Abstract: Time series analysis often requires data that represents
the evolution of an observed variable in equidistant time steps. In
order to collect this data sampling is applied. While continuous
signals may be sampled, analyzed and reconstructed applying
Shannon-s sampling theorem, time-discrete signals have to be dealt
with differently. In this article we consider the discrete-event
simulation (DES) of job-shop-systems and study the effects of
different sampling rates on data quality regarding completeness and
accuracy of reconstructed inventory evolutions. At this we discuss
deterministic as well as non-deterministic behavior of system
variables. Error curves are deployed to illustrate and discuss the
sampling rate-s impact and to derive recommendations for its wellfounded
choice.