For a given process, do you think everyone would create a similar looking control chart and make a similar statement relative to process control and predictability? What about their statement about its process capability for given specification limits? Not necessarily. Process statements are not only a function of process characteristics and sampling chance differences but can also be dependent upon sampling approach.
This can have dramatic implications:
To illustrate how different interpretations can occur, let’s analyze the following process time series data to determine its state of control and predictability and then its capability relative to customer specifications of 95 to 105 [see Table 1].
Figure 1: X bar and R control chart, Source: Figure 12.7 of IEE Volume III
This type of data traditionally leads to an X bar and R control chart, as shown in Figure 1. Whenever a measurement on a control chart is beyond the upper control limit [UCL] or lower control limit [LCL], the process is said to be out of control. Out of control conditions are called special cause conditions, and out of control conditions can trigger a causal problem investigation. Since so many out of control conditions are apparent in Figure 1, many causal investigations could have been initiated. But out of control processes are not predictable, and no process capability statement should be made about how the process is expected to perform in the future relative to its specification limits.Figure 2: Individuals control chart of one sample from each subgroup. Source: Figure 12.8 of IEE Volume III
When creating a sampling plan, we may select only one sample instead of several samples for each subgroup. Let’s say this is what happened and only the first measurement was observed for each of the 10 subgroups. For this situation we would create an individuals control chart like the one shown in Figure 2.This control chart is very different from the x̄ andRcharts shown in Figure 1. Since the plotted values are within the control limits, we can conclude only common cause variability exists and the process should be considered to be in control or predictable.
The dramatic difference between the limits of these two control charts is caused by the differing approaches to determining sampling standard deviation, which is a control limit calculation term. To illustrate this, let’s examine how these two control chart limit calculations are made.
For X bar charts, the UCL and LCL are calculated from the relationships
where the X double bar is the overall average of the subgroups, A2 is a constant depending upon subgroup size and the R bar symbol is the average range within subgroups.
For X charts the UCL and LCL are calculated from the relationships
where the MR bar symbol is the average moving range between subgroups.
The limits for the X bar chart are derived from within-subgroup variability [the R bar symbol], while sampling standard deviations for XmR charts are calculated from between-subgroup variability [ the MR bar symbol].
Which is the best approach? In my next blog, I will describe why the individuals control chart is typically a better choice than the X bar and
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.
Conclusion
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.
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