Process Derivation Methodology for Reconfigurable Smart Factory

• Published in: International journal of precision engineering and manufacturing Vol. 25; no. 3; pp. 497 - 508
• Main Authors: Choi, Seunghyun, Youm, Sekyoung, Kang, Yong-Shin
• Format: Journal Article
• Language: English
• Published: Seoul Korean Society for Precision Engineering 01.03.2024; Springer Nature B.V
• Subjects: Data analysis; Data collection; Derivation; Engineering; Factories; Industrial and Production Engineering; Industrial plants; Manufacturing; Materials Science; Methodology; Process management; Reconfiguration; Regular Paper; Sequential control; Sequential Pattern Mining; Pattern mining; Autonomous Reconfigurable Smart Factory; Time perspective; Formalism; Process management
• ISSN: 2234-7593; 2005-4602
• DOI: 10.1007/s12541-023-00820-9

Due to the development of computing technology, various data are collected and analyzed to extract meaningful information and are utilized in various industries. In particular, in the manufacturing industry, collected data are analyzed and used for process management, monitoring, quality prediction, etc., to establish smart factories and Reconfigurable Smart Factories. To continuously manage the processes of a Reconfigurable Smart Factory, it is necessary to derive a process model that includes factors affecting the process and to compare the actual execution process with the designed process and make improvements. The conventional methods for deriving process model are methods that apply to processes with a fixed start and end and are difficult to apply directly to the processes of a Reconfigurable Smart Factory. Furthermore, the conventional methods are sequential control flow-oriented, and a process derivation method that includes factors affecting the process is required. Therefore, it is necessary to consider various factors (e.g., time attributes, task time) that affect the process, not just the sequence, to solve this. In this study, we propose a novel method to derive a process that includes sequential flow and time conditions. The proposed methodology focuses on the execution time and order of instances to derive the process. Experiments were conducted by changing the conditions of the methodology and measuring the execution time to validate the proposed methodology.