The research project is based on the research hypothesis that simulation-based characteristic surfaces with uncertainty bands can provide comparable information to extensive simulation experiments with a smaller number of simulation runs. Therefore, a methodology is developed with the objective of empirically investigating complex systemic interactions between different logistics parameters (such as load and performance parameters) under consideration of statistical uncertainty to determine optimal operating points. For this purpose, statistical experimental design, meta-modeling, and discrete event simulation are combined. The object of interest of the systemic investigations is order picking systems.

The novel methodology is based on a classification of order picking systems with the help of representative reference models, a taxonomy of statistical models for logistics performance indicators and their allocation to the reference models as well as the corresponding implementation in simulation models. In addition to the development of the methodology in the sense of a planning assistance, empirical system-theoretical insights into representative order picking systems are gained.