2.69.5 Control Charts

1 Processing

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Control charts is a graph used to determine if a process is being controlled under stable conditions. Origin provides different kinds of controls basing on what data are collected

Processing

Select from the drop-down list and click on the icon to open dialog

Variables Charts for Subgroups

The control chart in this list are to monitor the process performance of continuous data, which are collected from subgroups. 6 kinds of control chart are provided.

Xbar-R	Xbar-S	I-MR-R/S
Xbar	R	S

Please look at the table below for when and how they should be used

Combination Charts

Charts	Components	Purpose	Usage	Comments
Xbar-R	a Xbar and a R chart	Determine is your process is stable	For subgroups that have 2~8 observations	Examine the R chart first to make sure the process variation must be in control, otherwise result of xbar chart may be inaccurate
Xbar-S	a Xbar and a S chart	Determine is your process is stable	For subgroups that have 9 or more observations	Examine the S chart first to make sure the process variation must be in control, otherwise result of xbar chart may be inaccurate
I-MR-R/S	an individuals chart, a moving range chart, and a R or S chart.	Assess the variation within and across subgroups	Consistent source of variation within the subgroups	The moving range charts shows whether the between-subgroup variation is in control, and the R or S chart shows whether within-subgroup variation is in control

Basic Charts

Charts	Description
Xbar	Plots the process mean over time for variables data in subgroups
R	Plots the process range over time for variables data in subgroups
S	Plots the process standard deviation over time for variables data in subgroups

Variables Chart for Individuals

The control chart in this list are to monitor the process performance of continuous data but not in subgroups. 4 kinds charts are provided

I-MR	Z-MR
Individuals	Moving Range

Please look at the table below for when and how they should be used

Combination Charts

Charts	Components	Usage
I-MR	An individual chart and a Moving Range chart	For continuous data that are individual observations not in subgroups
Z-MR	A standardized process mean chart and a Moving Range chart	For short run processes when little data are available

Basic Charts

Charts	Components	Usage
Individuals	Also call I-Chart. Plots individual observations over time for variables data.	Monitor the process center for individual observations
Moving Range	Also call MR-Chart. Plots the moving range over time for individual observations.	Monitor the process variation

Attributes Charts

The control chart in this list are to monitor the process performance of attribute data, such as counts of defectives for defects. 8 kinds of charts are provided

P Chart Diagnostic	P	Laney P'	NP
U Chart Diagnostic	U	Laney U'	C

Please look at the table below for when and how they should be used

know only whether each item is defective or not

Use when	Charts	Description
Subgroup size is unequal	P Chart Diagnostic	Test for overdispersion or underdispersion in your defects data
	P	Plots the fraction, percent, or proportion of nonconforming units
	Laney P'	Variation of P Chart, can adjust for overdispersion or underdispersion in your data
Subgroup size is equal	NP	Plots the number of nonconforming units when subgroup sizes are equal.

know the number of defects on each item

Use when	Charts	Description
Subgroup size is unequal	U Chart Diagnostic	Test for overdispersion or underdispersion in your defects data
	U	Plots the number of nonconformities or defects per unit
	Laney U'	Variation of U Chart, can adjust for overdispersion or underdispersion in your data
Subgroup size is equal	C	Plots the number of nonconformities or defects when subgroup sizes are equal.

Time-Weighted Charts

The Time-Weighted Charts are useful in detecting small process shifts. 3 kinds of charts are provided.

Moving Averag

EWMA

CUSUM

Please look at the table below for when and how they should be used

Chart	Description	Comments
Moving Average	Also call MA chart. Plots the unweighted moving average over time for individual observations	Useful in detecting small process shifts.
EWMA	Plots the exponentially weighted moving averages. Each EWMA point incorporates information from all the previous subgroups or observations based on a user-defined weighting factor.	Generally prefered over MA chart as all previous observations are taken into account.
CUSUM	Displays the cumulative sums (CUSUMs) of the deviations of each sample value from the target value	Useful in detecting processes that are slowly slipping away from the target value due to machine wear, calibration issues, etc.