Process mining and production routing fast profiling for data-driven digital twins

Abstract

Industry increasingly focuses on Digital Shadows and Twins of production lines, especially for planning, controlling, and optimizing operations. In parallel, shop floor processes can be described using Discrete Event Simulation (DES) models, which are ranked among the top tools for manufacturing system decision support. Although, Process Mining (PM) and model-driven Digital Twins (DT) were investigated in separate research communities. The integration of these two research fields is essential for advancing industrial applications by reducing time and efforts to model and describe processes. Thus, the objective of this paper is to propose a data integration pipeline to enhance realistic event logs and support the early stages of Data-driven Modelling of DT through PM techniques. This paper is expected to provide three relevant contributions. The first contribution is the enhancement of the production system event logs through the implementation of data integration techniques. The second contribution is to enable machine learning techniques to be applied by trace profiling the enhanced event logs, generating an attribute-value database. The third contribution is to extract value from a process-centered analysis, increasing the data value from a practical perspective.