Multi-Flow Process Mining as an Enabler for Comprehensive Digital Twins of Manufacturing Systems

Process Mining (PM) has proven useful for extracting Digital Twin (DT) simulation models for manufacturing systems. PM is a family of approaches designed to capture temporal process flows by analyzing event logs that contain time-stamped records of relevant events. With the widespread availability of sensors in modern manufacturing systems, events can be tracked across multiple process dimensions beyond time, enabling a more comprehensive performance analysis. Some of these dimensions include energy and waste. By integrating and treating these dimensions analogously to time, we enable the use of PM to extract process flows along multiple dimensions, an approach we refer to as multi-flow PM. The resulting models that capture multiple dimensions are ultimately combined to enable comprehensive DTs that support multi-objective decision-making. In this paper, we present our approach to generating these multidimensional discrete-event models and, through an illustrative case study, demonstrate how they can be utilized for multi-objective decision support.