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For this test, look for groups of points moving up or down in succession. Count consecutive points, including horizontal runs within the run. This pattern is probably the result of a trend in one of the system resources. The chart below shows a group of seven points moving downward.
See also: >> Analyze for special causes of variation >> Any point lying outside the control limits >> 7 or more points in a row above or below the centerline >> Any nonrandom pattern
To apply this test, look for groups of points above or below the average or centerline. Count consecutive points. Are there groups of seven or more? This is probably the result of a shift in one of the system resources (materials, people, methods, environment, information aids, equipment, and measurement). The following chart, which can be created using SQCpack, shows two groups, one with eight above the centerline and one with seven below.
See also: >> Analyze for special causes of variation >> Any point lying outside the control limits >> 7 or more points in one direction >> Any nonrandom pattern
This is the quickest and easiest test for system stability. Look above the upper control limit and below the lower control limit to see whether any points fall in those regions of the chart. If you are looking at a chart pair (X-bar and R, X-bar and s, or X and MR), look at both charts.
Points falling outside the control limits may be the result of a special cause that was corrected quickly, either intentionally or unintentionally. It may also point to an intermittent problem. The chart below shows two points outside the control limits.
See also: >> Analyze for special causes of variation >> Any point lying outside the control limits >> 7 or more points in a row above or below the center line >> 7 or more points in one direction >> Any nonrandom pattern
The key to chart interpretation is to initially ascertain the type of variation in the system—that is, whether the variation is coming from special or common causes. When the system has only common causes of variation, it is referred to as stable or in control. If, however, the system has special causes of variation, it is referred to as unstable, or out of control.
Look any of the conditions listed below, which indicate that the process is statistically unstable:
When you have determined whether or not there is special cause variation, declare the system stable or unstable.
The t-chart creates a picture of a process over time. Each point on the chart represents an amount of time that has passed since a prior occurrence of a rare event. The time unit might be hours, days, weeks, months, etc. For example, a chart might plot the number of days between infection outbreaks at a hospital. A traditional plot of this data may contain many points at zero and an occasional point at one. A t-chart avoids two types of mistakes: over control and under control that a traditional control chart might generate.
The t-chart helps to identify the two types of variation present in a system, special and common cause, so that appropriate improvement actions can be taken. Specific formulas for t-chart control limits are used with this type of data. T-charts can be created using software programs like SQCpack.
By way of contrast, consider the information that is provided with a chart of lost production due to work time accidents. The chart shows many months where there were no occurrences of lost production. Thus, this chart is not beneficial and provides little insight to know if the process changes resulted in improvement.
Now consider the same data using a t-chart. Instead of plotting the lost production each month, plot the days passed since the last time of lost production. The data is still being collected and charted, but the method used to analyze the information is different. This shows that an improvement is occurring (or has occurred), with the days between lost production increasing.
A g-chart is a chart for attributes data. It is used to count the number of events between rarely-occurring errors or nonconforming incidents.
The g-chart creates a picture of a process over time. Each point represents the number of units between occurrences of a relatively rare event. For example, in a production setting, where cars are produced daily, an unexpected line shutdown may occur. A g-chart might be used to look at the number of units (i.e. cars) produced between line shutdowns. The units produced can be almost anything. For example, you might look at the number of invoices printed, the number of customers served, or the number of patients seen, between occurrences of some event. A traditional plot of data such as this is not conducive to control chart interpretation. The g-chart helps to visualize this data in traditional control chart form. Specific formulas for g-chart control limits are used with this type of data.
The “g” in g-chart stands for geometric, since data relating to events between occurrences represents a geometric distribution. G-charts can be created using software programs like SQCpack.
A rare event chart is used when a traditional control chart is not effective. Consider a rare event chart when one or more of these conditions exist:
Here are some processes in various industries that lend themselves to rare event application.
A rare event chart is a control chart that provides information about the process being studied where the data comes from rarely-occurring incidents. It provides insight into a process when tracking quality measures that occur infrequently enough that tracking them on a traditional control chart tends to be ineffective. Rare event charts were developed in response to the limitations of attributes control charts and individuals charts in rare event scenarios.
In a typical count or attributes control chart, there is a numerator (what is counted) and a denominator (the sample size or items in the pool to count). If many of the periods being counted are zero because no incident has occurred, a chart with many flat lines ensues, where everything is plotted along the x-axis. When an incident occurs, the count of one for this period is plotted, then the counts drop back to zero for the next few periods. This cycle continues, and the resulting control chart looks odd, the control limits are not effective, and the chart fails to provide proof of quality performance.
Two types of rare event charts exist. A t-chart measures the time elapsed since the last event, and a g-chart counts the number of possible opportunities since the last event.