Abstract

In discussions concerning statistical process control (SPC) and industrial statistics, the two are often treated as separate disciplines, related only through the commonality of statistical principles. It is important to understand that in practice, a much more interdependent relationship should exist. The inherent tendency of processes to lack statistical control has important implications for statistical studies, particularly where establishing the link between SPC and industrial statistics is concerned. A model is presented for understanding the role of statistical process control (SPC) as it relates to industrial statistics. To conduct an analytical study on a process lacking statistical control is risky because the cause system is predictable only after it has been reduced to common causes. Control charts and SPC can be used to obtain statistical control by eliminating the assignable causes and reducing the process to unassignable or common causes. The use of control charts to maintain that state by dealing with assignable causes in the future is known as the operation of maintaining control. Assignable causes are present in processes that have never been subject to the operation of obtaining control due to inputs to that process. It is within these inputs that the operation of SPC plays a pivotal role. With professionals in all disciplines applying statistical techniques to make decisions about important processes, it is vitally important that this concept be understood.