The Impact of Process Complexity on Process Performance: A Study using Event Log Data

TL;DR

This study investigates how process complexity, measured through 38 metrics in event logs, significantly affects process performance, specifically throughput time, with models explaining up to 96% of variance.

Contribution

It introduces a comprehensive set of event log-based complexity metrics and demonstrates their strong explanatory power for process throughput time.

Findings

Complexity metrics explain up to 96% of throughput time variance.

Event log data effectively captures process complexity.

Regression models show a strong link between complexity and performance.

Abstract

Complexity is an important characteristic of any business process. The key assumption of much research in Business Process Management is that process complexity has a negative impact on process performance. So far, behavioral studies have measured complexity based on the perception of process stakeholders. The aim of this study is to investigate if such a connection can be supported based on the analysis of event log data. To do so, we employ a set of 38 metrics that capture different dimensions of process complexity. We use these metrics to build various regression models that explain process performance in terms of throughput time. We find that process complexity as captured in event logs explains the throughput time of process executions to a considerable extent, with the respective R-squared reaching up to 0.96. Our study offers implications for empirical research on process performance and can serve as a toolbox for practitioners.