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Astract: Nowadays, manufacturers are increasingly able to collect and analyze data from sensors on manufacturing equipment, and also from other types of machinery, such as smart meters, pipelines, delivery trucks, etc. Nevertheless, many manufacturers are not yet ready to use analytics beyond a tool to track historical performance. However, just knowing what happened and why it happened does not use the full potential of the data and is not sufficient anymore. Manufacturers need to know what happens next and what actions to take in order to get optimal results. It is a challenge to develop advanced analytics techniques including machine learning and predictive algorithms to transform data into relevant information for gaining useful insights to take appropriate action. In the proposed research we target new analytic methods and tools that make insights not only more understandable but also actionable by decision makers. The latter requires that the results of data analytics have an immediate effect on the processes of the manufacturer. Thereby, data analytics has the potential to improve industrial processes by reducing maintenance costs, avoiding equipment failures and improving business operations. Accordingly, the overall objective of this project is to develop a set of tools - including algorithms, analytic machinery, planning approaches and visualizations - for industrial process improvements based on data analytics.

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Astract: OPC Unified Architecture (UA) is a powerful technology for modeling and instantiating domain-specific information in a standardized manner. Its initial application scenario is in the domain of automated production systems, that increasingly have to deal with variability information, (i) regarding the products being manufactured and (ii) regarding the production systems themselves. In this work we propose a non-intrusive OPC UA information model for the modeling and querying of variability information using feature models, which are a well-known paradigm in the management of software product lines. Our information model can be applied "aside" existing domain information without interfering with their internal structure.

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Astract: Resource-Event-Agent (REA) has been applied to various engineering and business domains, with a focus on transfer activities rather than transformation activities. In the context of smart manufacturing, vertical integration of IT systems (e.g., business applications and production control systems) is a key factor. In this work, we shed light on the integration of REA concepts into production environments by investigating properties of REA transformations and aligning them with concepts from an international standard for enterprise-control system integration (ISA-95).

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Astract: IT systems' integration in manufacturing companies is currently investigated in both academia and industry. While there can be found specialized systems and standards that tackle specific, e.g., production relevant problems, little has been done in the alignment of and transformation between such industrial standards. We will present the alignment of two specialized international standards, which will foster vertical system integration through detailed mapping of related concepts: (i) the Automation Markup Language (AML) standardizes the modeling of factory shop floors on top of the XML-based Computer Aided Engineering Exchange (CAEX) data format and (ii) ISA-95 is a series of standards targeting the integration of enterprise control systems, most prominent enterprise resource planning systems and manufacturing execution systems. In order to provide higher level semantics to lower level system descriptions, we have (i) aligned elements from AML and ISA-95 in order to make explicit both overlaps and complementary concepts and (ii) defined a ruleset for referencing external ISA-95 documents/elements from AML documents. Finally, we have developed a scenario that shows the potential use case for such an entwined use of AML and ISA-95.