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Optimization of manufacturing enterprise collaboration with subcontractors through production process simulation

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Języki publikacji
EN
Abstrakty
EN
This paper presents a simulation-based model aimed at optimizing the outsourcing of production tasks to external subcontractors, focusing on minimizing costs and production times. The study explores a manufacturing scenario where a company utilizes both in-house and subcontractor resources for machining metal components. A simulation model was developed in FlexSim, employing advanced tools such as the OptQuest optimizer to analyze various scenarios of production allocation. Parameters like production cost, machine utilization, and task completion times were modelled and evaluated. The results showcase the effectiveness of simulation models in identifying optimal production strategies, balancing cost-efficiency with operational flexibility. The findings emphasize the utility of such tools for decision-making in production management, providing insights into resource allocation and performance improvement.
Wydawca
Rocznik
Strony
129--137
Opis fizyczny
Bibliogr. 21 poz., rys., tab.
Twórcy
autor
  • Czestochowa University of Technology, Poland
Bibliografia
  • 1.Beaverstock, M., Greenwood, A., Lavery, E., Nordgren, W., 2011. Applied simulation: modeling and analysis using FlexSim, BookBaby.
  • 2.Castane, G. G., Simonis, H., Brown, K. N., Lin, Y., Ozturk, C., Garraffa, M., Antunes, M. (Eds.), 2019. Simulation-Based Optimization Tool for Field Service Planning.
  • 3.Dadi, V., Nikhil, S. R., Mor, R. S., Agarwal, T., Arora, S., 2021. Agri-Food 4.0 and Innovations: Revamping the Supply Chain Operations, Production Engineering Archives, 27(2), 75-89. DOI: 10.30657/pea.2021.27.10
  • 4.Daroń, M., 2022. Simulations in planning logistics processes as a tool of decision-making in manufacturing companies, Production Engineering Archives, 28(4), 300-308. DOI: 10.30657/pea.2022.28.
  • 5.Dziuba, ST, Szołtysek, K, Kozyra, C., 2011. Application of FAM-Fail Assessment Method-to optimization of unit costs of producing flours for special purposes, open.icm.edu.pl
  • 6.Ingaldi, M., 2022. E-service quality assessment according to hierarchical service quality models, Management Systems in Production Engineering, 30(4), 311-318, DOI: 10.2478/mspe-2022-0040
  • 7.Ingaldi, M.; Ulewicz, R. The Business Model of a Circular Economy in the Innovation and Improvement of Metal Processing. Sustainability 2024, 16, 5513. DOI: 10.3390/su16135513
  • 8.Jemala, M., 2024, Recognizing Key Macro-factors of Technological Innovation Based on Leading Technology Companies’ Research. Production Engineering Archives, 30(4), 413-430. DOI: 10.30657/pea.2024.30.40
  • 9.Knop, K., 2021. Analysing the machines working time utilization for improvement purposes, Production Engineering Archives, 27(18), 137-147, DOI: 10.30657/pea.2021.27.18
  • 10.Krynke M. 2023a. Optimizing supply chain in a foundry through computer simulation using flexsim - a case study, System Safety: Human - Technical Facility - Environment (red.) Ulewicz Robert, Nikolic Ruzica R., Klimecka-Tatar Dorota, 172-181, De Gruyter.
  • 11.Krynke M., 2023b. The use of computer simulation in the management of subcontractors and outsourced services, Materials Research Proceedings, 34, 334-343, 2023. DOI: 10.21741/9781644902691-39
  • 12.Krynke, M., 2021. Management optimizing the costs and duration time of the process in the production system, Production Engineering Archives, 27(3), 163-170. DOI: 10.30657/pea.2021.27.21
  • 13.Krynke, M., Klimecka-Tatar, D., 2022. Production costs management in process supported by external entities-Process flow optimization, AIP Conference Proceedings, pubs.aip.org, DOI: 10.1063/5.0100420
  • 14.Laguna, M., 2011. OptQuest: Optimization of Complex Systems, Opttek Systems, Inc. https://www.opttek.com/sites/default/files/pdfs/optquest-optimization%20of% 20complex%20systems.pdf
  • 15.Nowakowska-Grunt, J, Mazur, M., 2015. Safety management in logistic processes of the metallurgical industry, Proceedings of METAL
  • 16.Pawlewski, P., Anholcer, M., 2019. Using CSP Solvers as Alternative to Simulation Optimization Engines, In P. Pawlewski, P. Hoffa-Dabrowska, P. Golinska-Dawson, & K. WernerLewandowska (Eds.), EcoProduction. Environmental issues in logistics and manufacturing. FlexSim in academe: Teaching and research, 131-143. Springer. DOI: 10.1007/978-3-030- 04519-7_10
  • 17.Poloczek, R., Oleksiak, B., 2024. Evaluation of production line expansion efficiency using computer simulation. Production Engineering Archives, 30(4), 520-527. https://doi.org/10.30657/pea.2024.30.48
  • 18.Qawaqzeh, M. Z., Miroshnyk, O., Shchur, T., Kasner, R., Idzikowski, A., Kruszelnicka, W., Tomporowski, A., Bałdowska-Witos, P., Flizikowski, J., Zawada, M., Doerffer, K., 2021. Research of Emergency Modes of Wind Power Plants Using Computer Simulation, Energies, DOI: 10.3390/en14164780
  • 19.Saragih, J., Tarigan, A., Pratama, I., Wardati, J., Silalahi, E. F., 2020. The impact of total quality management, supply chain management practices and operations capability on firm performance, Polish Journal of Management Studies, 21(2), 384-397. DOI: 10.17512/pjms.2020.21.2.27
  • 20.Siwiec, D., Pacana, A., Ulewicz, R., 2022. Concept of a model to predict the qualitative-cost level considering customers’ expectations, Polish Journal of Management Studies, 26(2), 330-340. DOI: 10.17512/pjms.2022.26.2.20
  • 21.Ulewicz, R., Mazur, R. 2019. Economic aspects of robotization of production processes by example of a car semi-trailers manufacturer, Manufacturing Technology 19(6), 1054 - 1059, DOI: 10.21062/ujep/417.2019/a/1213-2489/MT/19/6/1054
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-18e9f940-abcb-48e4-8b81-0f052d3b730d
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