There are two types of variation in production and service processes – common cause variation and assignable cause variation. The common cause variation is the “usual” variation that is caused by statistical anomalies in the production or service processes. The assignable cause variation is variation caused by a specific change in the underlying process structure.
There is a simple way to distinguish between common cause variation and assignable cause variation: Common cause variation falls within the previously mentioned control limits (upper control limit, lower control limit). Assignable cause variation falls outside those control limits. The control limits can be set as three standard deviations in each direction from the mean. A field with a width of six standard deviations will contain 99,7% of all cases. If a sample lies notably outside of these limits, assignable cause variation is likely to be the reason.
The purpose of so-called statistical process control is to constantly monitor the process output in order to be alerted to the occurrence of assignable cause variation almost immediately. In the famous Toyota production system, this is realized through a detect – stop – alert framework which catches defects quite quickly. This is critical, because defects tend to (a) produce more defects over time and (b) cause higher monetary losses once the defective flow units get through to the process bottleneck. Both problems provide huge incentives for figuring out how to detect defects as soon as possible. Some techniques that can be effectively used here are the drawing of fishbone diagrams as well as laddering. The idea behind both techniques is to basically ask “why-questions” over and over again until the actual root cause(s) of defects is (are) identified.
These lecture notes were taken during 2013 installment of the MOOC “An Introduction to Operations Management” taught by Prof. Dr. Christian Terwiesch of the Wharton Business School of the University of Pennsylvania at Coursera.org.