From Static Models to Dynamic Systems: The Evolution of Business Process Simulation to Digital Twins

The paper explores the progression from traditional process simulation techniques to the advanced concept of Digital Twins within the context of business processes and supply chain management. Initially, it outlines various discrete-event simulations and their suitability for analyzing business processes. Furthermore, it introduces the author’s developments in simulation algorithms, particularly the “Hybrid Model Simulation” and “SIMchronization” methods. The Hybrid Model Simulation approach allows simulation across different control flow-oriented modeling notations. In contrast, SIMchronization focuses on synchronizing material and information flows in supply chain networks. A significant focus is on Digital Twins that integrate models, simulations, and real-time data to provide a dynamic representation of physical systems. The work culminates in proposing an extended SIMchronization method, integrating real-time data interfaces and artificial intelligence (AI) models to provide dynamic representation and adaptive behavior modeling of physical systems. This proposed extension aims to bring the capabilities of Digital Twins to the SIMchronization method, enhancing its utility in rapidly changing real-world environments. Overall, the paper contributes to the understanding of the evolution and application of simulation techniques in business process management, highlighting the transformative impact of Digital Twins and the potential of integrating AI and real-time data into existing simulation methodologies.