Process mining-enabled digital twins: a systematic literature review of techniques, simulation methods, and supply chain 5.0 implications

Digital Twins (DTs) enable real-time monitoring and adaptive decision-making, yet they are often developed through expert-driven approaches that only partially reflect real operations. Process Mining (PM) offers an alternative by extracting process knowledge from event logs, supporting DT creation, validation, and continuous updating. Despite growing interest, PM-DT research remains fragmented. This Systematic Literature Review (2020-2026) examines sectors adopting PM for DTs, the PM techniques used, and the modelling approaches combined with them. Results show the prevalence of generic frameworks, with manufacturing as the most mature field; Process Discovery is the dominating technique, while Conformance Checking is less explored. Discrete Event Simulation is the main modelling approach, complemented by Agent-Based Modelling, Petri Nets, and System Dynamics. The review also focuses on limited supply chain applications and frames PM-DT contributions in relation to the pillars of Supply Chain 5.0, highlighting directions for future research and practical development of data-driven PM-DT integrated systems.