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Process mining and simulation modeling in production system : a review

Kurniasih Julia, Abas Zuraida Abal, Asmai Siti Azirah, Wibowo Agung Budhi

LogForum

2024

Vol. 20, no. 4

429--453

Background: Production systems often face challenges that impact efficiency, productivity, and overall performance. Various approaches and techniques are employed to address these issues. This study examines the practical application of process mining and simulation modeling to resolve problems in production systems, with a focus on optimizing the event log data recorded by computer-assisted production processes. Process mining analyzes this data to reveal actual workflows, while simulation modeling explores design alternatives and predicts future performance issues. This study provides a comprehensive review of process mining and simulation modeling integration, offering insights into how these methods improve system performance in production environments. Methods: This research implements bibliometric analysis and a systematic literature review based on a systematic mapping approach. In accordance with PRISMA guidelines and the inclusion and exclusion criteria, 59 articles were deemed eligible for bibliometric analysis, with 53 selected for the literature review. Results: Bibliometric analysis indicates that current publication trends focus on simulation, particularly discrete event simulation, with an emphasis on production systems, simulation models, and production control. The literature review reveals that process mining and simulation modeling address various issues, including system flexibility and reliability, data compatibility and synchronization, product quality monitoring, asset maintenance, cycle time prediction, resource bottleneck management, capacity allocation, productivity, machine downtime, process design, waste utilization, carbon emission prediction, and energy efficiency. Discrete event simulation is the most commonly used approach in process mining for production systems. Existing research in this area often addresses problems with limited improvement potential rather than large-scale challenges, particularly in process investigation, digital process modeling, validation and verification, production operation optimization, predictive analysis, assessment and management, continuous enhancement, and decision support. Conclusions: Integrating process mining with simulation modeling helps production systems tackle operational challenges, optimize performance, and improve decision-making. This approach provides valuable insights for managing complex environments and enhances scheduling, resource management, and sustainability.