Für viele Unternehmen sind Produktivität und Terminabweichung sehr wichtige Zielgrößen. Sowohl in der Theorie als auch in der Praxis werden sie in der Regel getrennt voneinander betrachtet, obwohl sie eng miteinander verbunden sind. Eine sinkende Produktivität führt häufig zu einem steigenden Rückstand und folglich zu einer steigenden Terminabweichung. Der Beitrag beschreibt, wie die Zusammenhänge zwischen der Produktivität und der Terminabweichung mithilfe von Kennlinien modelliert werden können.   Many manufacturing companies consider productivity and lateness as very import performance indicators. In theory as well as in the industrial practice both terms usually are considered isolated. Nevertheless they are linked to each other. A lower than expected productivity often results in an increasing backlog and thus a higher lateness. This paper proposes a way to model the relationship between productivity and lateness with the help of operating curves.

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Recent developments in robotics allow the design of work systems with enhanced human robot collaboration (HRC) for assembly tasks. Productivity improvements are a common aim for companies that look into the implementation of HRC. To harvest the full productivity potential of these work systems, an analysis of the HRC work processes is essential. However, a dedicated method for the analysis of productivity in HRC is missing. This paper presents an approach using 3D-cameras to observe the employee in HRC. The approach links this information to robot states. The resulting analysis aims at improving the productivity of the work system e.g. by identifying and reducing balancing losses in HRC. The method tracks the movements of the employees in the HRC area and matches the corresponding robot activities.