Validation of a Digital Simulation Model for Maintenance in a High-Volume Automated Manufacturing Facility. Issue 19 (2022)

• Record Type: Journal Article
• Authors: Ruane, Patrick; Walsh, Patrick; Cosgrove, John
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 19(2022)
• Page Start: 127
• Page End: 132
• Publication Date: 2022
• Subjects: Simulation Model -- Digital Model Validation -- OEE -- MTBF -- MTTR -- Reliability; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2022.09.195
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Digitalization in manufacturing is the conversion of information into digital format, the integration of this digital data and technologies into the manufacturing process and the use of those technologies (eg: simulation) to change a business model to provide new revenue and value-producing opportunities. Digitalization may be seen as the increased generation, analysis, and use of data to improve the efficiency of the overall manufacturing system. Simulation in manufacturing is often applied in situations where conducting experiments on a real system is impossible or very difficult due to cost or time to carry out the experiment is too long. A key input to the simulation model of automated equipment is the acquisition of valid data in relation to cycle time and reliability of various workstations on this line. As a consequence of being able to simulate equipment processes and interact with this validated simulation model, both the understanding of how the production system will perform under varying reliability and cycle time conditions is achieved. The simulation model then enables the experimentation of 'what if scenarios' that can be tested easily, while also providing a valuable tool to inform the maintenance personnel what station reliabilities they need to target in order to sustain a high performing manufacturing line. The author has adopted an open source simulation tool (JaamSim) to develop a digital model of an automated production line in a Johnson & Johnson Vision Care (JJVC) manufacturing facility. This research demonstrated how a digital model was validated for use. The validated digital model can then be used by the author/facility engineering teams to perform scenario testing during the design stage of the line. This simulation model can also be used as a subset of an optimization system to determine recommended optimum line parameters to maximize line performance, either during the line design or when line is in operation.