Abstract
This paper presents a statistical methodology to construct a model for early detection of reliability problems using information from warranty databases and upstream supply chain. This is contrary to extant methods that are mostly reactive and only rely on data available from the OEMs (original equipment manufacturers). The paper proposes hazard rate models to link upstream supply chain quality/testing information as explanatory covariates for early detection of reliability problems. In doing so, it improves both the accuracy of 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 Tier-1 automotive supplier.
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