Risk Factors Analysis in Blood Supply Chain: A Fuzzy Cognitive Mapping Approach
Subject Areas :
Keywords: Risk factor, Blood supply chain, FCM,
Abstract :
The blood supply chain (BSC) is a critical component of healthcare systems, where efficiency and reliability are paramount to ensuring timely and safe delivery of blood products to patients in need. Risk factors as the factors that directly affect the BSC could be considered permanently to ensure BSC’s productivity. So, understanding and managing these risks is vital for ensuring a robust and resilient blood supply chain. This research employs the Fuzzy Cognitive Mapping (FCM) approach to identify key risk factors affecting the blood supply chain. The required data was gathered using pairwise comparison questionnaire from 10 experts of the regional office of the Blood Transfusion Organization in Tehran province and analysed using FCM Expert software. By mapping the complex interrelationships between various risks, the study reveals that "Delays in Allocation and Distribution," "Disruptions in Logistics Processes," and "Blood Shortages" are among the most influential factors, with significant implications for the overall performance of the supply chain. The analysis also highlights the importance of "Weak Collaboration" and "Insufficient Capacity," which exacerbate operational inefficiencies. The findings suggest that addressing these risks through enhanced collaboration, capacity building, and the integration of advanced technologies can substantially improve the resilience and effectiveness of blood supply chains. Furthermore, the study offers strategic recommendations and suggests avenues for future research,
References:
Aghsami, A., Abazari, S. R., Bakhshi, A., Yazdani, M. A., Jolai, S., & Jolai, F. (2023). A meta-heuristic optimization for a novel mathematical model for minimizing costs and maximizing donor satisfaction in blood supply chains with finite capacity queueing systems. Healthcare Analytics, 3, 100136.
Ala, A., Simic, V., Bacanin, N., & Babaee Tirkolaee, E. (2024). Blood supply chain network design with lateral freight: A robust possibilistic optimization model. Engineering Applications of Artificial Intelligence, 133(Part A), 108053.
Asadpour, M., Olsen, T. L., & Boyer, O. (2022). An updated review on blood supply chain quantitative models: A disaster perspective. Transportation Research Part E: Logistics and Transportation Review, 158, 102583.
Beliën, J., & Force, H. (2012). Supply chain management of blood products: A literature review. European Journal of Operational Research, 217(1), 1-16.
Dehghani, M., Abbasi, B., & Oliveira, F. (2019). Proactive transshipment in the blood supply chain: A stochastic programming approach. Omega, 23, 1245-1253.
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.
Dutta, P., & Nagurney, N. (2019). Multitiered blood supply chain network competition: Linking blood service organizations, hospitals, and payers. Operations Research for Health Care, 23, 100230.
Entezari, S., Abdolazimi, O., Fakhrzad, M. B., Shishebori, D., & Ma, J. (2024). A bi-objective stochastic blood type supply chain configuration and optimization considering time-dependent routing in post-disaster relief logistics. Computers & Industrial Engineering, 188, 109899.
Eskandari-Khanghahi, M., Tavakkoli-Moghaddam, R., Taleizadeh, A. A., & Hassanzadeh Amin, S. (2018). Designing and optimizing a sustainable supply chain network for a blood platelet bank under uncertainty. Engineering Applications of Artificial Intelligence, 71, 236–250.
Ezugwu, A. E., Micheal, M. O., & Govender, P. (2019). Mathematical model formulation and hybrid metaheuristic optimization approach for near-optimal blood assignment in a blood bank system. Expert Systems with Applications, 137, 74-99.
Fahimnia, B., Jabbarzadeh, A., & Ghavamifar, A. (2018). Risk management in blood supply chains under emergency conditions. European Journal of Operational Research, 267(2), 1031-1047.
Fallahi, A., Mokhtari, H., & Akhavan Niaki, S. T. (2021). Designing a closed-loop blood supply chain network considering transportation flow and quality aspects. Sustainable Operations and Computers, 2, 170-189.
Fanoodi, M., Malmir, B., & Firouzi Jahantigh, F. (2019). Reducing demand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models. Computers in Biology and Medicine, 113, 103415.
Ghatreh Samani, M. R., Hosseini-Motlagh, S. M., & Ghannadpour, S. F. (2019). A multilateral perspective towards blood network design in an uncertain environment: Methodology and implementation. Computers & Industrial Engineering, 130, 450–471.
