Multivariate outlier modeling for capturing customer returns — How simple it can be

Univariate outlier analysis has become a popular approach for improving quality. When a customer return occurs, multivariate outlier analysis extends the univariate analysis to develop a test model for preventing similar returns from happening. In this context, this work investigates the following question: How simple multivariate outlier modeling can be? The interest for answering this question are twofold: (1) to facilitate implementation of a test model in test application and (2) to ensure robustness of the methodology. In this work, we explain that based on a Gaussian assumption, a simpler covariance-based outlier analysis approach can be sufficient over a more complex density-based approach such as one-class SVM. We show that correlation among tests can be a good metric to rank potential outlier models. Based on these observations a simple outlier analysis methodology is developed and applied to effectively analyze customer returns from two automotive product lines.