In Quality Control, using control charts is probably as common as putting on a pair of socks. Whether this analogy made you smile or not, it’s not at all incidental – some charts are created and used in pairs. This is also the case of the X-bar and R-chart, a combination that helps manufacturers to understand the stability of their processes and to pinpoint variation.
X-bar and R-charts are always shown together. But is there a difference between them? Are they complementing each other like peanut butter and jelly, or are they contrasting like night and day? In this article, we’ll answer this question, highlight applications for process stability, and discuss how to get the most from your quality control reporting dashboard using a smart QMS system like AlisQI.
The difference between X-bar and R-chart
Manufacturers typically use the X-bar and R-chart pair to visualize continuous data collected at regular intervals in sample subgroups. The size of the subgroups is also very important, it needs to be between 2 and 10. If your sample size is 1 or more than 10, you need to select different control charts.
Both X-bar and R-chart provide you with visual snapshots of data that are assumed to be normally distributed. The X-bar helps to monitor the average or the mean of the process and how this changed over time. The R-chart shows the sample range, which represents the difference between the highest and lowest value in each sample. Both X-bar and R-chart display control limits. Manufacturers must pay attention and study any points outside the control limits as these indicate out-of-control processes and can help locate the origins of the process variables.
So, is there a difference? The short answer is yes. The overall mean or process mean (shown by the X-bar) differs from the range statistic center line (shown by the R-chart). A closer look at how the X-bar and R-chart are interpreted shows that while they are different, the two charts are used in conjunction with one another.
When working with this chart pair to visualize your data, start by examining the R-chart first. Why? Because the control limits for the X-bar are derived from average range values (shown on the R-chart). Only if the values of the R-chart are in control, you can interpret the X-bar. If the values are out of control, this is a sign that the X-bar control limits are inaccurate.
X chart example
R chart example
Applications for process stability
Control charts like the X-bar and R-chart allow manufacturers to learn from their data. Aside from monitoring the process, the charts can also help to:
- Standardize the manufacturing process
- Determine if there are opportunities for improvement or the exact opposite, to avoid unnecessary changes
- Analyze improvement by comparing data results to historical performance
- Measure equipment performance
X-bar and R-chart - just two clicks away
Now that we’ve looked at the differences and highlighted applications for process stability, you’re probably wondering about the use of the X-bar and R-chart in a smart QMS platform like AlisQI. Control charts, including the above-mentioned pair, are part of our SPC toolkit. This wonderful set of easy-to-use statistics also includes histograms, boxplots, scatter plots, correlation plots, Cpk and Ppk indices, and more.
Unlike tools that are too complex or too expensive to use organization-wide, we wanted to bring SPC to the shop floor, make data accessible, anytime and on any device. This also means no manual calculations, no need to create the charts or reinvent them – but that we provide clear overviews that are just a few clicks away.
“If you wanted to know something ─ certain data of a production line ─ it took an absolute age but now, with AlisQI, it’s two clicks, and you have your graph.”, confirms Wendy Beks, Laboratory Manager at Berry Global.
Berry Global, a world leader in plastics, packaging, and non-woven specialty materials decided to implement a modern quality management platform to give its quality a boost. Involving the shop floor and making quality omnipresent was an important part of that decision.
Making all data accessible to everyone in one central place is where AlisQI has been the most transformative “We had real-time data before, but it was highly fragmented,” they explained. “With AlisQI, it is accessible and transparent, also for our operators who finally have a statistical tool to interpret quality data. How is that data distributed? How reliable and accurate is it? You can show your operators and process engineers trends, which gives them a lot more insight into their own data. It’s great.” Using AlisQI, all insights, overviews, and charts can be stored for quick reuse in dashboards or exported as pdfs.
Manufacturers typically use the X-bar and R-chart pair to visualize continuous data. The X-bar helps to monitor the average or the mean of the process and how this changed over time. The R-chart shows the sample range, which represents the difference between the highest and lowest value in each sample. While they are different, the X-bar and R-chart are used in conjunction with one another.
Part of the AlisQI SPC toolkit, the X-bar and R-chart pair can help manufacturers to learn from their data, to better understand the stability of their processes, and interpret quality data. Using the smart, intuitive system, these visual snapshots are just two clicks away.
It's important to note that while X-bar and R-charts are useful for analyzing continuous data, they are not suitable for all types of data. For example, if the data is not normally distributed or if the subgroup size is too small or too large, alternative control charts may be more appropriate. It's important to select the appropriate control chart based on the type of data being analyzed to ensure accurate and meaningful insights. With a modern quality management platform like AlisQI, users can access a variety of control charts and statistical tools to help them make data-driven decisions and continuously improve their processes.