An Analysis of Supply Chain Macro-Strategies in the Context of Industry 4.0 with a System Dynamics Approach (The Case of: Iran's Steel Supply Chain)

Document Type : Research Article

Authors

• Sima Motallebi
• Mostafa Zandieh
• Akbar Alem Tabriz

Department of Industrial Management and Information Technology, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran.

10.22067/jstinp.2024.89351.1115

Abstract

Forward-thinking decisions and adopting cutting-edge technologies typically influence the management of large, multi-level supply chains. Given the various raw materials, semi-finished products, and final goods in these multi-level supply chains, balancing imports and exports is one of the most significant challenges and macro-level issues facing countries. System dynamics simulation is a powerful tool for analyzing macro-level issues, as it can predict future system behavior based on current conditions. In this research, Iran's steel supply chain was selected as a case study to assess future trends at the macro level under the influence of Industry 4.0. Industry 4.0 encompasses a range of innovative technologies that can improve supply chain efficiency. For this purpose, AnyLogic software was used to simulate the model. According to the system dynamics simulation results, keeping a balance between supply and demand at each stage of the supply chain plays a crucial role in increasing efficiency and profitability. Additionally, macro-level policies such as budget allocation, export rate, and support for investments in various parts of the supply chain directly impact this chain's performance. Sensitivity analysis revealed that increasing the budget and production capacity in the direct reduced iron (DRI) and crude steel sectors has a greater impact on the overall profitability of the chain, and exporting crude steel can be a significant way to increase revenue and foreign exchange earnings for the country.

Keywords

• System dynamics simulation
• Multi-level supply chain
• Supply chain macro-level
• Industry 4.0
• Iran's steel supply chain

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References

Alamerew, Y.A. and Brissaud, D., 2020. Modelling reverse supply chain through system dynamics for realizing the transition towards the circular economy: A case study on electric vehicle batteries. Journal of Cleaner Production, 254, p.120025. https://doi.org/10.1016/j.jclepro.2020.120025.

Elyasi, A. and Teimoury, E., 2023. Applying Critical Systems Practice meta-methodology to improve sustainability in the rice supply chain of Iran. Sustainable Production and Consumption, 35, pp.453-468. https://doi.org/10.1016/j.spc.2022.11.024.

Gao, M. and Ma, D., 2020, December. Measurement and suppression of supply chain bullwhip effect under service transformation: based on system dynamics simulation. In 2020 IEEE 6th International Conference on Computer and Communications (ICCC) (pp. 2334-2339). IEEE. https://doi.org/10.1109/ICCC51575.2020.9345148.

Gereffi, G., Lim, H.C. and Lee, J., 2021. Trade policies, firm strategies, and adaptive reconfigurations of global value chains. Journal of International Business Policy, 4(4), p.506. https://doi.org/10.1057/s42214-021-00102-z.

Ghadge, A., Er Kara, M., Moradlou, H. and Goswami, M., 2020. The impact of Industry 4.0 implementation on supply chains. Journal of Manufacturing Technology Management, 31(4), pp.669-686. https://doi.org/10.1108/JMTM-10-2019-0368.

Golgeci, I., Bouguerra, A. and Rofcanin, Y., 2020. The human impact on the emergence of firm supply chain agility: A multilevel framework. Personnel Review, 49(3), pp.733-754.  https://doi.org/10.1108/PR-12-2018-0507.

Kache, F. and Seuring, S., 2017. Challenges and opportunities of digital information at the intersection of Big Data Analytics and supply chain management. International journal of operations & production management, 37(1), pp.10-36.  https://doi.org/10.1108/IJOPM-02-2015-0078.

Luthra, S S. and Mangla, S.K., 2018. Evaluating challenges to Industry 4.0 initiatives for supply chain sustainability in emerging economies. Process safety and environmental protection, 117, pp.168-179. https://doi.org/10.1016/j.psep.2018.04.018.

Mangla, S.K., Kazancoglu, Y., Ekinci, E., Liu, M., Özbiltekin, M. and Sezer, M.D., 2021. Using system dynamics to analyze the societal impacts of blockchain technology in milk supply chainsrefer. Transportation Research Part E: Logistics and Transportation Review, 149, p.102289. https://doi.org/10.1016/j.tre.2021.102289.

Mashayekhi, A.N. and Ghili, S., 2010, March. System dynamics problem definition as an evolutionary process using ambiguity concept. In Proceedings of the International System Dynamics Conference (Vol. 1157)..

Mohammadi, M.A., Sayadi, A.R., Khoshfarman, M. and Kashan, A.H., 2022. A systems dynamics simulation model of a steel supply chain-case study. Resources Policy, 77, p.102690. https://doi.org/10.1016/j.resourpol.2022.102690.

Nuñez Rodriguez, J., Andrade Sosa, H.H., Villarreal Archila, S.M. and Ortiz, A., 2021. System dynamics modeling in additive manufacturing supply chain management. Processes, 9(6), p.982. https://doi.org/10.3390/pr9060982.

Olivares-Aguila, J. and ElMaraghy, W., 2021. System dynamics modelling for supply chain disruptions. International Journal of Production Research, 59(6), pp.1757-1775.   https://doi.org/10.1080/00207543.2020.1725171.

Ozbayrak, M., Papadopoulou, T.C. and Akgun, M., 2007. Systems dynamics modelling of a manufacturing supply chain system. Simulation Modelling Practice and Theory, 15(10), pp.1338-1355. https://doi.org/10.1016/j.simpat.2007.09.007.

Rebs, T., Brandenburg, M. and Seuring, S., 2019. System dynamics modeling for sustainable supply chain management: A literature review and systems thinking approach. Journal of cleaner production, 208, pp.1265-1280. https://doi.org/10.1016/j.jclepro.2018.10.100.

Steel Statistical Yearbook., 2022. Publication worldsteel.org.  Available at: https://worldsteel.org/publications/bookshop/ssy_subscription-2024/.

Sterman, J., 2000. Instructor's manual to accompany business dyanmics: systems thinking and modeling for a complex world. McGraw-Hill.

Tahami, H. and Fakhravar, H., 2020. Multilevel Reorder Strategy-based Supply Chain Model. In 5th North American Conference on Industrial Engineering and Operations Management (IEOM), Michigan, USA.

Waltz, C.F. and Bausell, B.R., 1981. Nursing research: design statistics and computer analysis. Davis Fa. https://dl.acm.org/doi/abs/10.5555/578318.

Wang, J., Qiu, S., Liang, T. and Du, X., 2021. Research on combat simulation system based on multi-agent. In Journal of Physics: Conference Series (Vol. 1748, No. 3, p. 032031). IOP Publishing. https://doi.org/10.1088/1742-6596/1748/3/032031.

Zhu, J., Zhang, J. and Feng, Y., 2022. Hard budget constraints and artificial intelligence technology. Technological Forecasting and Social Change, 183, p.121889. https://doi.org/10.1016/j.techfore.2022.121889.