Application of Bayesian partition models in warranty data analysis

Abstract

Automotive companies are forced to continuously extend and improve their product line-up. However, increasing diversity, higher design complexity, and shorter development cycles can produce new and unforeseen quality issues. Warranty data analysis helps quality engineers in their task of identifying the root cause of manufacturing or design related problems and in planning and implementing remedial actions. In this paper we show how Bayesian partition models can be used to support root cause investigations by applying Bayesian model comparison. We review product partition models, exemplify how partitions can be ranked, and illustrate their expressive power compared to Bayesian networks. Based on this, we outline a data analysis approach that considers dependencies, in particular taxonomic and partonomic relationships, among influencing variables and identifies the most likely semantically meaningful partitions that are close to the concept that actually caused a quality issue. The approach can be integrated seamlessly with interactive decision trees which have been successfully applied in our domain. An evaluation on test data and real-world case studies illustrate how the approach can be used by engineers to investigate cause-effect relationships and show that its application is not limited to the automotive domain.