  Performance analysis in process mining aims to provide insights on the
performance of a business process by using a process model as a formal
representation of the process. Such insights are reliably interpreted by
process analysts in the context of a model with formal semantics. Existing
techniques for performance analysis assume that a single case notion exists in
a business process (e.g., a patient in healthcare process). However, in
reality, different objects might interact (e.g., order, item, delivery, and
invoice in an O2C process). In such a setting, traditional techniques may yield
misleading or even incorrect insights on performance metrics such as waiting
time. More importantly, by considering the interaction between objects, we can
define object-centric performance metrics such as synchronization time, pooling
time, and lagging time. In this work, we propose a novel approach to
performance analysis considering multiple case notions by using object-centric
Petri nets as formal representations of business processes. The proposed
approach correctly computes existing performance metrics, while supporting the
derivation of newly-introduced object-centric performance metrics. We have
implemented the approach as a web application and conducted a case study based
on a real-life loan application process.
