Difference Between X-Bar and R-Chart and How They Are Used

An X-Bar and R-Chart are control charts utilized with processes that have subgroup sizes of 2 or more. They are a standardized chart for variables data and help determine if a particular process is predictable and stable. These are used to monitor the effects of process improvement theories.

X-Bar and R-Charts are typically used when the subgroup size lies between 2 and 10. These control charts can be used if you need to assess system stability, if the collected data is in subgroups larger than one but less than 11, if the data is invariable form, and if the time order of the subgroups is preserved. Using more subgroups in control limit calculations means that the analyses are more reliable.

Control charts like the X-Bar and R-Chart are often used in business applications like manufacturing, to measure equipment part sizes; in service, industries to evaluate customer support call handle times, or in healthcare for uses like measuring blood pressure over time.

X-Bar and R-Chart - How They Are Different

These control charts are used in conjunction with one another, but there are distinctions between the two.

X-Bar Chart

Indicates how the average or mean changes over time. It's utilized to monitor the process mean when calculating subgroups at regular intervals from a process. The X-Bar Chart is typically combined with an R-Chart to monitor process variables. If the variable isn't under control, then control limits might be too general, which means that causes of variation that are affecting the process mean can't be pinpointed.

Each point on the chart acts as a subgroup mean value. The process mean is the center line, and if this isn't specified, then it's the weighted mean of the subgroup means.

R-Chart

Indicates how the range of the subgroups changes over time. This is utilized to monitor process variability, like the range, when measuring subgroups less than ten at regular intervals in a process. Each point on a chart represents the subgroup range value.

The range statistic expected value is the center line for each subgroup. The center line differs when subgroup sizes aren't equal.

Applications of X-Bar and R-Chart for System Stability

When improving a system, X-Bar and R-Charts have numerous applications that enable system stability to be evaluated. Take a manufacturing plant, for instance. They may want to figure out how to improve their processes and operations by analyzing results through this statistical method.

  • First, they would want to collect as many subgroups as possible to accurately calculate control limits. These subgroups can then be used to measure system stability.
  • Once the stability has been assessed, you must figure out if the data needs to be stratified. Different results may be found between shifts among different workers, or different machines and equipment, or among different materials, for example. Data must be collected and entered in a manner that enables you to stratify by symptom, operator, location, or time.
  • X-Bar and R-Charts can be applied to analyze process improvement results. This lets a business determine how a process is running and compare it to historical performance to see if process changes produced the right improvement.
  • X-Bar and R-Charts can also be used for standardization, which is why data should be collected and analyzed throughout the process operation. Otherwise, there's no way to identify if the process has changed, or to locate the origins of the process variables.

Example - How X-Bar and R-Chart Can be Utilized

A quality engineer at automotive body parts manufacturing plant may use X-Bar and R-Charts to monitor the lengths of ignition coils. Three equipment machines manufacture these ignition coils for three shifts per day. The quality engineer has to measure five ignition coils from each machine during each shift.

An X-Bar, R-Chart can be developed for each machine to monitor ignition coil lengths.

  • Three X-Bar, R-Charts are created, one chart for each machine.
  • The engineer examines the R-Chart first because the control limits on the X-Bar charts are inaccurate if the R-Chart indicates that the process variation is not in control.
  • The R-Charts for the three machines indicate that the process variation is in control, no points are out of control, and all points fall within the control limit in a random pattern.
  • The X-Bar Charts indicate that machine 2 is in control, but machines 1 and 3 aren't . Machine 1 has gone out of control point, and Machine 3 has out of control points.

The test results for these machines (indicated in the charts above) are as follows:

  • The test results for X-Bar Chart of Machine 1 shows the test failed at point 8.
  • The test results for an X-Bar chart for Machine 3 shows that the test failed at points 2 and 14.

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