Run charts (often known as line graphs outside the quality management field) display process performance over time. Upward and downward trends, cycles, and large aberrations may be spotted and investigated further. In a run chart, events, shown on the y axis, are graced against a time period on the x axis. For example, a run chart in a hospital may plot the number of patient transfer delays against the time of day or day of the week.
The results might show that there are more delays at noon than at 3 pm Investigating this phenomenon could unearth potential improvement needs. Run charts can also be used to track improvements that have been put into place, checking to determine their success. Also, an average line can be added to a run chart to clarify movement of the data away from the average.
Every process varies. If you write your name ten times, your signatures will all be similar, but no two signatures will be exactly alike. There is an inherent variation, but it varies between predictable limits. If, as you are signing your name, someone bumps your elbow, you get an unusual variation due to what is called a "special cause". If you are cutting diamonds, and someone bumps your elbow, the special cause can be expensive. For many processes, it is important to notice special causes of variation as soon as they occur.
There's also "common cause" variation. Consider a baseball pitcher. If he has good control, most of his pitches are going to be where he wants them. There will be some variation, but not too much. If he is "wild", his pitches are not going where he wants them; there's more variation. There may not be any special causes – no wind, no change in the ball – just more "common cause" variation. The result: more walks are issued, and there are unintended pitches over the plate where batters can hit them. In baseball, control wins ballgames. Likewise, in most processes, reducing common cause variation saves money.
Happily, there are easy-to-use charts which make it easy to see both special and common cause variation in a process. They are called control charts, or sometimes shewhart charts, after their inventor, Walter Shewhart, of Bell Labs. There are many different subspecies of control charts which can be applied to the different types of process data which are typically available.
All control charts have three basic components:
– A centerline, usually the mathematical average of all the samples plotted.
– Upper and lower statistical control limits that define the constants of common cause variations.
– Performance data plotted over time.