The Control Chart

The human mind is highly visual in its perception of the world. That is especially true when we try to convert data to understandable information. Very few of us can grasp the significance hidden in columns of numbers on paper. We need visual methods of presenting such data so that its significance is immediately clear. Arguably the most important graphical aid for process control is the control chart.

Figure 1. Control chart for feldspar in body batches.

What is a Control Chart?

A control chart makes process trends and performance immediately clear. Using today’s sophisticated software, generating charts is simple, fast and accurate. The only danger is that unnecessary or redundant charts will be generated, and personnel might be overwhelmed by them. That warning noted, let’s take a look at the basics of control charts.

We will use some batching data to illustrate the process. If we were to look at the batch weight for each component of each batch we process for a period of months, we might be able to spot changes, trends or increased (or decreased) variability in the data. We might miss a lot of that, however, because our minds don’t visualize well from raw data. If we instead made a simple plot of that data sequentially over time, we could see trends or changes much more easily and quickly. This is called a run chart. While it helps our visualization, it does not tell us if the process is in control or not. We expect that there will be some level of variability in a process—no worker or piece of equipment does exactly the same thing every time.

The most common control chart consists of a run chart with the average for all of the data and the upper (UCL) and lower (LCL) control limits superimposed on the data plot. Usually, the control limits are set at plus or minus three sigma (±3ð), or three times the standard deviation for the data, on either side of the average. We would expect all but about one quarter of one percent of the data, or all but one in 400 points, to fall within the 3ð limits. The process is in control if it never varies outside its normal range of variability. If data moves outside the control limits, we can assume that the process is being influenced by an extraneous or non-usual factor and is out of control.

If we were measuring mechanical strength of a small fraction of our production (or of sample bars), we would plot the average of these individual daily groups, and the generation of the control limits would be a bit more complex. We can also impose control limits that are not statistically generated from the data when, for example, critical property values must be met. A minimum strength level, for instance, might be used as the lower control limit.

Charting Feldspar Batching

In Figure 1, a control chart that was generated by a software package illustrates real batching data for feldspar, one component of a ceramic body, over a period of several months. We can see that the average for the 708 batches recorded over a number of months was 3773 lbs. (The set point was 3763 lbs.) For the first 60-70 batches, there was a lot less variation in the batch weights, and the average was a bit lower (closer to the set point).

From batches 100 to about 600, a much greater amount of variation appeared and there were a number of weights outside the control limits. The process was out of control, but because this control chart was not generated until about batch 600, the manufacturing personnel did not perceive the lack of control. A mechanical problem was found and corrected, and the system returned to its previous level of performance.

Important Points

Control charts are easy to create and can be very beneficial, but some important factors must be taken into consideration. The control chart does not tell us what has happened, only that something has happened that was outside the normal or historic process operating conditions. Additionally, the chart by itself cannot tell us if the loss of control was detrimental to the product, product yield, losses or costs. But, once we make a change to improve a process, using the control chart can help prevent a reversion to the pre-change conditions.

This has been a very cursory coverage of control charts—our goal here is to try to make clear how critically useful they can be. As with all statistical control methods, a little knowledge can be dangerous. It is important to learn a lot more about control charts before we can make intelligent use of them.

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