Verifying a dominant cause of output variation

Finding the dominant cause(s) of variation in process improvement projects is an important task. Before trying to reduce variation in the dominant cause or mitigate the effect of variation in the dominant cause to reduce output variation, it is strongly recommended that we verify we have identified the true (dominant) cause. This article is about how best to verify we have correctly identified a dominant cause, as the existing literature does not properly answer this question. Although it may seem that a randomized controlled experiment is sufficient for this purpose, we show that experimental studies alone cannot provide all the required information. An experiment identifies whether a suspect is a <i>cause</i> of variation; however, we also require additional information (i.e., from observational studies) to determine whether it is <i>dominant</i> and not just significant. This article lists some viable composite study designs, assesses their relative merits, and recommends proper sample sizes. We also investigate how to systematically conduct a verification study in the era of smart manufacturing. Moreover, we provide a tangible example to illustrate our proposed procedure.