From a philosophical standpoint, it is obvious that statistical process control is a powerful method for eliminating inefficiencies and variances in a system. But for those who actually practice statistical process control, it’s not about the philosophy so much as the nitty-gritty of setting, gathering, reading, and interpreting data. Much of this has been automated in recent years thanks to developing technologies, but actual humans are still deeply involved in every step of statistical process control. And when humans are involved in interpreting often complex data, it helps to have a few tools to make the information clearer.

Here are a few types of tools that are commonly used by professionals in the field of statistical process control:

Flow charts: A flow chart maps a process in all its complex parts from beginning to end. While they are not unique to statistical process control and in fact have little to do with the statistics themselves, flow charts are useful for giving quality control managers an overall picture of the process, which makes it easier to evaluate data discrepancies pointing to potential variances. They’re also useful for giving a clear picture of where portions of the process fall within an overall timeline and in relation to other portions.

Run graphs: A run graph is simply a graph that displays data in terms of time. While run graphs can be useful for looking at a single point of data, in statistical process analysis they are often used to get a quick snapshot of the relationships between different data points. For example, if two data points almost always rise and fall at the same time, then this points to a likely relationship, whether direct or indirect, between what is measured by the two statistics. Run graphs are most useful for two variables and can become quite chaotic when more variables are introduced, but using them to interpret the relationships between three or more data points is not unheard of.

Designed experiments: A designed experiment is exactly what it sounds like. When a process begins creating results that are out of line with expectations, quality control managers may undergo a set of experiments to pinpoint the source of the flaws. This may involve segmenting the process, taking some elements out, or adding elements in an attempt to attenuate the flaws for informational purposes.

Pareto charts: Pareto charts are based on the Pareto Principle, which states that that not all causes of a phenomenon have the same impact. So, for example, if a product is flawed, there may be four different causes of the flaw, but it may be that one of the causes is the biggest culprit while the others would not be significant without the primary cause. Pareto charts try to make sense of this, mapping the relative impact of different factors in a system.

Control graphs: Control graphs are essentially maps of the frequency of variations over time. If a process is statistically stable, a control graph shows nothing. It’s only when instability occurs and variances start creeping in that a control graph becomes useful. These graphs are helpful for locating patterns of variance and identifying whether variances result from mere chance or are results of a flaw in the process.