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Tytuł artykułu

Analysis of Production Process Performance Using Computer

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Planning a production process in terms of correctly defining its efficiency, duration and the level of generated costs is a difficult task. Manufacturing processes are characterized by a high level of complexity and are exposed to a large number of external factors that are difficult to predict. In many cases, one of the tools supporting decisions in the analysis of manufacturing process parameters is the use of computer simulation both at the process planning stage and in real time during its implementation. In this article, a computer simulation tool (FlexSim simulation software) was used to analyze the parameters of a selected production process. The conducted analyses allowed for indicating the direction of actions in the use of computer simulation as a decision support tool at selected stages of production process planning in the area of evaluating solutions aimed at implementing improvements.
Wydawca
Rocznik
Tom
Strony
414--419
Opis fizyczny
Bibliogr. 29 poz., rys., tab.
Twórcy
  • Silesian University of Technology Faculty of Materials Engineering ul. Krasińskiego 8, 40-019 Katowice, Poland
  • Silesian University of Technology Faculty of Materials Engineering ul. Krasińskiego 8, 40-019 Katowice, Poland
Bibliografia
  • [1] D.L.M. Nascimento, V. Alencastro, O.L.G. Quelhas, R.G.G. Caiado, J.A. Garza-Reyes, L. Rocha-Lona, G. Tortorella, „Odkrywanie technologii Przemysłu 4.0 umożliwiających stosowanie praktyk gospodarki o obiegu zamkniętym w kontekście produkcyjnym: propozycja modelu biznesowego”, Journal of Manufacturing Technology Management, vol. 30, pp. 607-627, 2019.
  • [2] W. Lewicki, M. Niekurzak, J. Wróbel, „Development of a Simulation Model to Improve the Functioning of Production Processes Using the FlexSim Tool”, Applied Sciences, vol. 14(16), pp. 6957, 2024.
  • [3] A. Jamwal, R. Agrawal, M. Sharma, A. Giallanza, „Technologie Przemysłu 4.0 dla zrównoważonego rozwoju produkcji: przegląd systematyczny i przyszłe kierunki badań”, Applied Sciences, vol. 11(11), pp. 5725, 2021.
  • [4] M. Barton, R. Budjac, P. Tanuska, G. Gaspar, P. Schreiber, „Identification overview of Industry 4.0 essential attributes and resource-limited embedded artificial-intelligence-of-things devices for small and medium-sized enterprises”, Applied Sciences, vol. 12(11), pp. 5672, 2022.
  • [5] I.J. Akpan, O.F. Offodile, „The Role of Virtual Reality Simulation in Manufacturing in Industry 4.0”, Systems, vol. 12(1), pp. 26, 2024.
  • [6] V. Liagkou, D. Salmas, C. Stylios, „Realizing virtual reality learning environment for industry 4.0”, Procedia CIRP, vol. 79, pp. 712-717, 2019.
  • [7] Y.-T. Jou, M.-C. Lin, R.M. Silitonga, S.-Y. Lu, N.-Y. Hsu, „A Systematic Model to Improve Productivity in a Transformer Manufacturing Company: A Simulation Case Study”, Applied Sciences, vol. 14(2), pp. 519, 2024.
  • [8] P. Sharma, „Discrete-Event Simulation”, International Journal of Science Technology Research, vol. 4(5), pp. 136-140, 2015.
  • [9] P. Barosz, G. Gołda, A. Kampa, „Efficiency Analysis of Manufacturing Line with Industrial Robots and Human Operators”, Applied Sciences, vol. 10(8), pp. 2862, 2020.
  • [10] M. Beaverstock, A. Greenwood, E. Lavery, W. Nordgren, „Applied Simulation: Modeling and Analysis Using FlexSim”, FlexSim Software Products Inc., Orem, UT, USA, 2012.
  • [11] A. Gola, Ł. Wiechetek, „Modelling and simulation of production flow in job-shop production system with enterprise dynamics software”, Applied Computer Science, vol. 13(2), pp. 87-97, 2017.
  • [12] U. Sarvan Kumar, Y. Shivraj Narayan, „Productivity improvement in a windows manufacturing layout using FlexSim simulation software”, International Journal of Research Advent Technology.
  • [13] H. Huihui, M. Xiaoxia, M. Xiangguo, „Simulation and optimization of warehouse operation based on FlexSim”, Journal of Applied Science and Engineering Innovation, vol. 3(4), pp. 125-128, 2016.