Ghatreh Samani, M. R., Torabi, S. A., & Hosseini-Motlagh, S. M. (2018). Integrated blood supply chain planning for disaster relief. International Journal of Disaster Risk Reduction, 27, 168-188.
Ghorashi, S. B., Hamedi, M., & Sadeghian, R. (2020). Modeling and optimization of a reliable blood supply chain network in crisis considering blood compatibility using MOGWO. Neural Computation & Applications, 32, 12173–12200.
Gilani Larimi, N., & Yaghoubi, S. (2019). A robust mathematical model for platelet supply chain considering social announcements and blood extraction technologies. Computers & Industrial Engineering, 137, 106014.
Gilani Larimi, N., Yaghoubi, S., & Hosseini-Motlagh, S. M. (2019). Itemized platelet supply chain with lateral transshipment under uncertainty evaluating inappropriate output in laboratories. Socio-Economic Planning Sciences, 68, 100697.
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.
Habibi-Kouchaksaraei, M., Paydar, M. M., & Asadi-Gangraj, E. (2018). Designing a bi-objective multi-echelon robust blood supply chain in a disaster. Applied Mathematical Modelling, 55, 583-599.
Habibi-Kouchaksaraei, A., & Kazemi, A. (2020). Financial challenges in maintaining an efficient blood supply chain. Journal of Health Economics and Outcomes Research, 8(1), 29-37.
Haghjoo, N., Tavakkoli-Moghaddam, R., Shahmoradi-Moghadam, H., & Rahimi, Y. (2020). Reliable blood supply chain network design with facility disruption: A real-world application. Engineering Applications of Artificial Intelligence, 90, 103493.
Hamdan, B., & Diabat, A. (2020). Robust design of blood supply chains under risk of disruptions using Lagrangian relaxation. Transportation Research Part E: Logistics and Transportation Review, 134, 101764.
Hosseini-Motlagh, S. M., Ghatreh Samani, M. R., & Cheraghi, S. (2019). Robust and stable flexible blood supply chain network design under motivational initiatives. Socio-Economic Planning Sciences, 70, 100725.
Hosseini-Motlagh, S. M., Ghatreh Samani, M. R., & Faraji, M. (2024). Dynamic optimization of blood collection strategies from different potential donors using rolling horizon planning approach under uncertainty. Computers & Industrial Engineering, 188, 109908.
Jacobs, D. A., Silan, M. N., & Clemson, B. A. (1996). An analysis of alternative locations and service areas of American Red Cross blood facilities. Interfaces, 26(3), 40-50.
Kazemi, Z., Homayounfar, M., Fadaei, M., Soufi, M., & Salehzadeh, A. (2024). Multi-objective Optimization of Blood Supply Network Using the Meta-Heuristic Algorithms. Journal of Optimization in Industrial Engineering, (In Press), xx-xx.
Kenan, N., & Diabat, A. (2022). The supply chain of blood products in the wake of the COVID-19 pandemic: Appointment scheduling and other restrictions. Transportation Research Part E, 159, 102576.
Khalil, A., et al. (2020). Challenges in recruiting voluntary blood donors: A systematic review. Transfusion Medicine Reviews, 34(3), 173-179.K
Khalilpour Azari, S., Soltanzadeh, S., Weber, G. W., & Roy, S. K. (2020). Designing an efficient blood supply chain network in crisis: Neural learning, optimization and case study. Annals of Operations Research, 289, 123–152.
Kharaghani, M., Homayounfar, M., & Taleghani, M. (2023). Value Chains Analysis: Application of Fuzzy Cognitive Map in Pharmaceutical Industry. Journal of System Management, 10(2), 63-78.
Liu, X., & Song, X. (2019). Emergency operations scheduling for a blood supply network in disaster reliefs. IFAC-Papers OnLine, 52(13), 778-783.
Mansur, A., Handayani, D. I., Wangsa, I. D., Utama, D. M., & Jauhari, W. A. (2023). 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.
Meneses, M., Santos, D., & Barbosa-Póvoa, A. (2023). Modelling the blood supply chain. European Journal of Operational Research, 307(2), 499-518.
Movahedi, M., Homayounfar, M., Fadaei Eshkiki, M., & Soufi, M. (2022). Ranking Tehran Stock Exchange Industries using a Combined FCM-ELECTRE III-LA Method. Iranian Journal of Optimization, 14(3), 173-188.
Nezamoddini, N., Gholami, A., & Aqlan, F. (2020). A risk-based optimization framework for integrated supply chains using genetic algorithm and artificial neural networks. International Journal of Production Economics, 225, 107569.
