Data‐driven framework for warranty claims forecasting with an application for automotive components

Abstract

Abstract Automakers spend billions of dollars annually towards warranty costs, and warranty reduction is typically high on their priorities. An accurate understanding of warranty performance plays a critical role in controlling and steering the business, and it is of crucial importance to fully understand the actual situation as well as be able to predict future performance, for example, to set up adequate financial reserves or to prioritize improvement actions based on expected forthcoming claims. Data maturation, a major nuisance causing changes in performance metrics with observation time, is one of the factors complicating warranty data analysis and typically leads to over‐optimistic conclusions. In this paper, we propose a sequence of steps, decomposing and addressing the main reasons causing data maturation. We first compensate for reporting delay effects using a Cox regression model. For the compensation of heterogeneous build quality, sales delay, and warranty expiration rushes, a constrained quadratic optimization approach is presented, and finally, a sales pattern forecast is provided to properly weigh adjusted individual warranty key performance indicators. The results are shown to dramatically improve prior modeling approaches.

Keywords

• dissimilarity
• forecasting
• machine learning
• predictive analytics
• proportional hazards
• reporting delay