{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T06:10:48Z","timestamp":1774419048625,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T00:00:00Z","timestamp":1774224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Manufacturing is going through a significant shift propelled by Industry 4.0 and smart manufacturing infrastructures, requiring sophisticated production control techniques that can adaptively adjust to fluctuating operational situations. This paper presents a novel five-step hybrid simulation framework for adaptive real-time production speed control in smart manufacturing lines, integrating conceptual modelling, hybrid simulation, algorithm redefinition, design of experiments, optimisation, and real-system implementation. The framework transforms the speed management systems into online digital twins capable of optimising system performance and mitigating unforeseen fluctuations, faults, and congestion. A comprehensive case study from the beverage manufacturing sector demonstrates the framework\u2019s effectiveness, utilising a universal simulation platform to model both continuous fluid flow and discrete event processes. The proposed stepwise, multi-threshold algorithm employs multiple distinct logical thresholds evaluated sequentially to optimise both upstream and downstream station speeds, with decision thresholds independently adjustable for each production line segment. The experimental results show significant improvements, including around an 18% increase in overall throughput and a 95.7% reduction in work-in-process inventory. A comprehensive resiliency analysis and statistical tests under various disruption scenarios further validated the approach, demonstrating its superiority. Beyond the studied case, the framework provides a transferable pathway for real-time adaptive control across a wide range of smart manufacturing environments, enabling enhancements to operational efficiency without requiring additional capital investment in new equipment or infrastructure.<\/jats:p>","DOI":"10.3390\/systems14030335","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T15:54:53Z","timestamp":1774281293000},"page":"335","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive Real-Time Speed Control for Automated Smart Manufacturing Systems: A Disturbance-Resilient Solution for Productivity"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6479-7555","authenticated-orcid":false,"given":"Ahmad","family":"Attar","sequence":"first","affiliation":[{"name":"Exeter Digital Enterprise Systems Laboratory (ExDES), Department of Engineering, University of Exeter, Streatham Campus, Exeter EX4 4QF, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4863-4005","authenticated-orcid":false,"given":"Shuya","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Management, University of Bath, Bath BA2 7AY, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1368-8284","authenticated-orcid":false,"given":"Martino","family":"Luis","sequence":"additional","affiliation":[{"name":"Exeter Digital Enterprise Systems Laboratory (ExDES), Department of Engineering, University of Exeter, Streatham Campus, Exeter EX4 4QF, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3071-5691","authenticated-orcid":false,"given":"Voicu Ion","family":"Sucala","sequence":"additional","affiliation":[{"name":"Exeter Digital Enterprise Systems Laboratory (ExDES), Department of Engineering, University of Exeter, Streatham Campus, Exeter EX4 4QF, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101837","DOI":"10.1016\/j.rcim.2019.101837","article-title":"Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues","volume":"61","author":"Lu","year":"2020","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1016\/j.eng.2019.01.014","article-title":"Digital twins and cyber\u2013physical systems toward smart manufacturing and industry 4.0: Correlation and comparison","volume":"5","author":"Tao","year":"2019","journal-title":"Engineering"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1080\/00207543.2017.1351644","article-title":"Smart manufacturing","volume":"56","author":"Kusiak","year":"2018","journal-title":"Int. J. Prod. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s11465-018-0499-5","article-title":"Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives","volume":"13","author":"Zheng","year":"2018","journal-title":"Front. Mech. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1080\/0951192X.2019.1699254","article-title":"The framework design of smart factory in discrete manufacturing industry based on cyber-physical system","volume":"33","author":"Chen","year":"2020","journal-title":"Int. J. Comput. Integr. Manuf."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"\u017didek, K., Pite\u0142, J., Ad\u00e1mek, M., Lazor\u00edk, P., and Ho\u0161ovsk\u00fd, A. (2020). Digital Twin of Experimental Smart Manufacturing Assembly System for Industry 4.0 Concept. Sustainability, 12.","DOI":"10.3390\/su12093658"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.jmsy.2020.06.010","article-title":"Smart manufacturing process and system automation\u2014A critical review of the standards and envisioned scenarios","volume":"56","author":"Lu","year":"2020","journal-title":"J. Manuf. