Application of graphical models in the automotive industry

Abstract

The production pipeline of present day's automobile manufacturers consists of a highly heterogeneous and intricate assembly workflow that is driven by a considerable degree of interdependencies between the participating instances as there are suppliers, manufacturing engineers, marketing analysts and development researchers. Therefore, it is of paramount importance to enable all production experts to quickly respond to potential on-time delivery failures, ordering peaks or other disturbances that may interfere with the ideal assembly process. Moreover, the fast moving evolvement of new vehicle models require well-designed investigations regarding the collection and analysis of vehicle maintenance data. It is crucial to track down complicated interactions between car components or external failure causes in the shortest time possible to meet customer-requested quality claims. To summarize these requirements, let us turn to an example which reveals some of the dependencies mentioned in this chapter. As we will see later, a normal car model can be described by hundreds of variables each of which representing a feature or technical property. Since only a small number of combinations (compared to all possible ones) will represent a valid car configuration, we will present a means of reducing the model space by imposing restrictions. These restrictions enter the mathematical treatment in the form of dependencies since a restriction may cancel out some options, thus rendering two attributes (more) dependent. This early step produces qualitative dependencies like "engine type and transmission type are dependent." To quantify these dependencies some uncertainty calculus is necessary to establish the dependence strengths. In our cases probability theory is used to augment the model, e.g., "whenever engine type 1 is ordered, the probability is 56% of having transmission type 2 ordered as well." There is a multitude of sources to estimate or extract this information from. When ordering peaks occur like an increased demand of convertibles during the Spring, or some supply shortages arise due to a strike in the transport industry, the model is used to predict vehicle configurations that may run into delivery delays in order to forestall such a scenario by, e.g., acquiring alternative supply chains or temporarily shifting production load. Another part of the model may contain similar information for the aftercare, e.g., "whenever a warranty claim contained battery type 3, there is a 30% chance of having radio type 1 in the car." In this case dependencies are contained in the quality assessment data and are not known beforehand but are extracted to reveal possible hidden design flaws. © 2008 Springer-Verlag Berlin Heidelberg.