What is a Control Chart?

What is a Control Chart?

Learn how to use the control chart layer in Tulip Analytics

What is a Control Chart?

Learn how to use the control chart layer in Tulip Analytics

Control charts are a widely used tool of statistical process control (SPC). They allow monitoring of and identification of "out of control" states for values in repetitive processes that follow a known mathematical distribution (e.g. are normally distributed).

From a mathematical perspective, identifying an "out of control" process means identifying that the values occurring (e.g. the data being measured on the shopfloor) do not fit the expectations based on the statistical distribution previously collected data.

Some common use cases for control charts are monitoring the following:

• A measured dimension of individual parts or the mean value of measuring a sample of identical parts
• The duration of a repeated process
• The number of defective items in each batch

Control Chart Rules

In the control chart layer one or multiple rules are evaluated for the data. There are different rule sets used around industry (e.g. Nelson and Western Electric). Tulip currently supports the two rules that are statistically most effective in detecting out of control states without leading to a high number of false detection ("false positives"). These are:

Configuration

Prerequisites

As described in the article on What is a Control Chart layers are available for specific chart configurations. In the case of the Control Chart Layer these are the following:

• Date Source: Tulip Table
• Template: One Operation
• View: Line Chart
• x-Axis: any
• y-Axis: Numeric value (Number, Integer, Interval)

Once a chart configuration is fulfilling these requirements, the Control Chart Layer will be available in the Chart Layers panel. This can be opened by clicking on the Chart Layers button in the top right corner of the Analytics Editor.

Setup

To enable the control chart Layer, click on the toggle next to "Control Chart" in the Layers side panel. This will open up the layer settings in which the following can be set:

• Upper control limit (UCL) and lower control limit (LCL) - mandatory

• Custom center line value - optional

  • As default, the center line will be calculated as the average of UCL and LCL [(UCL + LCL)/2]

• Active rules - at least one

  • Single Outlier
  • Prominent Shift

Tulip control charts are implemented as "manual limit" control charts at the moment. This means that the control limits are input manually by the user upon initial chart configuration. (Note: This is a one-time action for a chart, as control limits should not be changed while using the chart to monitor a process). In combination with the Analytics Editor settings, this allows to implement the following standard control chart types:

• X chart
• Xbar chart
• R chart
• S chart
• c chart
• NP chart

There are different ways to identify the control limits for each of the chart types for entering them in the configuration:

• If you used another tool for SPC before switching to Tulip and your process has not changed, you can enter the same boundaries as before
• If you have already collected data within Tulip you can calculate the statistically correct limits using the equations relevant for the desired chart type based on that data.
• If you want to use the control chart more freely you can set any limits you like and use the rules with those limits of your choice to detect outliers and shifts

A custom center line value can optionally be configured to accommodate for skewed distributions.

Usage

Once the control chart layer is successfully configured, you can save the analysis and use it the same way you use any other analysis. You can:

• Add it to a dashboard
• Embed it into a Tulip App
• Make it available via a sharing link

If data in the chart triggers any of the rules selected in the configuration, the relevant data points will be highlighted in the graph as depicted below.

Further Reading

• Introduction to Chart Layers
• Forecast Layer