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1016\/j.procs.2023.08.109","article-title":"Digital Twin based Smart Manufacturing; From Design to Simulation and Optimization Schema","volume":"221","author":"Ebni","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_9","first-page":"475","article-title":"Optimizing smart manufacturing systems using digital twin","volume":"18","author":"Ojstersek","year":"2023","journal-title":"Adv. Prod. Eng. Manag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/17477778.2025.2478976","article-title":"Managing uncertainty in production sequencing: A digital twin framework for mixed-model assembly lines","volume":"13","author":"Fani","year":"2025","journal-title":"J. Simul."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1080\/17477778.2021.1931500","article-title":"A hybrid simulation approach applied in sustainability performance assessment in make-to-order supply chains: The case of a commercial aircraft manufacturer","volume":"17","author":"Barbosa","year":"2023","journal-title":"J. Simul."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106874","DOI":"10.1016\/j.compchemeng.2020.106874","article-title":"Hybrid Modeling in the Era of Smart Manufacturing","volume":"140","author":"Yang","year":"2020","journal-title":"Comput. Chem. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1007\/s12008-021-00778-w","article-title":"A flexible and open environment for discrete event simulations and smart manufacturing","volume":"15","author":"Martinez","year":"2021","journal-title":"Int. J. Interact. Des. Manuf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1057\/s41273-016-0037-6","article-title":"Simulation optimization in the era of Industrial 4.0 and the Industrial Internet","volume":"10","author":"Xu","year":"2016","journal-title":"J. Simul."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.iotcps.2023.04.006","article-title":"Internet of things for smart factories in industry 4.0, a review","volume":"3","author":"Soori","year":"2023","journal-title":"Internet Things Cyber Phys. Syst."},{"key":"ref_16","unstructured":"Attar, A. (2022, January 12\u201314). Conceptual Framework for Multi-State Systems in Smart Factories using Reliability-Redundancy Allocation. Proceedings of the 2022 Conference on Manufacturing Technologies and Industrial Engineering, Tehran, Iran."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.1016\/j.procs.2019.11.244","article-title":"A Generic Evaluation Framework of Smart Manufacturing Systems","volume":"161","author":"Mahmoud","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1660","DOI":"10.1016\/j.promfg.2020.01.118","article-title":"Modelling a Platform for Smart Manufacturing System","volume":"38","author":"Park","year":"2019","journal-title":"Procedia Manuf."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.jmsy.2020.06.012","article-title":"A digital twin to train deep reinforcement learning agent for smart manufacturing plants: Environment, interfaces and intelligence","volume":"58","author":"Xia","year":"2021","journal-title":"J. Manuf. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1057\/s41273-016-0006-0","article-title":"Emulation of control strategies through machine learning in manufacturing simulations","volume":"11","author":"Bergmann","year":"2017","journal-title":"J. Simul."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"101578","DOI":"10.1016\/j.rcim.2023.102578","article-title":"A cyber-physical robotic mobile fulfillment system in smart manufacturing: The simulation aspect","volume":"83","author":"Keung","year":"2023","journal-title":"Robot. Comput. Integr. Manuf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1057\/jos.2011.20","article-title":"Evaluating production improvement opportunities in a chemical plant: A case study using discrete event simulation","volume":"6","author":"Sharda","year":"2012","journal-title":"J. Simul."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kuncova, M., and Zajoncova, M. (2018). Discrete event simulation usage to model and optimize the production line. MM Sci. J., 2240\u20132247.","DOI":"10.17973\/MMSJ.2018_03_2017117"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1080\/002075400189509","article-title":"Quantifying benefits of conversion to lean manufacturing with discrete event simulation: A case study","volume":"38","author":"Detty","year":"2000","journal-title":"Int. J. Prod. Res."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Pekarcikova, M., Trebuna, P., Kliment, M., and Dic, M. (2021). Solution of Bottlenecks in the Logistics Flow by Applying the Kanban Module in the Tecnomatix Plant Simulation Software. Sustainability, 13.","DOI":"10.3390\/su13147989"},{"key":"ref_26","first-page":"250","article-title":"Simulation and Genetic Algorithm-based approach for multi-objective optimization of production planning: A case study in industry","volume":"18","author":"Bojic","year":"2023","journal-title":"Adv. Prod. Eng. Manag."