Cyber-physical systems promise a complete networking of all actors and resources involved in production and thus an improved availability of information. In this context decision support systems enable appropriate processing and presentation of the captured data. In particular, production scheduling could benefit from this, since it is responsible for the short-term planning and control of released orders. Since decision support systems and cyber-physical systems together are not yet widely used in production scheduling, the aim of this research study is to analyse the adoption of these technologies. In order to do so, we conducted a qualitative interview study with experts on production scheduling. Thereby, we identified eleven influencing factors and 22 related challenges, which affect the adoption of decision support systems in production scheduling in the context of cyber-physical systems. We further discuss and assess the identified influencing factors based on the interview study. The results help to explain and improve the adoption of those systems and can serve as a starting point for their development.

The use of cyber-physical systems in production promises great potential for production scheduling since a larger information base is available for the scheduling of production orders. However, the mere acquisition of realtime data does not inherently lead to improvements. On the contrary, a targeted preparation of the data is required in order to prevent an information overload. Decision support systems that support decision makers in production scheduling can perform this task. However, the design of such systems in combination with cyber-physical systems has hardly been investigated so far. In this paper, we therefore design and implement a corresponding decision support system in a design science approach. For this, we identify meta-requirements based on a literature analysis and an interview study. Finally, we evaluate the created meta-artifact in a laboratory setting in order to obtain generalizable knowledge about building such a decision support system.

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