Manufacturing process analysis framework for process mining: case study of fully automated factory applications

This paper presents a data-driven approach to improving the productivity of manufacturing companies operating under Make-To-Order (MTO). In this study, a comprehensive analysis of processes, time, resources, and quality is performed using process mining techniques. This enables an understanding of the manufacturing process flow from a global perspective and addresses bottlenecks and workload issues from a local perspective in the manufacturing environment. This approach was implemented in a fully automated machining and logistics testbed developed by the Korea Electronics Technology Institute. Through a case study, the practical application and effectiveness of this approach are demonstrated, including specific improvement proposals. The validation of these proposals through simulations, focusing on key processes, resulted in significant productivity improvements. Ultimately, this study aims to build a more efficient and competitive manufacturing environment by showcasing the potential of process mining and various data visualization and analysis techniques. The results of this study demonstrate that adhering to the proposed framework enables continuous process optimization and improved operational performance is achievable in the manufacturing sector.