Workplaces in production contexts are subject to various changes due to automation and robotics. Apart from the expected positive effects on the safety and health of workers, there is also evidence that prolonged supervisory control can lead to increased cognitive workload (e.g., sustained attention and error detection) as well as biomechanical stress (e.g., unfavorable body postures). We present methods for an objective measurement of cognitive/emotional and biomechanical load, such as eye or motion tracking as well as subjective measures. We discuss that for an integrative and comprehensive examination of workload, methods are needed that represent levels individually as well as the interaction of different measures. Exemplary results from laboratory studies indicate that individual prioritization processes lead to a negative impact either on posture and movement or on cognitive performance. We argue that this methodology is suitable for the implementation in the production context as the learnings can be used for the ergonomic improvement of workplaces, as well as for process optimization, for example in production planning. There is also a great need at the organizational level to generate objective data for measuring the social impact for social life cycle assessments. This could be another application for the suggested methodological approach.