Inventory Analysis of Packed Red Cells Components Using Monte Carlo Simulation
DOI:
https://doi.org/10.30656/intech.v11i1.10388Keywords:
Blood Inventory Management, Blood Supply, Monte Carlo, Packed Red Cells, SimulationAbstract
This study analyzes Packed Red Cells (PRC) inventory management at PMI Banyumas using a Monte Carlo simulation to evaluate different stock management strategies. The initial model shows a significant shortage of blood supply. Two alternative scenarios were simulated to address this issue: adding 55 additional units from external sources and increasing donor participation by 15%. The simulation results demonstrate that these strategies effectively reduce the shortage from 62 units to just 5 units without increasing expired inventory while achieving the lowest total cost of Rp. 9,927,682. These findings highlight that increasing donor participation offers the best performance in balancing supply and demand. This study provides simulation-based strategic recommendations that other PMI branches can replicate to improve bloodstock management, reduce shortages, and maintain optimal service levels.
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Abidovna, A. S. (2023). Monte Carlo Modeling and Its Peculiarities in the Implementation of Marketing Analysis in the Activities of the Enterprise. Gospodarka i Innowacje., 42, 375–380. https://www.gospodarkainnowacje.pl/index.php/issue_view_32/article/view/2091
Ahmadimanesh, monireh, Pooya, A., Safabakhsh, H., & Sadeghi, S. (2022). Designing an Optimal Model of Blood Logistics Management with the Possibility of Return in the Three-Level Blood Transfusion Network. https://doi.org/10.21203/rs.3.rs-1187827/v1
Ahmadimanesh, M., Safabakhsh, H. R., & Sadeghi, S. (2023). Designing an optimal model of blood logistics management with the possibility of return in the three-level blood transfusion network. BMC Health Services Research, 23(1), 1304. https://doi.org/10.1186/s12913-023-10240-0
Akerina, L. J. C., & Adi, T. W. (2023). Simulasi Perhitungan Kebutuhan Refueller Dengan Menggunakan Software Arena di PT.XYZ. JJurnal Terapaan Logistik Migas, 2(1), 151. https://jtlm.akamigas.ac.id/index.php/jtlmig/article/view/40
Al-Farisy, A. F. (2023). Anaemia In Pregnancy: The Impact on Maternal and Fetal Health, Innovation, and Management in Kedungjati Primary Health Care. Jurnal Medika Malahayati, 7(1), 516–525. https://doi.org/10.33024/jmm.v7i1.9626
Anchinmane, V. T., & Sankhe, S. V. (2022). Analysis of Rate and Reasons of Discarding of Blood and its Components in Tertiary Care Hospital Blood Bank. International Journal of Pharmaceutical Sciences Review and Research, 113–115. https://doi.org/10.47583/ijpsrr.2022.v75i01.020
Awandani, H. (2022). Determination Strategy for Controlling Bloodstock Components Packed Red Cell Using Monte Carlo Simulation. Jurnal Sistem Teknik Industri, 24(1), 137–146. https://doi.org/10.32734/jsti.v24i1.7746
Darnis, R., Nurcahyo, G. W., & Yunus, Y. (2020). Simulasi Monte Carlo untuk Memprediksi Persediaan Darah. Jurnal Informasi Dan Teknologi, 2(4), 139–144. https://doi.org/10.37034/jidt.v2i4.98
Direktorat Jenderal Kependudukan dan Pencatatan Sipil. (2021). Dirjen Dukcapil: Indonesia miliki bank data 379 juta golongan darah. https://dukcapil.kalbarprov.go.id/post/dirjen-dukcapil-indonesia-miliki-bank-data-37-9-juta-golongan-darah
