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Use of the Dynamic Simulation to Reduce Handling Complexity in the Manufacturing Process

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EN
Abstrakty
EN
Many companies have started using dynamic simulation as full support for their own optimization team to optimize business processes. The 3D visualization can facilitate understanding of the links among processes and their connections. It can significantly contribute to its appropriate implementation, which aims at saving costs, simplifying processes, introducing new or innovated processes, etc. Application field is not significant for the 3D visualization. Predictive simulation can be applied in any process, from storage, logistics, handling, through production line optimization to distribution. The submitted paper deals with the optimization of the production process regarding the reduction of handling demands for the company in the automotive industry. Businesses are currently facing an issue of handling complexity, which has a relatively high cost, depending on the amount of unnecessary and chaotic trips within production processes. It is necessary to modify the charging method in any change of production. This is connected with an increase of non-productive rides. The article introduces the possibility of a variant solution with the possibility to use dynamic simulation as a powerful tool for the process optimization.
Twórcy
  • VŠB – Technical University of Ostrava, Faculty of Mechanical Engineering, 17. Listopadu 2172/15, 708 33 Ostrava – Poruba, Czech Republic
  • VŠB – Technical University of Ostrava, Faculty of Mechanical Engineering, 17. Listopadu 2172/15, 708 33 Ostrava – Poruba, Czech Republic
Bibliografia
  • 1. Cajzlová D. Production proces optimalization by the means of reducing manipulation demands. Master thesis. Vysoká škola báňská – Technická univerzita Ostrava. Ostrava 2012, Czech Republic.
  • 2. Cierna, H. and Sujova E. Application of modern QMS – Kaizen management system. MM Science Journal, November 2016, 1456–1464.
  • 3. Dlouhý, M. Simulace podnikových procesů. Computer Press, 2007.
  • 4. Fedorko G., Neradilová H. and Jackanin J. Discrete model simulation of a passenger cable car operation. Advances in Science and Technology Research Journal, 12(2), 2018, 170–179.
  • 5. Krolczyk J.B , Krolczyk G.M., Legutko S., Napiorkowski J., Hloch S., Foltys J. and Tama E. Material flow optimization – a case study in automotive industry. Technicki Vjestnik, 22(6), 2015, 1447–1456.
  • 6. Law A.M. Simulation modeling and analysis. McGraw-Hill, 2007.
  • 7. Liker J.K. The Toyota Way: 14 Management Principles from the World’s Greatest Manufacturer. McGraw-Hill, 2014.
  • 8. Méndez J.D.M. and Rodriguez R.S. Total productive maintenance (TPM) as a tool for improving productivity: a case study of application in the bottleneck of an auto-parts machining line. The International Journal of Advanced Manufacturing Technology, 92(1–4), 2017, 1013–1026.
  • 9. Patel A.B. and Desai T.N. A systematic review and meta-analysis of recent developments in sustainable supply chain management. International Journal of Logistics Research and Applications, 22(4), 2019, 349–370.
  • 10. Schindlerová V., Šajdlerová I. and Mohyla P. Optimization of metallurgical processes using dynamic simulation, Proc. of METAL 2016 – 25th Anniversary International Conference on Metallurgy and Materials, Brno, Czech Republic 2016, 2013–2018.
  • 11. Schindlerová V. and Šajdlerová I. Influence of tool wear on material flow. Advances in Science and Technology Research Journal, 11(1) 2017, 161–165.
  • 12. Worobel R, Čapek J., Kováčová L., Bubeník P. and Krajcovič M. Improving business processes using simulation tools. MM Science Journal, March 2018, 2244–2251.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b97eb582-9b5d-41d8-a5e0-4797221bbf36
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