Necessity of using Dynamic Bayesian Networks for feedback analysis into product development

Transformations into the modern business world is sustained by enhancement and improvement of strategies, systems and techniques towards evaluating and applying customer knowledge for the integration of Product Use Information (PUI) into product development, and meeting customer and market demands. In this paper the processing and modelling of PUI of many instances of one product type which is captured during the product use phase, e.g. condition monitoring data, failures or incidences of maintenance, raised by different graphical methods on the basis of a praxis and application scenario. Product Lifecycle Management (PLM) ensures a uniform data basis for supporting numerous engineering and economic organizational processes along the entire product life cycle-from the first product idea to disposal or recycling of the product. The processing and modelling of PUI raised by graphical methods like Bayesian Networks (BNs) or Dynamic Bayesian Networks (DBNs). In accordance, the product use knowledge leads back of the product development phase. This is used for discovering room for product improvements for the next product generation. Therefore the PUI of the different instances should be aggregated by applying fusion techniques to deduce/achieve generalized product improvements for a product type which is related to prospective research by focus on quality management systems and defining measures for customer satisfaction. As a result the significant aspect of this paper is to identify which graphical solution brings optimally the best results for the requirements of processing and modeling of PUI.