Bayesian approach to hazard rate models for early detection of warranty and reliability problems using upstream supply chain information

Abstract

Hazard rate models are proposed recently for detection of reliability problems using information from upstream supply chain and warranty databases. Whereas these models improve the accuracy of reliability problem detection, they require relatively long lead-times due to their reliance on just the actual warranty claims data collected from the field. We propose a Bayesian approach to hazard rate models that reduces the need for extensive warranty claim history. The paper introduces Bayesian hazard rate models to account for uncertainties of the explanatory covariates, in particular, information collected during product development, major design change/upgrade efforts, and manufacturing technology upgrades. In doing so, it improves both the accuracy of extant hazard rate models for reliability problem detection as well as the lead-time for detection. The proposed methodology is illustrated and validated using real-world data from a leading global Tier-1 automotive supplier.