Artificial Data Generation for Smart Manufacturing Systems: Discrete Event Simulation, Traceability, and Process Mining

• Alexandre Bellargus S. Costa ITA
• Juliano Y. Nishiura ITA
• Paulo Victor Lopes ITA / Chalmers University of Tech.
• Filipe Alves Neto Verri ITA
• Anders Skoogh Chalmers University of Tech.

DOI: https://doi.org/10.5753/eniac.2024.245210

Resumo

Industry 4.0 is revolutionizing the industrial sector with advanced technologies. In this scenario, process mining data mining for business and industrial processes is essential for optimizing operations by taking advantage of real-time data on performance, behaviour and trends. However, obtaining professional data and detailed models to apply process mining techniques is challenging. CLEMATIS is a discrete event simulation (DES) model that aims to generate data for this purpose, however, it was not meeting the technical requirements of process mining. This study therefore improves CLEMATIS to make it compatible with process mining techniques. The methodology involves the establishment of requirements, the traceability of production line resources via tokenization, the development of a visual simulation tool, and the construction of compatible event logs for commercial and academic use. The results shows the model’s effectiveness in applying process mining techniques in real time, meeting the needs of academic research in this area.

Palavras-chave: Discrete Event Simulation, Traceability, Process Mining

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