Effective Review and Interpretation of Control Charts

Control charts are a valuable tool for monitoring process performance.?Through the control chart, the process will let you know if everything is “under control” or if there is a problem present.?Potential problems include large or small shifts, upward or downward trends, points alternating up or down over time and the presence of mixtures.?

Variation comes from two sources, common and special causes.

Common cause variation: It is the variation that is always present in the process. And this type of variation is consistent and predictable.

Special cause of variation: Something is different. Something happened that was not supposed to happen. It is not part of the normal process. Special causes are not predictable and are sporadic in nature.

CONTROL CHART REVIEW:

The only effective way to separate common causes from special causes of variation is through the use of control charts.?A control chart monitors a process variable over time. The average is calculated after you have sufficient data.?The control limits are calculated – an upper control limit (UCL) and a lower control limit (LCL).?The UCL is the largest value you would expect from a process with just common causes of variation present. The LCL is the smallest value you would expect with just common cause of variation present.??As long as the all the points are within the limits and there are no patterns, only common causes of variation are present. The process is said to be "in control."?

Control chart is divided into three equal zones above and below the average.?This is shown in Figure 1.

Figure 1: Control Chart Divided into Zones

Zone C is the zone closest to the average.?It represents the area from the average to one sigma above the average.?There is a corresponding zone C below the average.?Zone B is the zone from one sigma to two sigma above the average.?Again, there is a corresponding Zone B below the average. Zone A is the zone from two sigma to three sigma above the average – as well as below the average.

THE 8 CONTROL CHART RULES

If a process is in statistical control, most of the points will be near the average, some will be closer to the control limits and no points will be beyond the control limits.?The 8 control chart rules listed in Table 1 give you indications that there are special causes of variation present.??Again, these represent patterns.

Table 1: Control Chart Rules

It should be noted that the numbers can be different depending upon the source.?For example, some sources will use 8 consecutive points on one side of the average (Zone C test) instead of the 7 shown in the table above.?But they are all very similar.?Figures 2 through 5 illustrate the patterns.?Figure 2 shows the patterns for Rules 1 to 4.

Figure 2: Zone Tests (Rules 1 to 4)

Rules 1 (points beyond the control limits) and 2 (zone A test) represent sudden, large shifts from the average.?These are often fleeting – a one-time occurrence of a special cause.???

Rules 3 (zone B) and 4 (Zone C) represent smaller shifts that are maintained over time.?A change in raw material could cause these smaller shifts.?The key is that the shifts are maintained over time – at least over a longer time frame than Rules 1 and 2.?

Figure 3 shows Rules 5 and 6.?Rule 5 (trending up or trending down) represents a process that is trending in one direction.?For example, tool wearing could cause this type of trend.?Rule 6 (mixture) occurs when you have more than one process present and are sampling each process by itself, hence the mixture term.??For example, you might be taking data from four different shifts.?Shifts 1 and 2 operate at a different average than shifts 3 and 4.?The control chart could have shifts 1 and 2 in zone B or beyond above the average and shifts 3 and 4 in zone B below the average – with nothing in zone C.

Figure 3: Rules 5 and 6

Figure 4 shows rules 7 and 8.??

Rule 7 (stratification) also occurs when you have multiple processes but you are including all the processes in a subgroup.?This can lead to the data “hugging” the average – all the points in zone C with no points beyond zone C.?

Rule 8 (over-control) is often due to over adjustment.?This is often called “tampering” with the process.?Adjusting a process that is in statistical control actually increases the process variation.???For example, an operator is trying to hit a certain value.?If the result is above that value, the operator makes an adjustment to lower the value.?If the result is below that value, the operator makes an adjust to raise the value.?This results in a saw-tooth pattern.

Figure 4: Rules 7 and 8

These rules represent different situations – patterns = on a control chart.?It should be noted that not all rules apply to all types of control charts.?Table 2 summaries the rules by the type of pattern.

Table 2: Rules by Type of Pattern

POSSIBLE CAUSES BY PATTERN

It is difficult to list possible causes for each pattern because special causes (just like common causes) are very dependent on the type of process.?Manufacturing processes have different issues.?Different types of control chart look at different sources of variation.?Still, it is helpful to show some possible causes by pattern description.?Table 3 attempts to do this based on the type of pattern.

Table 3: Possible Causes by Pattern

Table 3 provides some guidance on what should be the reasons for special causes.?For example, if Rule 1 or Rule 2 is violated, you should be asking “what in this process could cause a large shift from the average?”?Or if Rule 6 occurs, you should be asking “what in this process could cause there to be more than one process present?”?These types of questions can help guide brainstorming sessions to find the reasons for the special cause of variation.??The type of pattern can guide your analysis of the out of control point.