System Dynamics Modeling for Safety Stock Optimization Under Lead Time Uncertainty in Fertilizer Distribution: A Case Study in South Sulawesi
DOI:
https://doi.org/10.59890/ijgsr.v4i5.214Keywords:
System Dynamics, Safety Stock; Reorder Point, Lead Time Uncertainty, Fertilizer DistributionAbstract
This study develops a System Dynamics model to evaluate safety stock and reorder point policies under lead time uncertainty in fertilizer distribution in Sulawesi, Indonesia. The model integrates statistical inventory control with dynamic simulation using Vensim to capture the effects of vessel delays, port congestion, and extreme weather disruptions. Three service-level scenarios were tested using different Z-factors: 2.33 (99%), 1.645 (95%), and 1.28 (90%). Results show that safety stock increases significantly across scenarios (approximately +28% and +42%), while reorder point values remain relatively stable (below 3% variation), indicating demand dominance. Holding cost increases modestly, ranging from 0.98% to 2.93% across scenarios. The balanced policy provides the most feasible trade-off between service reliability and inventory cost
References
Aldito Hermawan, & Siti Muhimatul Khoiroh. (2023). Penerapan Metode Material Requirement Planning (MRP) guna Merencanakan Kebutuhan Bahan Baku (Studi Kasus: CV. AM Nanda Putra Sidoarjo). Jurnal Kendali Teknik Dan Sains, 1(3), 122–136. https://doi.org/10.59581/jkts-widyakarya.v1i3.642
Amoozad Mahdiraji, H., Yaftiyan, F., Garza-Reyes, J. A., Razavi Hajiagha, S. H., & Kazancoglu, Y. (2024). Decarbonised closed-loop supply chains resilience: examining the impact of COVID-19 toward risk mitigation by a fuzzy multi-layer decision-making framework. Annals of Operations Research. https://doi.org/10.1007/s10479-024-06093-3
Belhadi, A., Mani, V., & Kamble, S. S. (2024). Artificial intelligence ‑ driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism : an empirical investigation. Annals of Operations Research, 333(2), 533–557. https://doi.org/10.1007/s10479-021-03956-x
Chen, H. Y., Das, A., & Ivanov, D. (2022). Building Resilience and Managing Post-Disruption Supply Chain Recovery : Lessons from the Information and Communication Technology Industry. 1–32.
Dai, T., & Tang, C. (2022). Frontiers in service science: integrating ESG measures and supply chain management: research opportunities in the postpandemic era. Service Science, 14(1), 1–12.
Huaraz, A., & Andrade-arenas, L. (2021). Inventory Management Analysis under the System Dynamics Model. 12(1), 649–653.
Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing disruptions. Int. J. Prod. Res., 59(14), 4180–4195.
Khakdaman, M., Dullaert, W., Inghels, D., van Keeken, M., & Wissink, P. (2024). A system dynamics supply chain analysis for the sustainability transition of european rolled aluminum products. Sustainability, 16(20), 8892.
Kırmızı, S. D., & Ceylan, Z. (2024). Enhancing Inventory Management through Safety-Stock. 1–17.
Kumar, R., Singh, S. P., & Lamba, K. (2018). Sustainable robust layout using Big Data approach: A key towards industry 4.0. Journal of Cleaner Production, 204(September), 643–659. https://doi.org/10.1016/j.jclepro.2018.08.327
Kurniawan, N., & Suseno, S. (2023). Optimasi Sistem Penjadwalan Produksi Dengan Metode Nawaz Enscore Ham (NEH) Pada PT Sinar Semesta. Jurnal Inovasi Dan Kreativitas (JIKa), 3(1), 24–33. https://doi.org/10.30656/jika.v3i1.6001
Liu, J., Wan, L., Wang, W., Yang, G., Ma, Q., Zhou, H., Zhao, H., & Lu, F. (2023). Integrated fuzzy DEMATEL-ISM-NK for metro operation safety risk factor analysis and multi-factor risk coupling study. Sustainability, 15(7), 5898.
Mehregan, E. (2022). Supply chain modeling with system dynamics approach (Case study of Firooz Health Products Company). International Journal of Early Childhood Special Education, 14(4).
Model, C. (2022). Consumption Model. 1–12.
Nambiar, M., Simchi-levi, D., & Wang, H. (2019). Dynamic Inventory Allocation with Demand Learning for Seasonal Goods. 1–28.
Nazari-Ghanbarloo, V. (2022). A dynamic performance measurement system for supply chain management. International Journal of Productivity and Performance Management, 71(2), 576–597.
Park, J. J. (2021). Models for supply chain management esi6323. 1–5.
Pehlivan, E., Palalı, İ., Atan, S. G., Turan, D., Çınarka, H., & Çetinkaya, E. (2022). The effectiveness of POST-DISCHARGE telerehabilitation practices in COVID-19 patients: Tele-COVID study-randomized controlled trial. Annals of Thoracic Medicine, 17(2), 110–117.
Popov, P. V. (2024). Logistics Infrastructure as a Driver of Social and Economic Development of the Region. Vestnik Volgogradskogo Gosudarstvennogo Universiteta. Ekonomika, 89–96.
Zhang, H., Lv, Y., Zhang, S., & Liu, Y. D. (2024). Digital supply chain management: a review and bibliometric analysis. Journal of Global Information Management (JGIM), 32(1), 1–20.
Zhao, L., Jin, S., & Gao, P. (2024). Dynamics analysis of green supply chain under the conditions of demand uncertainty and blockchain technology. 1–19.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Fahri Anwar, Nurlaela, Andi Muadz Palerangi, Achmad Romadin
This work is licensed under a Creative Commons Attribution 4.0 International License.