  • [14] M. Kikolski, „Identification of production bottlenecks with the use of Plant Simulation Software”, Economics and Management, vol. 8(1), pp. 103-112, 2016.
  • [15] M. Pekarcikova, P. Trebuna, M. Kliment, M. Dic, „Solution of Bottlenecks in the Logistics Flow by Applying the Kanban Module in the Tecnomatix Plant Simulation Software”, Sustainability, vol. 13(8), pp. 7989, 2021.
  • [16] A. Gola, Ł. Wiechetek, „Modelling and simulation production flow in job-shop production system with Enterprise Dynamics software”, Applied Computer Science, vol. 13(2), pp. 87-97, 2017.
  • [17] R. Ferro, G.A. Cordeiro, R.E.C. Ordóñez, G. Beydoun, N. Shukla, „An Optimization Tool for Production Planning: A Case Study in a Textile Industry”, Applied Sciences, vol. 11(16), pp. 8312, 2021.
  • [18] M. Santos, E.L.S.B. Júnior, G.S. Palermo, R.A. Walker, M.F. Reis, „Application of FlexSim software in the analysis of waiting time in hospital units of Rio de Janeiro: An approach from the Discrete Events Simulation”, Proceedings of the VII Congresso Lean Six-Sigma, Campinas, Brazil, pp. 12-13, April 2018.
  • [19] G. Fedorko, V. Molnár, J. Strohmandl, P. Horváthová, D. Strnad, V. Cech, „Research on Using the Tecnomatix Plant Simulation for Simulation and Visualization of Traffic Processes at the Traffic Node”, Applied Sciences, vol. 12(11), pp. 12131, 2022.
  • [20] L. Patvivatsiri, E.J. Montes, O. Xi, „Modeling bioterrorism preparedness with simulation in rural healthcare system”, Proceedings of the 2007 Winter Simulation Conference, Washington, DC, USA, pp. 1155-1160, December 2007.
  • [21] R. Ferro, G.A. Cordeiro, R.E.C. Ordóñez, G. Beydoun, N. Shukla, „An Optimization Tool for Production Planning: A Case Study in a Textile Industry”, Applied Sciences, vol. 11(16), pp. 8312, 2021.
  • [22] G.M. Nawara, W.S. Hassanein, „Solving the job-shop scheduling problem by Arena simulation software”, International Journal of Engineering Innovation Research, vol. 2(3), pp. 161-166, 2013.
  • [23] M. Breznik, B. Buchmeister, N. Vujica Herzog, „Assembly Line Optimization Using MTM Time Standard and Simulation Modeling – A Case Study”, Applied Sciences, vol. 13(11), pp. 6265, 2023.
  • [24] M. Krynke, „Optimizing Supply Chain in a Foundry Through Computer Simulation Using Flexsim – A Case Study”, System Safety: Human – Technical Facility – Environment, vol. 5(1), pp. 172-181, 2023.
  • [25] G. Chryssolouris, „Manufacturing Systems – Theory and Practice”, Springer, New York, NY, USA, 2005.
  • [26] S. Robinson, „Conceptual modelling for simulation part I: Definition and requirements”, Journal of the Operational Research Society, vol. 59(3), pp. 278-290, 2008.
  • [27] Y.R. Wang, A.N. Chen, „Production logistics simulation and optimization of industrial enterprise based on FlexSim”, International Journal of Simulation Modelling, vol. 15, pp. 732-741, 2016.
  • [28] J.W. Kim, J.S. Park, S.K. Kim, „Application of FlexSim software for developing cyber learning factory for smart factory education and training”, Multimedia Tools and Applications, vol. 79, pp. 16281-16297, 2020.
  • [29] J. Mikulik, W.A. Cempel, S. Kracik, D. Dąbal, „A Simulation Model for Emergency Evacuation Time and Low-Cost Improvements of a Hospital Facility Using FlexSim Healthcare: A Case Study”, Intelligent Systems in Technical and Medical Diagnostics, J. Korbicz, M. Kowal (Eds.), Advances in Intelligent Systems and Computing, vol. 230, Springer, Berlin/Heidelberg, Germany, pp. 115-124, 2014.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0add75f3-51f0-490a-bf61-1e891c45ca13
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