20.500.12556/DKUM-87708 Optimizing smart manufacturing systems using digital twin Presented paper investigates the application of digital twins for the optimisation of intelligent manufacturing systems and focuses on the comparison between simulation modelling results and real-world production conditions. A digital twin was created in the Simio software environment using a data-driven simulation model derived from a real-world production system. Running the digital twin in real time, which was displayed graphically, facilitated the analysis of key parameters, including the number of finished products, average flow time, workstation utilization and product quality. The discrepancies were attributed to the use of random distributions of input data in the dynamic digital twin, as opposed to the long-term measurements and averages in the real-world system. Despite the limitations in the case study, the results underline the financial justification and predictive capabilities of digital twins for optimising production systems. Real-time operation enables continuous evaluation and tracking of parameters and offers high benefits for intelligent production systems. The study emphasises the importance of accurate selection of input data and warns that even small deviations can lead to inaccurate results. Finally, the paper high-lights the role of digital twins in optimising production systems and argues for careful consideration of input data. It highlights the importance of analysing real-world production systems and creating efficient simulation models as a basis for digital twin solutions. The results encourage extending the research to different types of production, from job shop to mass production, in order to obtain a comprehensive optimisation perspective. smart manufacturing digital twin optimisation simulation modelling Simio case study pametna proizvodnja simulacijsko modeliranje digitalni dvojček optimizacija študija primera true false true Chair of Production Engineering (CPE), University of Maribor, Faculty of Mechanical Engineering Angleški jezik Slovenski jezik https://apem-journal.org/ Content from this work may be used under the terms of the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Članek v reviji 2024-03-25 10:50:57 2024-03-25 10:50:58 2026-02-19 07:18:45 0000-00-00 00:00:00 2023 0 0 str. 475-485 no. 4 Vol. 18 Dec. 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2023-07-19 2023-12-20 2023-12-28 658.5:004.94 182000131 10.14743/apem2023.4.486 1854-6250 APEM18-4_475-485.pdf APEM18-4_475-485.pdf 1 DA91EF25DC6CAD0BDD3E6671131509F2 d6ffad7f7231cfe980e305b8d93c457fe367dfbf15cd492b4dcd668045dccba6 10eab10e-ea90-11ee-affd-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=177365 https://apem-journal.org/Archives/2023/APEM18-4_475-485.pdf 0 e0ee3cb1-f663-11ee-9d31-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=178121 Fakulteta za strojništvo Advances in production engineering & management 0 0 0