  Currently, data collection on the shop floor is based on individual resources
such as machines, robots, and Autonomous Guided Vehicles (AGVs). There is a gap
between this approach and manufacturing orchestration software that supervises
the process of creating single products and controls the ressources'
interactions. This creates the need to save resource-based data streams in
databases, clean it, and then re-contextualize it, i.e., by connecting it to
orders, batches, and single products. Looking at this data from a
process-oriented analysis point of view enables new analysis prospects. This
paper utilises these prospects in an experimental way by creating BPMN models
for the manufacturing of two real-world products: (1) a low volume, high
complexity lower-housing for a gas-turbine and (2) a high volume, low
complexity, small tolerance valve lifter for a gas turbine. In contrast to the
resource-based data collection, 30+ values are modeled into the BPMN models and
enacted by a workflow engine, creating execution logs in the XES standard
format. Conformance checks are carried out and interpreted for both scenarios
and it is shown how existing classification and clustering techniques can be
applied on the collected data in order to predict good and bad parts, ex-post
and potentially at run-time.
