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How do I choose the appropriate type of control chart?

Proper control chart selection is critical to realizing the benefits of Statistical Process Control. 


Steven Wachs
Principal Statistician
Integral Concepts, Inc.


Many factors should be considered when choosing a control chart for a given application. These include:

- The type of data being charted (continuous or attribute)
- The required sensitivity (size of the change to be detected) of the chart
- Whether the chart includes data from multiple locations or not
- The ease and cost of sampling
- Production volumes

For variable data, X-Bar and R (or X-Bar and S) charts are very common, however there are cases when they are not appropriate. For example, charts for multiple locations within the subgroup are utilized when a subgroup consists of measurements that may come from different distributions. Examples include:

- Multiple measurements on the same unit (e.g. diameter in 3 places)
- Units produced during the same cycle from different cavities, machining locations, filling heads, etc.

When sampling is costly, when within-sample variation is negligible, or when the detection of "small" process changes is unnecessary, charts of individual measurements are often utilized. EWMA and CUSUM charts are useful when charting individual measurements but the traditional Individuals/Moving Range charts do not provide adequate sensitivity (ability to detect process changes when they occur).

The following table may be utilized to help select an appropriate control chart for each application. The charts are segregated by data type. Charts for variable data are listed first, followed by charts for attribute data.

DATA TYPE CHARTS MONITORS  APPLICATIONS 
Variable        X-Bar and S Process average and standard deviation  High volume, single characteristic
Sample size 2 or larger 
Variable X-Bar and R Process average and range  High volume, single characteristic
Sample size between 2 and 6
Variable X and MR Process average and moving range  Sensitivity not required
Sampling is costly
Long cycle time
(Note: Normality of data must be considered.) 
Variable Deviation from Nominal Process average and range (or standard deviation) 

Short production runs (multiple parts)

All parts have similar standard deviation 

Variable Standardized X-Bar and R
Standardized X-Bar and S
Process average and range
Process average and standard deviation 
Short production runs (multiple parts)
Part standard deviations differ 
Variable X-Bar, Rb, d Process average, range between and difference between extreme locations  Multiple locations within subgroup
Location averages are statistically different 
Variable X-Bar, Rb, Rw
X-Bar, Rb, S
Process average, range (or standard deviation) within and range between subgroup  Multiple locations within subgroup
Variation within and between subgroups different 
Location averages are not statistically different 
Variable CUSUM                    Cumulative deviations from mean  Charts for individuals when X and MR are not sensitive enough 
Variable EWMA Weighted moving average  Charts for individuals when X and MR are not sensitive enough
-- --  --  -- 
Attribute np Number of Defectives Pass/Fail Data
Constant Sample Size
n > 3/p
Attribute p Proportion Defective Pass/Fail Data
Constant or Variable Sample Size
n > 3/p
Attribute Standardized p Standardized Proportion Defective Pass/Fail Data
Variable Sample Size
n > 3/p
Can be used for short production runs
Attribute c Number of Defects Multiple types of defects on unit
Constant sample size
n such that c > 7
Attribute u Number of Defects per unit Multiple types of defects on unit
Constant or variable sample size
n such that c > 7
Attribute Standardized u Standardized Number of Defects per unit Multiple types of defects on unit
Variable sample size
n such that c > 7
Can be used for short production runs