Efendi, G., & Zahmi, A. (2023). Optimalisasi Persediaan Darah Dengan Metoda Monte Carlo (Studi Kasus UTD PMI Solok). Journal of Operation System, 1(2), 99–113. https://www.ejournal.ybpindo.or.id/index.php/jos/article/view/35
Harahap, W. A. (2024). Implementasi Metode Monte Carlo Dalam Melakukan Prediksi Populasi Jumlah Hewan Ternak Babi Di Nusa Tenggara Timur. JATI (Jurnal Mahasiswa Teknik Informatika), 8(4), 4478–4481. https://doi.org/10.36040/jati.v8i4.9966
Hasanah, W. P. (2024). Manajemen Persediaan Darah Komponen Packed Red Cell (PRC) Menggunakan Simulasi Monte Carlo. Action Research Literate, 8(7), 1–15. https://doi.org/10.46799/arl.v8i7.447
Imamoglu, G., Topcu, Y. I., & Aydin, N. (2023). A Systematic Literature Review of the Blood Supply Chain through Bibliometric Analysis and Taxonomy. Systems, 11(3), 124. https://doi.org/10.3390/systems11030124
Jin, X., Tang, H., & Huang, Y. (2021). Dynamic Stochastic Optimization of Emergent Blood Collection and Distribution from Supply Chain Perspective. Complexity, 2021(1). https://doi.org/10.1155/2021/5532672
Maegele, M., Lier, H., & Hossfeld, B. (2023). Pre-hospital blood products for the care of bleeding trauma patients. Deutsches Ärzteblatt International, 120(4), 670–676. https://doi.org/10.3238/arztebl.m2023.0176
Maitra, S. (2024). Inventory Management Under Stochastic Demand: A Simulation-Optimization Approach. https://doi.org/10.48550/arXiv.2406.19425
Maulana, R. (2024). Aplikasi Metode Monte Carlo Dalam Analisis Prediksi Impor Beras Dari Pakistan. JATI (Jurnal Mahasiswa Teknik Informatika), 8(4), 6021–6027. https://doi.org/10.36040/jati.v8i4.10139
Navarra, A. (2021). The role of systematic errors. National Science Review, 8(10). https://doi.org/10.1093/nsr/nwab082
Putri, V. A., & Sitepu, S. (2024). Perbandingan Akurasi Metode Fuzzy Mamdani dan Fuzzy Sugeno dalam Memprediksi Kebutuhan Darah PMI Kota Medan. FARABI: Jurnal Matematika Dan Pendidikan Matematika, 7(2), 152–162. https://doi.org/10.47662/farabi.v7i2.757
Sadeghi, M. (2022). Model-Based Decision Making in Life Sciences [Northeastern University]. https://www.proquest.com/openview/fde4df90884f063c463696f8cc80e248/1?pq-origsite=gscholar&cbl=18750&diss=y
Saviano, A., Perotti, C., Zanza, C., Longhitano, Y., Ojetti, V., Franceschi, F., Bellou, A., Piccioni, A., Jannelli, E., Ceresa, I. F., & Savioli, G. (2024). Blood Transfusion for Major Trauma in Emergency Department. Diagnostics, 14(7), 708. https://doi.org/10.3390/diagnostics14070708
Sumbogo, D. W. (2021). Kualitas (Tingkat Hematokrit) dan Potensi Hemolisis dari Packed Red Cell (PRC) Selama Proses Suplai Darah di Unit Transfusi Darah Palang Merah Indonesia Kota Jakarta Utara Tahun 2020. Jurnal Genta Kebidanan, 11(1), 12–22. https://doi.org/10.36049/jgk.v11i1.36
Torrado, A., & Barbosa-Póvoa, A. (2022). Towards an Optimized and Sustainable Blood Supply Chain Network under Uncertainty: A Literature Review. Cleaner Logistics and Supply Chain, 3, 100028. https://doi.org/10.1016/j.clscn.2022.100028
Velikova, T., Mileva, N., & Naseva, E. (2024). Method “Monte Carlo” in healthcare. World Journal of Methodology, 14(3), 2. https://doi.org/10.5662/wjm.v14.i3.93930
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