Why consider the humanintheloop in automated cyberphysical production systems? Two cases from crosscompany cooperation Industry 4.0 and the Internet of Production can increase efficiency and effectiveness of workflows in manufacturing companies and production networks. Despite ubiquitous automation, people are essential in sociotechnical cyberphysical production systems due to unique cognitive capabilities, as final arbitrators, or for ethical and legal reasons. However, the design of interfaces between the humanintheloop and production systems poses challenges not yet been sufficiently elaborated in research and practice. We present two behavioural studies in the context of intercompany collaboration that show why considering the humanintheloop is crucial: The first study shows that information complexity and individual differences shape the overall decision quality. With increasing information complexity, the decision speed decreases and the decision accuracy descends. Consequently, a fine balance between necessary, abundant, and superfluous information must be found. The second experiment studies human decision making in complex environments using a business simulation. We found that correct decision aids can augment the humanintheloopxe2x80x99s decision making and that these can increase usability, trust, and proft. Yet, incorrect decision support has the opposite effect. Guidelines for designing sociotechnical cyberphysical production systems and a research agenda conclude this article.

A Decision Support System for rapid rampup of industry 4.0 enabled production systems Abstract   Production rampup is a key phase during the introduction or changeover of a production system. Process calibration and tuning are inevitably required to make such a system fully operational and let it reach its maximum production yield. A complex decisionmaking process takes place in order to optimally tune the system and requires a long time for testing and experimenting that will determine the system behaviour. This work considers the sequential nature of rampup and proposes a CyberPhysical Systems approach based on data capturing, learning mechanisms and knowledge extraction, leading to an Industry 4.0 compliant Decision Support System (DSS) for human operators. The proposed system is implemented as an online DSS and also supports offline learning using previously gathered knowledge. A number of experiments have been carried out on a micro scale assembly station, validating the expected benefits of the proposed DSS. Results show a reduction of over 40% in the number of rampup steps required when using the DSS.