Control limits: Since the sample sizes are unequal, the control limits vary from sample interval to sample interval.

Step 4) Plot the graph with the number of defects per unit on the y-axis and lots on the x-axis: Draw the centreline (u̅) and control limits (UCL and LCL). Interpret the data to determine whether the process is in control.

Question: Which of the following control charts is most appropriate for monitoring the number of defects on different sample sizes?

(A) u(B) np(C) c(D) p

Answer:

Which of the following control charts is used to monitor discrete data?

(A) p(B) I & mR(C) X Bar(D) X Bar – R

Answer: