Linking Actor Behavior to Process Performance Over Time

TL;DR

This paper introduces a novel method combining actor behavior analysis with Granger causality to uncover how individual actions influence process performance over time, providing deeper insights into process dynamics.

Contribution

It presents an integrated approach using time series analysis and Group Lasso for lag selection to identify causal links between actor behavior and process outcomes.

Findings

Actor behavior significantly impacts throughput time.

A small set of influential lags captures most causal effects.

Actor-centric time series methods reveal complex process dependencies.

Abstract

Understanding how actor behavior influences process outcomes is a critical aspect of process mining. Traditional approaches often use aggregate and static process data, overlooking the temporal and causal dynamics that arise from individual actor behavior. This limits the ability to accurately capture the complexity of real-world processes, where individual actor behavior and interactions between actors significantly shape performance. In this work, we address this gap by integrating actor behavior analysis with Granger causality to identify correlating links in time series data. We apply this approach to realworld event logs, constructing time series for actor interactions, i.e. continuation, interruption, and handovers, and process outcomes. Using Group Lasso for lag selection, we identify a small but consistently influential set of lags that capture the majority of causal influence, revealing that actor behavior has direct and measurable impacts on process performance, particularly throughput time. These findings demonstrate the potential of actor-centric, time series-based methods for uncovering the temporal dependencies that drive process outcomes, offering a more nuanced understanding of how individual behaviors impact overall process efficiency.