In recent years we observe dramatic changes in the way in which quality features of manufactured products are designed and inspected. The modeling and monitoring problems obtained by new inspection methods and fast multi-stream high-speed sensors are quite complex. These measurement tools are used in emerging technologies like, e.g., additive manufacturing. It has been shown that in these fields other types of quality characteristics have to be monitored. It is mainly not the mean, the variance, the covariance matrix or a simple profile which reflects the behavior of the quality characteristics but the shape, surfaces and images, etc. This is a new area for SPC. Note that more complicated characteristics arise in other fields of applications as well like, e.g., the monitoring of optimal portfolio weights in finance. Since in the last years many new approaches have been developed in the fields of image analysis, spatial statistics and for spatio-temporal modeling a huge amount of tools are available to model the underlying processes. Thus the main problem lies on the development of monitoring schemes for such structures.

In this talk new procedures for monitoring image processes are introduced. They are based on multivariate exponential smoothing and cumulative sums taking into account the local correlation structure. A comparison is given with existing methods. Within an extensive simulation study the performance of the analyzed methods is discussed.

The presented results are based on a joint work with Yarema Okhrin and Ivan Semeniuk.