A Process Mining Based Approach to Complex Manufacturing Process Flow Analysis: A Case Study

Abstract

With recent advances in IT infrastructure in manufacturing environments, a large amount of manufacturing data are collected and stored in a database at various stages of production. These data may include valuable information for manufacturing companies to improve their manufacturing processes. The method of manufacturing data analysis is crucial for understanding the manufacturing data. However, traditional manufacturing data analysis methods such as data mining, simulation, etc. have limitations for this purpose since those are difficult to provide overall process-level information. Therefore, in this thesis, a process mining based approach for analyzing complex manufacturing processes is proposed. Process mining is a useful tool for process-related knowledge acquisition since it enables users to derive not only manufacturing process models, but also several performance measures related to processes, resources, and tasks. This thesis suggests a framework for the manufacturing process analysis. To do this, it applies process mining techniques to perform four types of analysis, which are visualization of production flows, machine-to-machine inter-relationship analysis, machine utilization, and monitoring & diagnosis of task performance regarding yield rate and lead time. Furthermore, a case study is conducted to support the proposed framework with an event log of an electronic components manufacturing process.