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Developing a Risk Reduction Support System for Health System in Iran: A Case Study in Blood Supply Chain Management

Author

Listed:
  • Ali Sibevei

    (Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran 14115-111, Iran)

  • Adel Azar

    (Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran 14115-111, Iran)

  • Mostafa Zandieh

    (Department of Industrial Management and Information Technology, Management and Accounting Faculty, Shahid Beheshti University, Tehran 1983969411, Iran)

  • Seyed Mohammad Khalili

    (Department of Industrial Engineering, Faculty of Engineering, Khayyam University, Mashhad 9189747178, Iran)

  • Maziar Yazdani

    (School of Built Environment, University of New South Wales, Kensington, Sydney, NSW 2052, Australia)

Abstract

Health systems are recognised as playing a potentially important role in many risk management strategies; however, there is strong evidence that health systems themselves have been the victims of unanticipated risks and have lost their functionality in providing reliable services. Existing risk identification and assessment tools in the health sector, particularly in the blood supply chain, address and evaluate risks without taking into account their interdependence and a holistic perspective. As a result, the aim of this paper is to develop a new systemic framework based on a semi-quantitative risk assessment approach to measure supply chain risks, which will be implemented through a case study on the Iranian BSC. This paper identifies and assesses supply chain risks (SCRs) by employing a novel systemic process known as SSM-SNA-ISM (SSI). First, the supply chain and its risks are identified using Soft Systems Methodology (SSM). Then, given the large number of risks, the second stage uses Social Network Analysis (SNA) to identify the relationships between the risks and select the most important ones. In the third stage, risk levelling is performed with a more in-depth analysis of the selected risks and the application of Interpretive Structural Modelling (ISM), and further analysis is performed using the Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). The study found that by using the new proposed approach, taking into account risk relationships, and taking a holistic view, various supply chain risks could be assessed more effectively, especially when the number of risks is large. The findings also revealed that resolving the root risks of the blood supply chain frequently necessitates management skills. This paper contributes to the literature on supply chain risk management in two ways: First, a novel systemic approach to identifying and evaluating risks is proposed. This process offers a fresh perspective on supply chain risk modelling by utilising systems thinking tools. Second, by identifying Iranian BSC risks and identifying special risks.

Suggested Citation

  • Ali Sibevei & Adel Azar & Mostafa Zandieh & Seyed Mohammad Khalili & Maziar Yazdani, 2022. "Developing a Risk Reduction Support System for Health System in Iran: A Case Study in Blood Supply Chain Management," IJERPH, MDPI, vol. 19(4), pages 1-19, February.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:4:p:2139-:d:749017
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    References listed on IDEAS

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    1. Sun, Huaping & Geng, Yong & Hu, Lingxiang & Shi, Longyu & Xu, Tong, 2018. "Measuring China's new energy vehicle patents: A social network analysis approach," Energy, Elsevier, vol. 153(C), pages 685-693.
    2. Jorge Arnaldo Troche-Escobar & Herman Augusto Lepikson & Francisco Gaudêncio Mendonça Freires, 2018. "A Study of Supply Chain Risk in the Brazilian Wind Power Projects by Interpretive Structural Modeling and MICMAC Analysis," Sustainability, MDPI, vol. 10(10), pages 1-24, September.
    3. Mahyar Habibi Rad & Mohammad Mojtahedi & Michael J. Ostwald, 2021. "The Integration of Lean and Resilience Paradigms: A Systematic Review Identifying Current and Future Research Directions," Sustainability, MDPI, vol. 13(16), pages 1-24, August.
    4. Muhammad Junaid & Ye Xue & Muzzammil Wasim Syed & Ji Zu Li & Muhammad Ziaullah, 2019. "A Neutrosophic AHP and TOPSIS Framework for Supply Chain Risk Assessment in Automotive Industry of Pakistan," Sustainability, MDPI, vol. 12(1), pages 1-26, December.
    5. Kumar, Sourabh & Barua, Mukesh Kumar, 2022. "A modeling framework and analysis of challenges faced by the Indian petroleum supply chain," Energy, Elsevier, vol. 239(PE).
    6. Kevin B. Hendricks & Vinod R. Singhal, 2005. "Association Between Supply Chain Glitches and Operating Performance," Management Science, INFORMS, vol. 51(5), pages 695-711, May.
    7. Wang, Xiaojun & Chan, Hing Kai & Yee, Rachel W.Y. & Diaz-Rainey, Ivan, 2012. "A two-stage fuzzy-AHP model for risk assessment of implementing green initiatives in the fashion supply chain," International Journal of Production Economics, Elsevier, vol. 135(2), pages 595-606.
    8. Nili, Maryam & Seyedhosseini, Seyed Mohammad & Jabalameli, Mohammad Saeed & Dehghani, Ehsan, 2021. "A multi-objective optimization model to sustainable closed-loop solar photovoltaic supply chain network design: A case study in Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    9. Thun, Jörn-Henrik & Hoenig, Daniel, 2011. "An empirical analysis of supply chain risk management in the German automotive industry," International Journal of Production Economics, Elsevier, vol. 131(1), pages 242-249, May.
    10. Alam, Shahriar Tanvir & Ahmed, Sayem & Ali, Syed Mithun & Sarker, Sudipa & Kabir, Golam & ul-Islam, Asif, 2021. "Challenges to COVID-19 vaccine supply chain: Implications for sustainable development goals," International Journal of Production Economics, Elsevier, vol. 239(C).
    11. Wang, Wei & Liu, Wenbin & Mingers, John, 2015. "A systemic method for organisational stakeholder identification and analysis using Soft Systems Methodology (SSM)," European Journal of Operational Research, Elsevier, vol. 246(2), pages 562-574.
    12. Zahra Mohammadian-Behbahani & Armin Jabbarzadeh & Mir Saman Pishvaee, 2019. "A robust optimisation model for sustainable blood supply chain network design under uncertainty," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 31(4), pages 475-494.
    13. Dehghani, Maryam & Abbasi, Babak & Oliveira, Fabricio, 2021. "Proactive transshipment in the blood supply chain: A stochastic programming approach," Omega, Elsevier, vol. 98(C).
    14. Buzacott, John A. & Steve Peng, H., 2012. "Contract design for risk sharing partnerships in manufacturing," European Journal of Operational Research, Elsevier, vol. 218(3), pages 656-666.
    15. Safar Fazli & Reza Kiani & Mohammadali Vosooghidizaji, 2015. "Crude oil supply chain risk management with DEMATEL–ANP," Post-Print hal-02327343, HAL.
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