  Consider indoor positioning systems (IPS) in production halls where objects
equipped with sensors send their current position. Beside its large volume, the
analyzation of the resulting raw data is challenging due to the susceptibility
towards noise. Reasons are accuracy issues and undesired awakenings of sensors
that occur due to the dynamics of logistic processes (e.g.~vibrations of
passing forklifts). We propose a tailor-made statistical procedure for these
challenges and combine visual analytics with movement detection. Contrary to
common stay-point algorithms, we do not only distinguish between stops and
moves, but also consider undesired awakenings. This leads to a more detailed
interpretation scheme offering usages for online (e.g.~monitoring of orders)
and offline applications (e.g.~detection of problematic areas). The approach
does not require other information than the raw IPS output and enables an
ad-hoc analysis. We underline our findings in an extensive case study with real
IPS data of our industry partner.
