Modeling and analysis of automobile warranty data in presence of bias due to customer-rush near warranty expiration limit

Automobile users experiencing soft failures, often delay reporting of warranty claims till the coverage is about to expire. This results into a customer-rush near the warranty expiration limit leading to an occurrence of ‘spikes’ in warranty claims towards the end of warranty period and thereby introducing a bias into the dataset. At the same time, an occurrence of manufacturing/assembly defects in addition to the usage related failures, lead to ‘spikes’ in warranty claims near the beginning of the warranty period. When such data are used to capture the field failures for obtaining feedback on product quality/reliability, it may lead product or reliability engineers to potentially obtain a distorted picture of the reality. Although in reliability studies from automobile warranty data, several authors have addressed the well-recognized issues of incomplete and unclean nature of warranty data, the issue of ‘spikes’ has not received much attention. In this article, we address the issue of ‘spikes’ in the presence of the incomplete and unclean nature of warranty data and provide a methodology to arrive at component-level empirical hazard plots from such automobile warranty data.