  Operational processes in production, logistics, material handling,
maintenance, etc., are supported by cyber-physical systems combining hardware
and software components. As a result, the digital and the physical world are
closely aligned, and it is possible to track operational processes in detail
(e.g., using sensors). The abundance of event data generated by today's
operational processes provides opportunities and challenges for process mining
techniques supporting process discovery, performance analysis, and conformance
checking. Using existing process mining tools, it is already possible to
automatically discover process models and uncover performance and compliance
problems. In the DFG-funded Cluster of Excellence "Internet of Production"
(IoP), process mining is used to create "digital shadows" to improve a wide
variety of operational processes. However, operational processes are dynamic,
distributed, and complex. Driven by the challenges identified in the IoP
cluster, we work on novel techniques for comparative process mining (comparing
process variants for different products at different locations at different
times), object-centric process mining (to handle processes involving different
types of objects that interact), and forward-looking process mining (to explore
"What if?" questions). By addressing these challenges, we aim to develop
valuable "digital shadows" that can be used to remove operational friction.
