Abstract
Control charts are a representative method of statistical process control. Control charts make it possible to effectively manage the manufacturing process but it assumes the availability of large historical data sets. In the high-mix, low-volume production environment that has become a mainstream in recent years, sufficient samples for estimating process parameters cannot be obtained often. In such a situation, the control chart does not function properly. In addition, in manufacturing processes such as cutting, process averages can change due to deterioration even if the process operates in the in-control state, leading to type I error increases. Therefore, herein, we use hierarchical Bayesian modeling to propose control charts that function appropriately for trendy data sets in high-mix, low-volume production. We then show the usefulness of such an approach by comparing with a conventional method. Since hierarchical Bayesian modeling makes it possible to assume the same distribution for parameters of various types, it is possible to use all the information to estimate parameters comprehensively. This capability makes the new approach effective for high-mix, low-volume production.
