Statistical Process Control Chart Basics
Many companies employ SPC throughout various operations in their facility. Individuals who manage the SPC programs may be familiar with the formulas required to calculate control chart limits, but they are not always aware of the basic requirements or "rules" for control charts that must be followed in order to obtain valid results. There are many prerequisites or considerations for all control charts. These general requirements are specific as to whether you have variable or attribute data, whether or not the subgroup sizes are constant or changing, and how much sensitivity for variation detection is desired. A facility using SPC that is unaware of these requirements can be making mistakes, or errors in judgment, without knowing they have violated the general rules and in return may be actually harming the process rather than improving it. The analysis of control chart requirements and rules can be somewhat extensive. The following text illustrates just a sample of the basics of different types of control charts and their associated rules.
View The Top Seven Run Rules for control charts...Click Here
The Statistical Solutions SPC Charts Basic Document* 
VARIABLES CONTROL CHARTS (XBAR/S, XBAR/R and I charts for example)  
Variables control charts for subgroup data are powerful and simple visual tools for determining whether a process may be in or out of control.  




Control charts can help you determine whether the process average (center) and process variability (spread) are operating at normal levels. Control charts help you focus problemsolving efforts by distinguishing between common and specialcause variation.  
A variables control chart for subgroup data will consist of the following:  






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A graphical example of a basic control chart is shown at the right with a center line (green) and upper/lower control limits (red).  
Control charts evaluate the patterns of variation for stability through the use of tests for special causes. If you detect special cause variation, you should seek out the factors that contribute to this variation so that you can implement corrective actions.  
XBAR/S CHART vs. XBAR/R CHART  
Both XBar/S charts and XBar/R charts measure subgroup variability. The S chart uses the "standard deviation" to represent the spread in the data and the R chart uses the "range". Both charts lead to a similar estimate of the process standard deviation and similar control limits for the charts. The calculation of the range uses only two data points  the largest and smallest values  while the calculation of the standard deviation uses all the data from the subgroup. R charts are not as sensitive to small amounts of variation as the S chart. You must decide what is most important for your specific requirements when deciding between an S chart and an R chart.  
XBar Chart (Averages)  
The Xbar chart is where the subgroup averages or mean values are plotted. Probability shows us that the averages of our processes tend to stay constant unless specialcause is present. A process can be behaving normally for the averages and at the same time be considered outofcontrol for the R and S charts. The reverse is also true, R and S charts can remain incontrol while the averages become outofcontrol.  
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S Chart (Standard Deviations)  
Use the S chart when the subgroup sizes are nine or greater. S charts use all the data collected to calculate the subgroup and process standard deviations. S charts provide a more accurate indication of the process variation and result in a chart that is very sensitive to small changes in the process average. You should consider using S charts for processes with a high rate of production, when data collection is quick and inexpensive, or when increased sensitivity to variation is desired. S charts can detect smaller amounts of variation when compared to R charts. The only negative aspect in managing an S chart is the need to perform the more difficult calculations for the standard deviation which typically are accomplished by using a computer.  
R Chart (Ranges)  
Use the R chart when your subgroup sizes are eight or less. R charts are efficient for small subgroup sizes and are easier to manage due to basic shop math calculations that need to be performed. R charts can be highly influenced by a single data value from the subgroup.  
I Charts (Individuals)  
When collecting samples to learn about a process, it is sometimes easier to combine the samples into subgroups, if it makes sense to group the samples together. When grouping is not appropriate, then a subgroup size of one (1) provides a method for evaluating the process. Samples that cannot logically be grouped together are good candidates for individuals (I) and moving range (MR) charts.  
Examples of conditions that make using subgroups unfeasible or undesirable could be similar to the following:  












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ATTRIBUTES CONTROL CHARTS (P and NP charts for example)  
Attributes control charts represent a rational sample of data sampled from the process and are either counts (n) of the number of defectives or defects per sample, or proportions of the defectives or defects per sample (%).  




Control charts can help you determine whether the process average (center) and process variability (spread) are operating at normal levels. Control charts help you focus problemsolving efforts by distinguishing between common and specialcause variation.  
An attributes control chart for subgroup data will consist of the following:  






P CHART vs. NP CHART  
An attribute defect is a product or service in which a nonconformity (or flaw) renders the product or service unusable. Examples of this type of defect include broken articles, late deliveries, unanswered calls, scratched paint, and flat tires. Attributes can have only one of two outcomes, pass/fail, good/bad, go/nogo, etc.  
P Chart (Proportion Defective..%)  
Use P charts to study the proportion of defectives in each sample and determine whether or not the process is in control. Use P charts when your subgroup sample sizes vary.  
NP Chart (Number Defective..n)  
Use NP charts to examine the number of defectives in each sample and determine whether or not the process is in control. You should not use an NP chart when your subgroup sample sizes vary because the control limits and center line change when sample size changes. This variation in subgroup sample sizes and changing limits would make the NP chart difficult to manage and interpret.  
View The Normal Distribution  An
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