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Abstract

The dynamics of the market, changes in the implementation of products in a short time, often customized products, require many analyses and actions in a short time. The problem that arises in this type of aspect is what should be done and how. One of the possibilities is the use of selected Industry 4.0 technologies to perform relevant analyses. It is also important because of the need to constantly change, to become more competitive in the market, but also to look at opportunities for improvement without stopping the implementation of production processes. Therefore, the article also focuses on the applicability of one of the technologies, namely simulation and the type of results obtained from the analyses performed. An algorithm for creating simulations that supports the implementation of this technology is also described.

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References

  1. Bozarth, C.C., Warsing, D.P., Flynn, B.B., Flynn, E.J.: The impact of supply chain complexity on manufacturing plant performance. J. Oper. Manag. 27, 80–82 (2009)

    Article  Google Scholar 

  2. Buer, S.V., Strandhagen, J.O., Chan, F.T.S.: The link between industry 4.0 and lean manufacturing: mapping current research and establishing a research agenda. Int. J. Prod. Res. 56(8), 2924–2940 (2018)

    Google Scholar 

  3. Corallo, A., Lazoi, M., Lezzi, M.: Cybersecurity in the context of industry 4.0: a structured classification of critical assets and business impacts. Comput. Ind. 114, 103165 (2020)

    Google Scholar 

  4. Ghadge, A., Er Kara, M., Moradlou, H., Goswami, M.: The impact of Industry 4.0 implementation on supply chains. J. Manuf. Technol. Manage. 31(4), 669–686 (2020)

    Google Scholar 

  5. Hopkins, J.L.: An investigation into emerging industry 4.0 technologies as drivers of supply chain innovation in Australia. Comput. Ind. 125, 103323 (2021)

    Google Scholar 

  6. Jurczyk-Bunkowska, M.: Tactical manufacturing capacity planning based on discrete event simulation and throughput accounting: a case study of medium sized production enterprise. Adv. Prod. Eng. Manage. 16(3), 335–347 (2021)

    Google Scholar 

  7. Lachvajderová, L., Kádárová, J.: Industry 4.0 implementation and industry 5.0 readiness in industrial enterprises. Manage. Prod. Eng. Rev. 13(3), 102–109 (2022)

    Google Scholar 

  8. Lasi, H., Fettke, P., Kemper, H.G., Feld, T., Homann, M.: Industry 4.0. Bus. Inform. Syst. Eng. 6, 239–242 (2014)

    Google Scholar 

  9. Lewandowska-Ciszek, A.: Identifying the phenomenon of complexity in the sector of industrial automation. Manage. Prod. Eng. Rev. 13(2), 3–14 (2022)

    Google Scholar 

  10. Mayr, A., et al.: Lean 4.0 - a conceptual conjunction of lean management and industry 4.0. In: 51st CIRP Conference on Manufacturing Systems, pp. 622–628. Procedia CIRP (2018)

    Google Scholar 

  11. Olender-Skóra, M., Banaś, W., Gwiazda, A.: Possibilities of industrial utilization of FFF/FDM process for chosen element printing. Int. J. Mod. Manuf. Technol. 9(2), 1–6 (2017)

    Google Scholar 

  12. Olender-Skóra, M., Banaś, W.: Application of a digital twin for manufacturing process simulation. In: 15th Global Congress on Manufacturing and Management (GCMM-2020), pp. 1–6, Elsevier Ltd (2019)

    Google Scholar 

  13. Pacholski, L.: Managerial recommendations concerning the cybersecurity of information and knowledge resources in production enterprises implementing the industry 4.0 concept. Manage. Prod. Eng. Rev. 13(3), 30–38 (2022)

    Google Scholar 

  14. Rojek, I., Mikołajewski, D., Kotlarz, P., Macko, M., Kopowski, J.: Intelligent system supporting technological process planning for machining and 3D printing. Bull. Polish Acad. Sci. Tech. Sci. 69(2), 1–8 (2021)

    Google Scholar 

  15. Hryniewicz, P., Banas, W., Foit, K., et al.: Modelling cooperation of industrial robots as multi-agent systems. IOP Conf. Ser. Mater. Sci. Eng. 227, 1–7 (2017)

    Article  Google Scholar 

  16. Krenczyk, D., Skołud, B., Olender, M.: The production route selection algorithm in virtual manufacturing networks. IOP Conf. Ser. Mater. Sci. Eng. 227, 1–9 (2017)

    Article  Google Scholar 

  17. Tonelli, F., Demartini, M., Pacella, M., Lala, R.: Cyber-physical systems (CPS) in supply chain management: from foundations to practical implementation. In: Procedia CIRP, pp. 598–603 (2021)

    Google Scholar 

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Correspondence to Małgorzata Olender-Skóra .

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Olender-Skóra, M., Gwiazda, A. (2023). Possibilities of Decision Support in Organizing Production Processes. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 750. Springer, Cham. https://doi.org/10.1007/978-3-031-42536-3_9

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