Improved design of kernel distance–based charts using support vector methods

Statistical Process Control (SPC) techniques that originated in manufacturing have also been used to monitoring the quality of various service processes, which can be characterized by one or several variables. In the literature, these variables are usually assumed to be either continuous or categorical. However, in reality, the quality characteristics of a service process may include both continuous and categorical variables (i.e., mixed-type variables). Direct application of conventional SPC techniques to monitor such mixed-type variables may cause increased false alarm rates and misleading conclusions. One promising solution is the kernel distance–based chart (K-chart), which makes use of Support Vector Machine (SVM) methods and requires no assumption on the variable distribution. This article provides an improved design of the SVM-based K-chart. A systematic approach to parameter selection for the considered charts is provided. An illustration and comparison are presented based on a real example from a logistics firm. The results confirm the improved performance obtained by using the proposed design scheme.