Control Chart
SPC histogram calculates multiple control chart types from time series data and displays them as histograms. It offers various chart types including XmR, XbarR, and XbarS, along with customizable control lines and aggregation options, enabling comprehensive process monitoring and analysis.
Control Lines
A control line is a horizontal line that shows a limit or threshold. You can add custom control line values or use ones calculated from the dataset.
Calculated
 LCL  Lower Control Limit, marking the lower boundary of the control chart
 UCL  Upper Control Limit, marking the upper boundary of the control chart
 Min  Minimum of the sample values
 Max  Maximum of the sample values
 Mean  Average of all the sample values
 Range  Difference between the highest and lowest sample values
Custom
In the SPC histogram panel, there are two types of custom lines you can add:
 Static Value  Where you manually input a value for the control line.
 Feature series  Where the line's value is dynamically pulled from your feature dataset. This enhances the flexibility of your histograms by integrating limits or custom calculations dynamically from your dataset.
How to Add a Custom Control Line with field lookup

Setup Your Queries:
 Create at least two queries in your data source:
 One query for the time series data you wish to visualize in the histogram.
 Another query for the control limits or any reference values.
 Create at least two queries in your data source:

Configure the Histogram:
 By default, the histogram will attempt to calculate using all numeric values from all queries. To exclude a query meant only for reference (like control limits) from these calculations:
 Navigate to Editor > Histogram > Feature Queries.
 Select the query that contains your control limits or reference data.
 By default, the histogram will attempt to calculate using all numeric values from all queries. To exclude a query meant only for reference (like control limits) from these calculations:

Add a Control Line:
 Click on Add Control Line and select Custom.
 In the Series dropdown, choose the query that contains your reference or control limit data.

Set Position:
 Under Position Input, select Series.
 A Position dropdown will appear, displaying all available numeric fields from the selected series. Choose the field that represents your control limit.

Visualization:
 Once a numeric field is selected, it will appear as a vertical line on the histogram, representing the control limit or any other reference value.
If the selected field from your series returns more than one value, the histogram will use the last value for the control line.
Set up your reference query in the Feature Queries section. This prevents it from affecting the main data calculations for the histogram.
Visualization
You can change how a control line looks:
 Name  Display name of the control line
 Position  Xaxis position where the control line is drawn
 Series  Select the series for which to calculate the control (if you have more than one)
 Line width  Line thickness from 0 to 10
 Color
 Fill  Left fill, no fill, or right fill
 Fill Opacity
Aggregation
When the Chart type
is set to none
, you can choose an Aggregation Type
. This option lets you create new data series from the raw time series values. The available types of aggregation depend on the Subgroup Size
you pick.
For example, if you select a Subgroup Size
of 5 and choose Mean
as the aggregation type, the raw data will be split into groups of 5. Then, the average of each group will be calculated. The resulting series will be used to create the histogram.
Subgroup size = 1
 None
 Moving range
Subgroup size > 1
 Mean
 Range
 Standard Deviation
Dashboard Variable
The SPC Histogram feature includes builtin support for a dashboard variable called subgroupSize
. This allows you to control the subgroup size for multiple instances of an SPC Histogram panel using a single dashboard variable. To use this functionality, simply create a dashboard variable named subgroupSize
.
Chart Types
XmR Control Chart
The XmR Control Chart (also called Shewhart's Control Chart or IMR) tracks process variability based on samples taken over time. It consists of two charts: the individual (X) chart and the moving range (mR) chart. This chart is useful when it's hard to use rational subgroups, such as in automated processes.
Individual (X) Chart
The individual chart shows each data point over time, helping analyze central location. It reveals whether the process centers around a mean value.
Moving Range (mR) Chart
The moving range chart displays the difference between consecutive readings, showing process variability. The chart's average and upper control limit (UCL) help spot unusual patterns or shifts in the data.
XbarR Control Chart
The XbarR Control Chart is used when you have subgroups of data. It consists of two charts: the Xbar chart and the Rbar chart. This chart is useful for processes where you can collect samples in small subgroups.
Xbar Chart for XbarR
The Xbar chart plots the average (mean) of each subgroup over time. It helps monitor the central tendency of the process. The control limits on this chart are based on the average range of the subgroups.
Rbar Chart for XbarR
The Rbar chart plots the range of each subgroup over time. It helps monitor the variability within the subgroups. The control limits on this chart are based on the average range of all subgroups.
XbarS Control Chart
The XbarS Control Chart is similar to the XbarR chart but uses standard deviation instead of range to measure variability. It's often preferred for larger subgroup sizes (typically more than 10).
Xbar Chart for XbarS
This chart is identical to the Xbar chart in the XbarR system. It plots the average of each subgroup over time. However, the control limits are calculated using the average standard deviation instead of the average range.
Sbar Chart for XbarS
The Sbar chart plots the standard deviation of each subgroup over time. It helps monitor the variability within the subgroups. The control limits on this chart are based on the average standard deviation of all subgroups.
Using XbarS charts instead of XbarR charts can provide more sensitive detection of changes in process variation, especially for larger subgroup sizes.