Osorio, A. F., Brailsford, S. C., & Smith, H. K. (2018). Whole blood or apheresis donations? A multi-objective stochastic optimization approach. European Journal of Operational Research, 266(1), 193-204.
Pirabán, A., Guerrero, W. J., & Labadie, N. (2019). Survey on blood supply chain management: Models and methods. Computers and Operations Research, 112, 104756.
Rajendran, S., & Ravindran, S. R. (2019). Inventory management of platelets along blood supply chain to minimize wastage and shortage. Computers & Industrial Engineering, 130, 714–730.
Salehi, F., Mahootchi, M., & Husseini, S. M. M. (2019). Developing a robust stochastic model for designing a blood supply chain network in a crisis: A possible earthquake in Tehran. Annals of Operations Research, 283, 679–703.
Samani, M. R. G., & Hosseini-Motlagh, S. M. (2019). An enhanced procedure for managing blood supply chain under disruptions and uncertainties. Annals of Operations Research, 283, 1413–1462.
Setiawati, M., Simatupang, T. M., & Okdinawati, L. (2024). A dynamics approach for maturity assessment of hospital supply chain management. Journal of Optimization in Industrial Engineering, 17(1), 87-102.
Sharifi, M., & Ghaneipour, M. (2019). Technological challenges in Iran's blood supply chain. Iranian Journal of Blood and Cancer, 11(2), 79-85.
Shih, H., Kasaie, A., & Rajendran, S. (2023). A multiple criteria decision-making model for minimizing platelet shortage and outdating in blood supply chains under demand uncertainty. Healthcare Analytics, 3, 100180.
Suen, T. Y., Song, X., & Jones, D. (2023). A two-stage stochastic model for a multi-objective blood platelet supply chain network design problem incorporating frozen platelets. Computers & Industrial Engineering, 185, 109651.
Tavakol, P., Nahavandi, B., & Homayounfar, M. (2023). A dynamics approach for modeling inventory fluctuations of the pharmaceutical supply chain in COVID-19 pandemic. Journal of Optimization in Industrial Engineering, 34, 105–118.
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.
Toude Bahambari, R., & Soufi, M. (2019). Investigating the Moderating Role of Competitive Strategies on the Impact of Supply Chain Integration on the Financial and Operational Performance (Case Study: The car manufacturing industry in IRAN. Iranian Journal of Optimization, 11(2), 137-147.
Wang, C., & Chen, S. (2020). A distributionally robust optimization for blood supply network considering disasters. Transportation Research Part E: Logistics and Transportation Review, 134, 101840.
Wang, K. M., & Ma, Z. J. (2015). Age-based policy for blood transshipment during blood shortage. Transportation Research Part E: Logistics and Transportation Review, 80, 166-183.
World Health Organization (2017). Global status report on blood safety and availability 2016. WHO Press.
Aghsami, A., Abazari, S. R., Bakhshi, A., Yazdani, M. A., Jolai, S., & Jolai, F. (2023). A meta-heuristic optimization for a novel mathematical model for minimizing costs and maximizing donor satisfaction in blood supply chains with finite capacity queueing systems. Healthcare Analytics, 3, 100136.
Ala, A., Simic, V., Bacanin, N., & Babaee Tirkolaee, E. (2024). Blood supply chain network design with lateral freight: A robust possibilistic optimization model. Engineering Applications of Artificial Intelligence, 133(Part A), 108053.
Asadpour, M., Olsen, T. L., & Boyer, O. (2022). An updated review on blood supply chain quantitative models: A disaster perspective. Transportation Research Part E: Logistics and Transportation Review, 158, 102583.
Beliën, J., & Force, H. (2012). Supply chain management of blood products: A literature review. European Journal of Operational Research, 217(1), 1-16.
Dehghani, M., Abbasi, B., & Oliveira, F. (2019). Proactive transshipment in the blood supply chain: A stochastic programming approach. Omega, 23, 1245-1253.
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.
Dutta, P., & Nagurney, N. (2019). Multitiered blood supply chain network competition: Linking blood service organizations, hospitals, and payers. Operations Research for Health Care, 23, 100230.
Entezari, S., Abdolazimi, O., Fakhrzad, M. B., Shishebori, D., & Ma, J. (2024). A bi-objective stochastic blood type supply chain configuration and optimization considering time-dependent routing in post-disaster relief logistics. Computers & Industrial Engineering, 188, 109899.
