Enhancing Business Process Simulation Models with Extraneous Activity Delays
David Chapela-Campa, Marlon Dumas
TL;DR
The paper tackles the gap in BPS model discovery by modeling extraneous delays—waiting times not caused by resource contention or unavailability—using event-log analysis. It introduces causal-consecutive activity identification and three delay-estimation variants (Naive, Eclipse-aware, Extrapolated Eclipse-aware), then augments BPS models with timer events drawn from fitted delay distributions, with optional TPE-based hyperparameter tuning. Through synthetic and real-life evaluations, the approach improves temporal fidelity of simulations, with Eclipse-aware variants generally outperforming naive methods, albeit at higher computational cost when using TPE. The work demonstrates practical impact for producing what-if capable, temporally accurate process simulations and outlines avenues to further refine resource-specific delays and multitasking handling.
Abstract
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.