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Pihl, S., Attar, A., Luis, M., and Haugen, \u00d8. (2025). Digital Twins for Optimizing the Transition from Job-Shop to Mass Production: Insights from Marine Pump Manufacturing in Scandinavia. Proceedings of the 2025 Winter Simulation Conference (WSC), IEEE.","DOI":"10.1109\/WSC68292.2025.11338892"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1080\/09537287.2020.1830194","article-title":"Real-time data-driven discrete-event simulation for garment production lines","volume":"33","author":"Jung","year":"2020","journal-title":"Prod. Plan. Control"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"de Groot, L.R., and Hubl, A. (2021). Developing a Calibrated Discrete Event Simulation Model of Shops of a Dutch Phone and Subscription Retailer During COVID-19 to Evaluate Shift Plans to Reduce Waiting Times. Proceedings of the 2021 Winter Simulation Conference (WSC), IEEE.","DOI":"10.1109\/WSC52266.2021.9715306"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Grzn\u00e1r, P., Kraj\u010dovi\u010d, M., Gola, A., Dulina, L., Furmannov\u00e1, B., Mozol, S., Plinta, D., Burganov\u00e1, N., Danilczuk, W., and Svitek, R. (2021). The Use of a Genetic Algorithm for Sorting Warehouse Optimisation. Processes, 9.","DOI":"10.3390\/pr9071197"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Huynh, B.H., and Akhtar, H. (2020). Discrete event simulation for manufacturing performance management and optimization: A case study for model factory. Proceedings of the 2020 9th International Conference on Industrial Technology and Management (ICITM), IEEE.","DOI":"10.1109\/ICITM48982.2020.9080394"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1016\/j.ifacol.2015.06.141","article-title":"About The Importance of Autonomy and Digital Twins for the Future of Manufacturing","volume":"48","author":"Rosen","year":"2015","journal-title":"IFAC-PapersOnLine"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"939","DOI":"10.1016\/j.promfg.2017.07.198","article-title":"A review of the roles of Digital Twin in CPS-based production systems","volume":"11","author":"Negri","year":"2017","journal-title":"Procedia Manuf."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Lugaresi, G., and Matta, A. (2020). Generation and Tuning of Discrete Event Simulation Models for Manufacturing Applications. Proceedings of the 2020 Winter Simulation Conference, IEEE.","DOI":"10.1109\/WSC48552.2020.9383870"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.procir.2018.08.312","article-title":"Multiple System Dynamics and Discrete Event Simulation for manufacturing system performance evaluation","volume":"78","author":"Litwin","year":"2018","journal-title":"Procedia CIRP"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1057\/s41273-016-0048-3","article-title":"Processing incomplete data for simulation-based production planning in shipbuilding","volume":"11","author":"Steinhauer","year":"2017","journal-title":"J. Simul."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1016\/j.promfg.2018.07.148","article-title":"A simulation-based platform for assessing the impact of cyber-threats on smart manufacturing systems","volume":"26","author":"Bracho","year":"2018","journal-title":"Procedia Manuf."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"282","DOI":"10.6028\/jres.121.013","article-title":"Methods and Tools for Performance Assurance of Smart Manufacturing Systems","volume":"121","author":"Kibira","year":"2016","journal-title":"J. Res. Natl. Inst. Stand. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1177\/0037549716644055","article-title":"Simulation\u2013Optimization Approach for a Continuous-Review, Base-Stock Inventory Model with General Compound Demands, Random Lead Times, and Lost Sales","volume":"92","author":"Attar","year":"2016","journal-title":"Simulation"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Attar, A., Babaee, M., Raissi, S., and Nojavan, M. (2024). Airside Optimization Framework Covering Multiple Operations in Civil Airport Systems with a Variety of Aircraft: A Simulation-Based Digital Twin. Systems, 12.","DOI":"10.3390\/systems12100394"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Niekurzak, M., Lewicki, W., Coban, H.H., and Bera, M. (2023). A Model to Reduce Machine Changeover Time and Improve Production Efficiency in an Automotive Manufacturing Organisation. Sustainability, 15.","DOI":"10.3390\/su151310558"}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/14\/3\/335\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T05:19:33Z","timestamp":1774415973000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/14\/3\/335"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,23]]},"references-count":41,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["systems14030335"],"URL":"https:\/\/doi.org\/10.3390\/systems14030335","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,23]]}}}