Eskandari-Khanghahi, M., Tavakkoli-Moghaddam, R., Taleizadeh, A. A., & Hassanzadeh Amin, S. (2018). Designing and optimizing a sustainable supply chain network for a blood platelet bank under uncertainty. Engineering Applications of Artificial Intelligence, 71, 236–250.
Ezugwu, A. E., Micheal, M. O., & Govender, P. (2019). Mathematical model formulation and hybrid metaheuristic optimization approach for near-optimal blood assignment in a blood bank system. Expert Systems with Applications, 137, 74-99.
Fahimnia, B., Jabbarzadeh, A., & Ghavamifar, A. (2018). Risk management in blood supply chains under emergency conditions. European Journal of Operational Research, 267(2), 1031-1047.
Fallahi, A., Mokhtari, H., & Akhavan Niaki, S. T. (2021). Designing a closed-loop blood supply chain network considering transportation flow and quality aspects. Sustainable Operations and Computers, 2, 170-189.
Fanoodi, M., Malmir, B., & Firouzi Jahantigh, F. (2019). Reducing demand uncertainty in the platelet supply chain through artificial neural networks and ARIMA models. Computers in Biology and Medicine, 113, 103415.
Ghatreh Samani, M. R., Hosseini-Motlagh, S. M., & Ghannadpour, S. F. (2019). A multilateral perspective towards blood network design in an uncertain environment: Methodology and implementation. Computers & Industrial Engineering, 130, 450–471.
Ghatreh Samani, M. R., Torabi, S. A., & Hosseini-Motlagh, S. M. (2018). Integrated blood supply chain planning for disaster relief. International Journal of Disaster Risk Reduction, 27, 168-188.
Ghorashi, S. B., Hamedi, M., & Sadeghian, R. (2020). Modeling and optimization of a reliable blood supply chain network in crisis considering blood compatibility using MOGWO. Neural Computation & Applications, 32, 12173–12200.
Gilani Larimi, N., & Yaghoubi, S. (2019). A robust mathematical model for platelet supply chain considering social announcements and blood extraction technologies. Computers & Industrial Engineering, 137, 106014.
Gilani Larimi, N., Yaghoubi, S., & Hosseini-Motlagh, S. M. (2019). Itemized platelet supply chain with lateral transshipment under uncertainty evaluating inappropriate output in laboratories. Socio-Economic Planning Sciences, 68, 100697.
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.
Habibi-Kouchaksaraei, M., Paydar, M. M., & Asadi-Gangraj, E. (2018). Designing a bi-objective multi-echelon robust blood supply chain in a disaster. Applied Mathematical Modelling, 55, 583-599.
Habibi-Kouchaksaraei, A., & Kazemi, A. (2020). Financial challenges in maintaining an efficient blood supply chain. Journal of Health Economics and Outcomes Research, 8(1), 29-37.
Haghjoo, N., Tavakkoli-Moghaddam, R., Shahmoradi-Moghadam, H., & Rahimi, Y. (2020). Reliable blood supply chain network design with facility disruption: A real-world application. Engineering Applications of Artificial Intelligence, 90, 103493.
Hamdan, B., & Diabat, A. (2020). Robust design of blood supply chains under risk of disruptions using Lagrangian relaxation. Transportation Research Part E: Logistics and Transportation Review, 134, 101764.
Hosseini-Motlagh, S. M., Ghatreh Samani, M. R., & Cheraghi, S. (2019). Robust and stable flexible blood supply chain network design under motivational initiatives. Socio-Economic Planning Sciences, 70, 100725.
Hosseini-Motlagh, S. M., Ghatreh Samani, M. R., & Faraji, M. (2024). Dynamic optimization of blood collection strategies from different potential donors using rolling horizon planning approach under uncertainty. Computers & Industrial Engineering, 188, 109908.
Jacobs, D. A., Silan, M. N., & Clemson, B. A. (1996). An analysis of alternative locations and service areas of American Red Cross blood facilities. Interfaces, 26(3), 40-50.
Kazemi, Z., Homayounfar, M., Fadaei, M., Soufi, M., & Salehzadeh, A. (2024). Multi-objective Optimization of Blood Supply Network Using the Meta-Heuristic Algorithms. Journal of Optimization in Industrial Engineering, (In Press), xx-xx.
Kenan, N., & Diabat, A. (2022). The supply chain of blood products in the wake of the COVID-19 pandemic: Appointment scheduling and other restrictions. Transportation Research Part E, 159, 102576.
