A Digital Process Twin Conceptual Architecture for What-If Process Analysis

Business processes require continuous changes or interventions to remain efficient and competitive over time. However, implementing these changes-such as reordering or adding new tasks- can negatively affect the overall process performance. A longstanding problem in Business Process Management is that of forecasting ex-ante the values that process performance measures will assume after implementing changes. To achieve this, the concept of Digital Process Twins, which extends the well-established Digital Twin paradigm, paves the way for new interesting opportunities. Digital Process Twins enable enhanced what-if analysis by virtually predicting process performance under various changes, thus allowing for informed decision-making before actuating process changes in the real world. However, despite recognition as one of the new key enablers of modern process re-engineerization, a comprehensive approach to implementing Digital Process Twins is still lacking. This paper proposes a novel conceptual architecture for deploying Digital Process Twins to address this gap. Additionally, we introduce Dolly, a framework that implements such conceptual architecture using a multi-modeling approach combining domain data and process modeling along with a data-driven process simulation technique.