Graph-based Predictions and Recommendations in Flexible Manufacturing Systems

Due to the emerging paradigm of masscustomization, manufacturing processes are becoming increasingly complex. Management of this complexity requires system support that goes beyond traditional MES capabilities, such as discovery of patterns throughout massive networks of interdependent processes. As of today, Manufacturing Analytics offer only limited decision support focused on descriptive metrics that cannot account for predictive and prescriptive decision support, such as detection of systematic fault patterns. The application of predictive models in manufacturing environments is non-trivial, because they need to reflect system domain constraints and preserve semantics of manufacturing operations. Recent approaches of so-called Advanced Manufacturing Analytics try to fill this gap by applying standard data mining algorithms with customized data preparation for domain-specific use cases. In order to overcome the problem of high customization efforts, we introduce a graph-based analytics framework derived from a comprehensive requirements analysis. Additionally, we demonstrate applicability of the presented framework on two exemplary manufacturing analytics use cases.