Khalil, A., et al. (2020). Challenges in recruiting voluntary blood donors: A systematic review. Transfusion Medicine Reviews, 34(3), 173-179.K
Khalilpour Azari, S., Soltanzadeh, S., Weber, G. W., & Roy, S. K. (2020). Designing an efficient blood supply chain network in crisis: Neural learning, optimization and case study. Annals of Operations Research, 289, 123–152.
Kharaghani, M., Homayounfar, M., & Taleghani, M. (2023). Value Chains Analysis: Application of Fuzzy Cognitive Map in Pharmaceutical Industry. Journal of System Management, 10(2), 63-78.
Liu, X., & Song, X. (2019). Emergency operations scheduling for a blood supply network in disaster reliefs. IFAC-Papers OnLine, 52(13), 778-783.
Mansur, A., Handayani, D. I., Wangsa, I. D., Utama, D. M., & Jauhari, W. A. (2023). 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.
Meneses, M., Santos, D., & Barbosa-Póvoa, A. (2023). Modelling the blood supply chain. European Journal of Operational Research, 307(2), 499-518.
Movahedi, M., Homayounfar, M., Fadaei Eshkiki, M., & Soufi, M. (2022). Ranking Tehran Stock Exchange Industries using a Combined FCM-ELECTRE III-LA Method. Iranian Journal of Optimization, 14(3), 173-188.
Nezamoddini, N., Gholami, A., & Aqlan, F. (2020). A risk-based optimization framework for integrated supply chains using genetic algorithm and artificial neural networks. International Journal of Production Economics, 225, 107569.
Osorio, A. F., Brailsford, S. C., & Smith, H. K. (2018). Whole blood or apheresis donations? A multi-objective stochastic optimization approach. European Journal of Operational Research, 266(1), 193-204.
Pirabán, A., Guerrero, W. J., & Labadie, N. (2019). Survey on blood supply chain management: Models and methods. Computers and Operations Research, 112, 104756.
Rajendran, S., & Ravindran, S. R. (2019). Inventory management of platelets along blood supply chain to minimize wastage and shortage. Computers & Industrial Engineering, 130, 714–730.
Salehi, F., Mahootchi, M., & Husseini, S. M. M. (2019). Developing a robust stochastic model for designing a blood supply chain network in a crisis: A possible earthquake in Tehran. Annals of Operations Research, 283, 679–703.
Samani, M. R. G., & Hosseini-Motlagh, S. M. (2019). An enhanced procedure for managing blood supply chain under disruptions and uncertainties. Annals of Operations Research, 283, 1413–1462.
Setiawati, M., Simatupang, T. M., & Okdinawati, L. (2024). A dynamics approach for maturity assessment of hospital supply chain management. Journal of Optimization in Industrial Engineering, 17(1), 87-102.
Sharifi, M., & Ghaneipour, M. (2019). Technological challenges in Iran's blood supply chain. Iranian Journal of Blood and Cancer, 11(2), 79-85.
Shih, H., Kasaie, A., & Rajendran, S. (2023). A multiple criteria decision-making model for minimizing platelet shortage and outdating in blood supply chains under demand uncertainty. Healthcare Analytics, 3, 100180.
Suen, T. Y., Song, X., & Jones, D. (2023). A two-stage stochastic model for a multi-objective blood platelet supply chain network design problem incorporating frozen platelets. Computers & Industrial Engineering, 185, 109651.
Tavakol, P., Nahavandi, B., & Homayounfar, M. (2023). A dynamics approach for modeling inventory fluctuations of the pharmaceutical supply chain in COVID-19 pandemic. Journal of Optimization in Industrial Engineering, 34, 105–118.
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.
Toude Bahambari, R., & Soufi, M. (2019). Investigating the Moderating Role of Competitive Strategies on the Impact of Supply Chain Integration on the Financial and Operational Performance (Case Study: The car manufacturing industry in IRAN. Iranian Journal of Optimization, 11(2), 137-147.
Wang, C., & Chen, S. (2020). A distributionally robust optimization for blood supply network considering disasters. Transportation Research Part E: Logistics and Transportation Review, 134, 101840.
Wang, K. M., & Ma, Z. J. (2015). Age-based policy for blood transshipment during blood shortage. Transportation Research Part E: Logistics and Transportation Review, 80, 166-183.
World Health Organization (2017). Global status report on blood safety and availability 2016. WHO Press.