Minimizing Packed Red Cell Shortages in the Blood Supply Chain: A Discrete Simulation and Taguchi-based Approach
Subject Areas : Inventory control
Agus Mansur
1
(Universitas Islam Indonesia)
Keywords: Blood supply chain, Shortages, Discrete Event Simulation, Taguchi Method, Indonesia,
Abstract :
The blood supply chain is critical in providing better quality services in the health care system. Packed red cells (PRC) is one of the components in blood that transports the oxygen from the heart to the whole body and disposes the carbon dioxide. Packed red cells are the most demanded product in hospitals.However, the shortage is still a challenge due to its uncertain demand. This research aims to address that challenge. In this research, a blood bank is considered as the middle level in the echelons of the blood supply chain. The discrete event simulation approach is used to develop a simulation model representing a complex blood supply chain system. The Taguchi Method technique is employed to identify the control variables and levels for analysis. The novelty of this research is to develop a simulation model dedicated to observing the uncertainty of supply and demand in the blood supply chain. The model provides an opportunity to customize blood age, which the customers (hospitals) requested. The control variables used in this research are supply arrangement, maximum target of inventory, and production percentage. This research results a policy that could effectively reduce the shortage. Compared to the existing conditions, the proposed decision could increase the order fulfillment rate by up to 99% and decrease outdated products by 16.28%.
Abbasi, B., Vakili, G., & Chesneau, S. (2017). Impacts of reducing the shelf life of red blood cells: A view from down under. Interfaces, 47(4), 336-351.
Agac, G., Baki, B., & Ar, I. M. (2024). Blood supply chain network design: a systematic review of literature and implications for future research. Journal of Modelling in Management, 19(1), 68-118.
Alfonso, E., Xie, X., Augusto, V., & Garraud, O. (2013). Modelling and simulation of blood collection systems: improvement of human resources allocation for better cost‐effectiveness and reduction of candidate donor abandonment. Vox Sanguinis, 104(3), 225-233.
Alghamdi, S. Y. (2023). A review of blood delivery for sustainable supply chain management (BSCM). Sustainability, 15(3), 2757.
Blake, J., & Hardy, M. (2013). Using simulation to evaluate a blood supply network in the Canadian maritime provinces. Journal of Enterprise Information Management, 26(1/2), 119-134.
Dalalah, D., & Alkhaledi, K. A. (2023). Optimization of red blood cell inventory: a blood‐type compatibility‐preference and emergency model. International Transactions in Operational Research, 30(1), 239-272.
Dalalah, D., Bataineh, O., & Alkhaledi, K. A. (2019). Platelets inventory management: A rolling horizon Sim–Opt approach for an age-differentiated demand. Journal of Simulation.
Dillon, M., Oliveira, F., & Abbasi, B. (2017). A two-stage stochastic programming model for inventory management in the blood supply chain. International Journal of Production Economics, 187, 27-41.
Fahimnia, B., Sarkis, J., & Davarzani, H. (2015). Green supply chain management: A review and bibliometric analysis. International journal of production economics, 162, 101-114.
Ghasemi, P., Khalili, H. A., Chobar, A. P., Safavi, S., & Hejri, F. M. (2022). A New Multiechelon Mathematical Modeling for Pre‐and Postdisaster Blood Supply Chain: Robust Optimization Approach. Discrete Dynamics in Nature and Society, 2022(1), 2976929.
Grant, D. B. (2010). Integration of supply and marketing for a blood service. Management Research Review, 33(2), 123-133.
Gunpinar, S., & Centeno, G. (2015). Stochastic integer programming models for reducing wastages and shortages of blood products at hospitals. Computers & Operations Research, 54, 129-141.
Kopach, R., Balcıoğlu, B., & Carter, M. (2008). Tutorial on constructing a red blood cell inventory management system with two demand rates. European Journal of Operational Research, 185(3), 1051-1059.
Li, Y. C., & Liao, H. C. (2012). The optimal parameter design for a blood supply chain system by the Taguchi method. International Journal of Innovative Computing, Information and Control, 8(11), 7697-7712.
Lucas, M. T., Novak, D. C., & Puranam, K. (2022). Heuristics and sensitivity analyses to guide replenishment decisions for red blood cells with random transfer. Decision Support Systems, 154, 113685.
Mansur, A., Handayani, D. I., Wangsa, I. D., Utama, D. M., & Jauhari, W. A. (2023a). A mixed-integer linear programming model for sustainable blood supply chain problems with shelf-life time and multiple blood types. Decision Analytics Journal, 8, 100279.
Mansur, A., Mar’ah, F. I., & Amalia, P. (2020). Platelet inventory management system using monte carlo simulation. IOP Conference Series: Materials Science and Engineering, 722(1), 012004.
Mansur, A., Vanany, I., & Arvitrida, N. I. (2023b). Improving blood bank performance in a decentralised blood supply chain using discrete event simulation.Operations and Supply Chain Management: An International Journal, 16(1), 77-96.
Mansur, A., Vanany, I., & Arvitrida, N. I. (2023c). Horizontal collaboration in a decentralised system: Indonesian blood supply chain. Supply Chain Forum: An International Journal, 24(3), 334-350.
Marques, L., Martins, M., & Araújo, C. (2020). The healthcare supply network: current state of the literature and research opportunities. Production planning & control, 31(7), 590-609.
Ozkan, O. (2023). Multi‐objective optimization of transporting blood products by routing UAVs: the case of Istanbul. International Transactions in Operational Research, 30(1), 302-327.
Purnomo, M. R. A., Wangsa, I. D., Rizky, N., Jauhari, W. A., & Zahria, I. (2022). A multi-echelon fish closed-loop supply chain network problem with carbon emission and traceability. Expert Systems with Applications, 210, 118416.
Qamsari, A. S. N., Hosseini-Motlagh, S. M., & Ghannadpour, S. F. (2022). A column generation approach for an inventory routing problem with fuzzy time windows. Operational Research, 1-51.
Rezaei Kallaj, M., Hasannia Kolaee, M., & Mirzapour Al-e-hashem, S. M. J. (2023). Integrating bloodmobiles and drones in a post-disaster blood collection problem considering blood groups. Annals of Operations Research, 321(1), 783-811.
Rytilä, J. S., & Spens, K. M. (2006). Using simulation to increase efficiency in blood supply chains. Management Research News, 29(12), 801-819.
Vanany, I., Wangsa, I. D., & Jeremi, N. A. (2024). A multi-objective mixed-integer linear model for sustainable dairy supply chain with food waste and environmental pollutants. Process Integration and Optimization for Sustainability, 8(3), 723-740.
Wangsa, I. D., Vanany, I., & Siswanto, N. (2022). Issues in sustainable supply chain’s futuristic technologies: a bibliometric and research trend analysis. Environmental Science and Pollution Research, 29(16), 22885-22912.
Wangsa, I. D., Vanany, I., & Siswanto, N. (2023). An optimization model for fresh-food electronic commerce supply chain with carbon emissions and food waste. Journal of Industrial and Production Engineering, 40(1), 1-21.
Zahraee, S. M., Rohani, J. M., Firouzi, A., & Shahpanah, A. (2015). Efficiency improvement of blood supply chain system using Taguchi method and dynamic simulation. Procedia Manufacturing, 2, 1-5.
Zhou, Y., Cheng, J., Wu, C., & Teo, K. L. (2023). Multi-objective two-stage emergent blood transshipment-allocation in COVID-19 epidemic. Complex & intelligent systems, 9(5), 4939-4957.