  Business Process Simulation (BPS) is a common approach to estimate the impact
of changes to a business process on its performance measures. For example, it
allows us to estimate what would be the cycle time of a process if we automated
one of its activities, or if some resources become unavailable. The starting
point of BPS is a business process model annotated with simulation parameters
(a BPS model). In traditional approaches, BPS models are manually designed by
modeling specialists. This approach is time-consuming and error-prone. To
address this shortcoming, several studies have proposed methods to
automatically discover BPS models from event logs via process mining
techniques. However, current techniques in this space discover BPS models that
only capture waiting times caused by resource contention or resource
unavailability. Oftentimes, a considerable portion of the waiting time in a
business process corresponds to extraneous delays, e.g., a resource waits for
the customer to return a phone call. This article proposes a method that
discovers extraneous delays from event logs of business process executions. The
proposed approach computes, for each pair of causally consecutive activity
instances in the event log, the time when the target activity instance should
theoretically have started, given the availability of the relevant resource.
Based on the difference between the theoretical and the actual start times, the
approach estimates the distribution of extraneous delays, and it enhances the
BPS model with timer events to capture these delays. An empirical evaluation
involving synthetic and real-life logs shows that the approach produces BPS
models that better reflect the temporal dynamics of the process, relative to
BPS models that do not capture extraneous delays.
