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Astract: Variability is crucial in the design of many advanced goods and it is also receiving increasing attention in production systems engineering. Since OPC Unified Architecture plays an important role when it comes to standardized information exchange in modern production systems, it can be a melting pot for information from various engineering domains, such as product design and production engineering - thus, it is an ideal place to hold variability information of products and production systems alike. Based on an initial variability information model we propose additional concepts for the persisting of configurations.

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Astract: Model-driven engineering (MDE) provides tools and methods for the manipulation of formal models. In this letter, we leverage MDE for the transformation of production system models into flat files that are understood by general purpose planning tools and that enable the computation of "plans", i.e., sequences of production steps that are required to reach certain production goals. These plans are then merged back into the production system model, thus enriching the formalized production system knowledge.

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Astract: Smart production systems need to be able to adapt to changing environments and market needs. They have to reflect changes in (i) the reconfiguration of the production systems themselves, (ii) the processes they perform or (iii) the products they produce. Manual intervention for system adaptation is costly and potentially error-prone. In this article, we propose a model-driven approach for the automatic generation and regeneration of production plans that can be triggered anytime a change in any of the three aforementioned parameters occurs.

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Astract: AutomationML provides a versatile modeling environment for the description of production systems. However, when starting a new AutomationML project, or when serializing existing data with the AutomationML format, there are no rules on how to structure these models in a meaningful way. In this work, we present an approach for structuring AutomationML models, based on the IEC 62264 standard. In our approach we are implementing the process of serializing IEC 62264 information declaratively, by leveraging the power of model transformations, as they are applied in the context of model-driven (software) engineering.

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Astract: Current trends in manufacturing lead to more intelligent products, produced in global supply chains in shorter cycles, taking more and complex requirements into account. To manage this increasing complexity, cognitive decision support systems, building on data analytic approaches and focusing on the product life cycle, stages seem a promising approach. With two high-tech companies (world market leader in their domains) from Austria, we are approaching this challenge and jointly develop cognitive decision support systems for three real world industrial use cases. Within this position paper, we introduce our understanding of cognitive decision support and we introduce three industrial use cases, focusing on the requirements for cognitive decision support. Finally, we describe our preliminary solution approach for each use case and our next steps