Keywords:
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.

Keywords:
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

Keywords:

Keywords:

Keywords: transfer montioring, knowledge measurement, framework, innovation course
Astract: The measurement of the knowledge change of employees as well as the transfer is discussed in this thesis. Although these two terms are often used synonymously, there is a difference between them. Learning is adapting to a situation whereas transfer is applying the knowledge to similar situations. There are many approaches to measuring learning success or transfer, most of which originate in educational science. In this thesis we consider the special case of innovation courses, where there are further requirements that must be met. Unfortunately, the existing frameworks are not designed for these requirements and are therefore not sufficient. An innovation course is a long-term course in which employees of companies are taught in a certain topic. Such an innovation course consists of several modules for which both the measurement of learning success and knowledge transfer for the participants must take place. To achieve this and to make the measurements repeatable and objective, we have developed a framework. We use the Design Science Approach to develop the framework. However, the goal is not to create a static artefact that can only be applied to the course of our case study, but to design a framework that is also easily adaptable and applicable in other innovation courses or in a similar environment. To test and improve the framework, we use it in four modules of the DigiTrans 4.0 innovation course. For three of the four modules of our case study, the difference between the knowledge before the module and at the end is statistically significant. We also create linear models to explain or predict the transfer. The models are created with and without heteroscedasticity adjustment. The results of the models are slightly different, but show a common trend, which originates from the same background formula. Since these characteristics are known in the literature of knowledge transfer, the framework created is well suited for measuring the transfer.