Graduation Term

2024

Degree Name

Master of Science (MS)

Department

Department of Technology

Committee Chair

Borinara Park

Abstract

This thesis introduces a methodology that integrates process mining with Discrete Event Simulation (DES) to improve the accuracy and applicability of simulation models. Traditional DES models often face challenges due to manual process mapping and subjective assumptions, leading to inaccuracies. By leveraging data from systems like ERP through process mining, this research generates precise process maps and high-quality data for DES modeling. The structured DES model incorporates real-time data feeds and iterative refinements to enhance model reliability. Process Mining tool is used to visualize and analyze actual process flows, identify bottlenecks, and provide performance metrics for model validation. This research advances simulation modeling by bridging the gap between theoretical capabilities and practical applications. It highlights the transformative potential of integrating process mining with DES, offering a more dynamic and precise tool for decision-making. Future research could explore automated model adjustments based on continuous data feeds and apply these methodologies across various sectors. The findings emphasize the importance of accurate data collection, model validation, and advanced tools to create robust DES models, setting a new standard for simulation accuracy and utility.

Access Type

Thesis-Open Access

DOI

https://doi.org/10.30707/ETD2024.20240827063557636615.999972

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