by Oliver Yu
A control chart is a graphical tool that helps you visualize process performance. Specifically, control charts help you visualize the expected variability of a process and unambiguously tells you what is normal (a.k.a. "common cause variability") and what is abnormal (a.k.a. "special cause variability").
Discerning common-cause from special-cause variability is crucial because responding to low results that are within expectation often induces more variability.
So up to this point, we know that low process variability allows us to detect changes to the process sooner. We also know that low process variability enables processes with higher capability.
Below is the control chart of the buffer osmo data from a previous blog post on reducing process variability.
The -green- horizontal line is the average of the population and the -red- lines are the control limits (upper control limit and lower control limit). Points that are within the UCL and LCL are expected (a.k.a. "common"). Points outside of the limits are unexpected (a.k.a. "special"). From the control chart, you can immediately see that the latest value of 301 mOsm/kg is "normal" or "common", and that no response is necessary.
Below, you see the control chart for the second set of data and how a reading of 297 mOsm/kg after 8 consecutive readings of 295 mOms/kg is anomalous and certainly worth an extra look.
There are all kinds of control charts and they have a rich history - worth reading if you're into that kind of thing. In batch/biologics processes, each data point corresponds with exactly one batch and so the type of control chart used is the IR chart.
It is important to know that the control limits are not computed from standard deviations - they are computed from the moving range... without going full nerd, the reason behind this is that control limits are sensitive to the order in which the points were observed and narrow when there is a trending pattern in the data.
Control charts for key process performance indicators are a must for any organization serious about reducing process variability. Firstly, control charts quantify variability. Secondly, control charts are easy to undertand. Lastly - and most importantly, control charts help marshall scarce resources by identifying common vs. special cause.