  Process mining has matured as analysis instrument for process-oriented data
in recent years. Manufacturing is a challenging domain that craves for
process-oriented technologies to address digitalization challenges. We found
that process mining creates high expectations, but its implementation and usage
by manufacturing experts such as process supervisors and shopfloor workers
remain unclear to a certain extent. Reason (1) is that even though
manufacturing allows for well-structured processes, the actual workflow is
rarely captured in a process model. Even if a model is available, a software
for orchestrating and logging the execution is often missing. Reason (2) refers
to the work reality in manufacturing: a process instance is started by a
shopfloor worker who then turns to work on other things. Hence continuous
monitoring of the process instances does not happen, i.e., process monitoring
is merely a secondary task, and the shopfloor worker can only react to
problems/errors that have already occurred. (1) and (2) motivate the goals of
this study that is driven by Technical Action Research (TAR). Based on the
experimental artifact TIDATE -- a lightweight process execution and mining
framework -- it is studied how the correct execution of process instances can
be ensured and how a data set suitable for process mining can be generated at
run time in a real-world setting. Secondly, it is investigated whether and how
process mining supports domain experts during process monitoring as a secondary
task. The findings emphasize the importance of online conformance checking in
manufacturing and show how appropriate data sets can be identified and
generated.
