Detecting early indicator cars in an automotive database: a multi-strategy approach

Abstract

No company so far achieved the ultimate goal of zero faults in manufacturing. Even high-quality products occasionally show problems that must be handled as warranty cases. In this paper, we report work done during the development of an early warning system for a large quality information database in the automotive industry. We present a multi-strategy approach to flexible prediction of upcoming quality problems. We used existing techniques and combined them in a novel way to solve a concrete application problem. The basic idea is to identify sub populations that, at an early point in time, behave like the whole population at a later time. Such sub populations act as early indicators for future developments. We present our method in the context of a concrete application and present experimental results. At the end of the paper, we outline how this method can be generalised and transferred to other KDD application problems.