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A systems dynamics simulation model of a steel supply chain-case study

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  • Mohammadi, Mir Ahmad
  • Sayadi, Ahmad Reza
  • Khoshfarman, Mahsa
  • Husseinzadeh Kashan, Ali

Abstract

Considering the complexity, interactions, and dynamics that permeate the Supply Chain (SC), computational modeling and simulation promote determining the system's behavior and decision-making. Among simulation techniques, system dynamics (SD) investigate on recognition of variables and their dynamic behavior trend throughout time in the SC. This paper presents a comprehensive SD model related to upstream steel SC management up to four echelons: concentrate, pellet, sponge iron, and steel. The model noticed Causal-Loops Diagrams (CLD) and Stock-Flow Diagrams (SFD) and provided a simulation framework. The robustness of the proposed model was evaluated by implementing the defined model in a multi-echelon steel complex in Iran. Various scenarios were analyzed applying stochastic simulation to include selected random variables. Iron ore grade and tonnage play the most critical role in the network’s performance. With an approximately 4% increase in the iron ore grade, steel production costs decreased 2.4%. The influence of the simultaneous uncertainty in iron ore grade and iron ore supply in the range of extreme levels of actual historical data resulted in an increase and decrease of +14% and −32% on the total steel production costs, respectively. Furthermore, removing energy subsidies and increasing five times in price results in a rise in total expenses up to 60% and a fall in the marginal profit up to 48%.

Suggested Citation

  • Mohammadi, Mir Ahmad & Sayadi, Ahmad Reza & Khoshfarman, Mahsa & Husseinzadeh Kashan, Ali, 2022. "A systems dynamics simulation model of a steel supply chain-case study," Resources Policy, Elsevier, vol. 77(C).
    • Handle: RePEc:eee:jrpoli:v:77:y:2022:i:c:s0301420722001386
    DOI: 10.1016/j.resourpol.2022.102690
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    1. Dong, Ruoyu & Fu, Cong & Liu, Yan & Xu, Yanqing & Li, Kenan, 2025. "Modeling China's carbon emission reduction trajectory and peak path using a system dynamic approach," Ecological Modelling, Elsevier, vol. 505(C).
    2. Shen, Jialin & Zhang, Qi & Tian, Shuoshuo, 2025. "Decarbonization pathways analysis and recommendations in the green steel supply chain of a typical steel end user-automotive industry," Applied Energy, Elsevier, vol. 377(PD).
    3. Sun, Chen & Song, Junnian & Zhang, Dongqi & Wang, Xiaofan & Yang, Wei & Qi, Zhimin & Chen, Shaoqing, 2023. "Tracing urban carbon footprints differentiating supply chain complexity: A metropolis case," Energy, Elsevier, vol. 282(C).
    4. Jiang, Hong-Dian & Liu, Yan-xin & Wang, Hanxu & Li, Huajiao & Jiang, Yutong, 2024. "An economy-wide and environmental assessment of an imported supply shortage for iron ore: The case of China," Economic Analysis and Policy, Elsevier, vol. 83(C), pages 606-617.

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