Multi-flow Process Mining for Comprehensive Simulation Model Discovery

Process mining has proven effective in explaining the underlying processes of systems, thereby improving systems' understanding, analysis, and operational efficiency. Process mining, however, often falls short in addressing multiple dimensions of systems' behaviors, limiting its ability to provide comprehensive insights for systems' performance and optimization opportunities. In this paper, we introduce an enhancement to conventional process mining that we term Multi-flow Process Mining (MFPM), which effectively extracts process flows across different system dimensions, such as time, energy, waste, and carbon footprint. MFPM enables a more comprehensive view of a system's dynamics, enabling holistic decision-making for enhanced system efficiency. We detail the framework of MFPM, outline corresponding data requirements, and introduce an expanded version of Petri nets-used here as a modeling formalism to describe and analyze multi-flow system processes. Through a detailed case study, we demonstrate the practical application of MFPM in capturing and analyzing multifaceted aspects of systems.