Computer simulations in planning production processes as a tool of decision making

• Autorzy: Pawlak Szymon

Identyfikatory

DOI

10.30657/pea.2025.31.18

• Języki publikacji: EN

Abstrakty

EN

Production planning in terms of defining the number of resources necessary to execute the production plan in a precisely defined time is a complex task. The complexity of the planning process results from the large number of variables affecting the production process. Planning the production process in such a way as to shorten its duration, reduce costs and at the same time ensure the appropriate quality of the product determines the competitiveness of a given company on the market, which is a key factor today. The aim of the article was to present the justification for using simulation software as a tool supporting decision-making in the field of selecting the appropriate number of production resources necessary to implement the assumed production plan. The subject of the research was the actual production process (case study). For the purposes of the research, a five-stage methodology was developed, the effect of which was to conduct a computer simulation of the production process. The obtained results allowed to indicate the best of the analyzed solutions, ensuring reduction, among others, of the duration of the production process and the costs generated by it. The analysis confirmed the justification for using computer simulation in planning production processes as a tool supporting decision-making.

Słowa kluczowe

EN

computer simulation; production process; FlexSim; production process planning

PL

symulacja komputerowa; proces produkcyjny; FlexSim; planowanie procesu produkcyjnego

• Wydawca: Oficyna Wydawnicza Stowarzyszenia Menedżerów Jakości i Produkcji
• Czasopismo: Production Engineering Archives
• Rocznik: 2025
• Tom: Vol. 31, Iss. 2
• Strony: 183--189
• Opis fizyczny: Bibliogr. 23 poz., rys., tab.

Twórcy

autor

Pawlak Szymon

• Silesia University of Technology, 40-019 Katowice, Krasińskiego st 8, Poland

Bibliografia

• 1.Barosz, P., Gołda, G., Kampa, A., 2020. Efficiency Analysis of Manufacturing Line with Industrial Robots and Human Operators. Applied Sciences, 10(8), 2862, DOI: 10.3390/ app10082862.
• 2.Banks, J., Carson, J., 2013. Discrete-Event System Simulation; Pearson New International Edition; Pearson Education India: Noida, India.
• 3.Bieńkowski, M., 2018. Innowacyjne rozwiązania dla Przemysłu 4.0. Automatyka, 26-34, DOI: 10.29119/1641-3466.2022.160.33.
• 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.2022.28.38
• 5.Dubaj, K., 2023. Practical application of flexsim in simulation modeling of production systems. Zeszyty Naukowe Akademii Górnośląskiej, 6, 23 - 31, DOI: 10.53259/2023.6.03.
• 6.Ferro, R., Cordeiro, G.A., Ordóñez, R.E.C., Beydoun, G., Shukla, N., 2021. An Optimization Tool for Production Planning: A Case Study in a Textile Industry. Applied Sciences, 11(18), 8312. DOI: 10.3390/app11188312
• 7.Gao, K., Huang, Y., Sadollah, A., 2020. A review of energy-efficient scheduling in intelligent production systems. Complex Intell. Syst. 6, 237-249, DOI: 10.1007/s40747-019-00122-6.
• 8.Medan, N., Ravai-nagy, S., 2020. Modelling, simulating and analysing a process flow for a machining part using flexsim software, Volume 65, Issue 4, 1229-1234.
• 9.Jamwal, A., Agrawal, R., Sharma, M., Giallanza, A., 2021 Industry 4.0 Technologies for Manufacturing Sustainability: A Systematic Review and Future Research Directions. Appl. Sci. 11, 5725, DOI: 10.3390/app11125725
• 10.Jasiulewicz - Kaczmarek, M., Gola, A., 2019. Maintenance 4.0 Technologies for Sustainable Manufacturing - an Overview, Volume 52, Issue 10, 91- 96, DOI: 10.1016/j.ifacol.2019.10.005.
• 11.Kampa, A., 2023. Modeling and Simulation of a Digital Twin of a Production System for Industry 4.0 with Work-in-Process Synchronization. Applied Sciences. 2023; 13(22):12261. DOI: 10.3390/app132212261.
• 12.Kikolski, M., 2017. Study of Production Scenarios with the Use of Simulation Models. Procedia Engineering, 182, 321-328, DOI: 10.1016/j.proeng.2017.03.102.
• 13.Kochańska, J., Burduk, A., 2023. A Method of Assessing the Effectiveness of the Use of Available Resources When Implementing Production Processes. Applied Sciences, 13(13), 7764, DOI: 10.3390/app13137764.
• 14.Luściński, S., Ivanov, V., 2020. A Simulation Study of Industry 4.0 Factories Based on the Ontology on Flexibility with Using FlexSim Software. Management and Production Engineering Review, 11, 74-83, DOI: 10.24425/mper.2020.134934.
• 15.López de Lacalle, L.N., Posada, J., 2022. New Industry 4.0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes: Volume II, Applied Sciences 12, no. 15: 7952, DOI: 10.3390/app12157952
• 16.Pawlak, S., Małysa, T., Fornalczyk, A., 2024. Analysis of Production Process Parameters Using Computer Simulation at the Planning Stage. Applied Sciences, 14(23), 10863. DOI: 10.3390/app142310863
• 17.Pech, M., Vrchota, J., 2020. Classification of Small- and Medium-Sized Enterprises Based on the Level of Industry 4.0 Implementation" Applied Sciences, 10(15), 5150. DOI: 10.3390/app10155150.
• 18.Rashid, A., Tjahjono, B., 2016. Achieving manufacturing excellence through the integration of enterprise systems and simulation, Production Planning & Control, 27(10), 837-852, DOI: 10.1080/09537287.2016.1143132.
• 19.Richnák, P., 2022. Current Trend of Industry 4.0 in Logistics and Transformation of Logistics Processes Using Digital Technologies:An Empirical Study in the Slovak Republi, Logistics, 6(4), 79, DOI: 10.3390/logistics6040079.
• 20.Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P., Harnisch, M., 2015. Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries. Boston Consulting Group, 9, 54-89.
• 21.Rüttimann, B., i Stöckli, M., 2020. From Batch & Queue to Industry 4.0— Type Manufacturing Systems: A Taxonomy of Alternative Production Models. JSSM, 13, 299-316, DOI: 10.4236/jssm.2020.132019.
• 22.Zhong, R.Y., Xu, X., Klotz, E., 2017. Intelligent Manufacturing in the Context of Industry 4.0: A Review. Engineering, 3(5), 616-630. DOI: 10.1016/J.ENG.2017.05.015.
• 23.Sujová, E., Vysloužilová, D., Koleda, P., Gajdzik, B., 2023. Research on the Evaluation of the Efficiency of Production Processes Through the Implementation of Key Performance Indicators. Management Systems in Production Engineering, 31(4), 404-410, DOI:10.2478/mspe-2023-0045.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).