Control chart selection begins with understanding your objective and what type of data you can obtain. There are variables and attributes data and therefore there are variables and attributes control charts.
Attribute Data
Attribute data is categorical data and results from counts. The count can be number defective or number of defects.
Variables Data
Variables data is continuous data and results from measurements on a continuous scale; length, width, height, weight, time, hardness, temperature, etc.
Control Charts are usually derived from samples taken from the larger population. Sampling must be collected in such a way that it does not bias or distort the interpretation of the Control Charts.
The process must be allowed to operate normally when taking a sample. If there is any special treatment or bias given to the process over the period the data is collected, the Control Chart interpretation will be invalid.
The frequency of sampling depends on the volume of activity and the ability to detect trends and patterns in the data. At the onset, error on the side of taking extra samples, and then, if the process demonstrates its ability to stay in control reduce the sampling rate.
Control Chart Selection for Variable/Continuous Data
Use I-MR, Xbar & R, and Xbar & S Charts
Individual Values (I) and Moving Range (MR) Charts are used when each measurement represents one batch. The subgroup size is equal to one when I-MR charts are used.
An Xbar-R is used primarily to monitor and control the stability of the average value. The Xbar Chart plots the average values of each of a number of small sampled subgroups.
The averages of the process subgroups are collected in sequential, or chronological, order from the process. The Xbar Chart, together with the Rbar Chart shown, is a sensitive method to identify assignable causes of product and process variation, and gives great insight into short-term variations.
Control Chart Selection for Attribute Data
Use NP, P, C and U Charts
Counting defective items (e.g. incorrect PO’s)
Counting the number of defects (e.g. number of errors in a PO)
The P Chart plots the proportion of nonconforming units collected from subgroups of equal or unequal size (percent defective). The proportion of defective units observed is obtained by dividing the number of defective units observed in the sample by the number of units sampled.
P Charts name comes from plotting the Proportion of defectives. When using samples of different sizes, the upper and lower control limits will not remain the same - they will look uneven.
The U Chart plots defects per unit data collected from subgroups of equal or unequal sizes. The “U” in U Charts stands for defects per Unit. U Charts plot the proportion of defects that are occurring.
The U Chart and the C Chart are very similar. They both are looking at defects, but the U Chart does not need a constant sample size like the sample size like the C Chart. The Control Limits on the U Chart vary with the sample size and therefore they are not uniform
A rational subgroup, commonly called a subgroup, is simply items that are alike. They are an attempt to separate common-cause and special-cause variation.
A rational subgroup could be items:
A rational subgroup is a sample of a process characteristic in which all the items in the sample were produced under very similar conditions and in a relatively short time period.
Rational subgroups are usually small in size, typically consisting of 3 to 5 units to make up the sample. It is important that rational subgroups consist of units that were produced as closely as possible to each other, especially if you want to detect patterns, shifts and drifts.
The selection of rational subgroups enables you to accurately distinguish special cause variation from common cause variation.
In the data sheet below, 5 samples which represent one subgroup, are collected each hour.
The goal is to have a "rational" for collecting the data so that variation within each subgroup is minimized. Rational sub-grouping is a key decision in control chart selection.
By collecting data this way we force a condition where only common-cause variation is expected within each subgroup. All other variation, special-cause, will therefore lie between the subgroups.
The ultimate goal of a rational subgroup is to separate special cause variation from common cause variation.
If your process consists of multiple machines, operators or other process activities that produce streams of the same output characteristic you want to control, it is best to use separate Control Charts for each of the output streams.
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