Using a two-step approach of risk matrix and DEMATEL to identify and analyze the most important risks in the blood supply chain (Blood Supply Chain of Tehran)
Subject Areas : medical documents
Ali Sibevei
1
(
PhD Student in Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran
)
Adel Azar
2
(
Professor, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran
)
Mostafa Zandieh
3
(
Associate Professor, Faculty of Management and Accounting, Shahid Beheshti University, Tehran, Iran
)
Keywords: DEMATEL, Blood supply chain, Risk matrix, Supply chain risk management,
Abstract :
Introduction: Supply chain risk management is very important for effective supply chain management. In the meantime, the healthcare supply chain and its risks need more attention given its important mission. Concerning the sensitivity of blood and its supply chain, the purpose of this study is to investigate the risks of the blood supply chain and their relations in Tehran. Methods: In this descriptive-analytical study, the risks of blood supply chain were selected by reviewing the articles and expert opinions through interviews and questionnaires. The most important ones were identified using risk matrix and expert opinions, and their relationships were discussed using DEMATEL method. Results: The 19 most important risks were identified. Also, according to the Demetel method, it was shown that in terms of the importance of risks (D + R), selection of inappropriate suppliers (2.25), lack of appropriate budget allocation (2.23), low employee productivity (2.16) and inadequate blood inventory (2.01) were identified as the most important risks. In terms of mutual influences (D-R), economic and political sanctions (1.68) were selected as the most influencing and inadequate response to hospital demand (-0.89) as the most influenced risk. Conclusion: After identifying the risks that are both significant and most influencing, such as the risks of lack of appropriate budget allocation, sanctions, exchange rate fluctuations, and catastrophes, in order to improve supply chain performance and prevent future problems, the factors that cause these risks need to be focused. And for controlling them, appropriate strategies, especially management strategies should be adopted.
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13- Hudnurkar M, Deshpande S, Rathod U, Jakhar SK. Supply chain risk classification schemes: A literature review. Oper and Supply Chain Manag. 2017; 10(4): 182-99.
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17- Kiani Mavi R, Goh M, Kiani Mavi N. Supplier selection with Shannon entropy and fuzzy TOPSIS in the context of supply chain risk management. 2016; 253: 216-225.
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31- Forati, E, Esparham, R, Dargahi, M. assessing and prioritizing the factors affecting the patient's trust in the physician from the point of view of experts in Ilam university of medical sciences in 2018 with the combined approach of DEMATEL and ANP. Aborz Univ Med J. 2020; 9(1): 9-20. [In Persian].
32- Conte KP, Davidson S. Using a ‘rich picture’ to facilitate systems thinking in research coproduction. Health Res. Policy Syst. 2020; 18(1): 1-14.
33- Wijnia Y. Asset Risk Management: Issues in the design and use of the risk matrix. In Engineering Asset Management and Infrastructure Sustainability. London: Springer; 2012: 1043-1059.
34- Aven T, Renn O. Risk management and governance: concepts, guidelines and applications. Germany: Springer Science & Business Media; 2010: 71-105.
35- Tavanti M, Wood L. A method for quantitative estimate of risk probability in use risk assessment. Proceedings of the Human Factors and Ergonomics Society Europe. 2017.
36- Kaya GK, Ward J, Clarkson J. A Review of Risk Matrices Used in Acute Hospitals in England. Risk Anal. 2019; 39(5): 1060-1070.
37- Al-Zuheri A, Amer Y, Vlachos I. Risk assessment and analysis of healthcare system using probability-impact matrix. Nur Primary Care. 2019; 3(4):1-4.
38- Ale B, Burnap P, Slater D. On the origin of PCDS – (Probability consequence diagrams). Safety science. 2015; 72: 229-239.
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1- Georgiadis MC, Tsiakis P, Longinidis P, Sofioglou MK. Optimal design of supply chain networks under uncertain transient demand variations. Omega. 2011; 39(3): 254-72.
2- Peck H. Reconciling supply chain vulnerability, risk and supply chain management. International Journal of Logistics: Res. Appl. 2006; 9(2): 127-42.
3- Ghadge A, Dani S, Kalawsky R. Supply chain risk management: present and future scope. Int. J. Logist. Manag. 2012; 23(3): 313-339.
4- Kamalahmadi M, Parast MM. A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research. Int. J. Prod. Econ. 2016; 171: 116-33.
5- Pereira CR, Christopher M, Da Silva AL. Achieving supply chain resilience: the role of procurement. Supply Chain Manag. 2014; 19(5/6): 626-642.
6- Stefanovic D, Stefanovic N, Radenkovic, B. Supply network modelling and simulation methodology. Simul Model Pract Theory. 2009; 17(4): 743-766.
7- Boonyanusith W, Jittamaip. Blood supply chain risk management using house of risk model. Walailak J Sci Technol. 2019; 16(8): 573-91.
8- Nagurney A, Masoumi AH, Yu M. Supply chain network operations management of a blood banking system with cost and risk minimization. Comput. Manag. Sci. 2012; 9(2): 205-231.
9- Stanger SH, Wilding R, Yates N, Cotton S. What drives perishable inventory management performance? Lessons learnt from the UK blood supply chain. Supply Chain Manag. 2012; 17: 107-123.
10- Aven T. Risk assessment and risk management: Review of recent advances on their foundation. Eur. J. Oper. Res. 2016; 253(1):1-13.
11- Wagner SM, Bode C. An empirical investigation into supply chain vulnerability. J. Purch. Supply Manag. 2006; 12(6): 301-312.
12- Zsidisin GA. A grounded definition of supply risk. J. Purch. Supply Manag. 2003; 9(5-6): 217-224.
13- Hudnurkar M, Deshpande S, Rathod U, Jakhar SK. Supply chain risk classification schemes: A literature review. Oper and Supply Chain Manag. 2017; 10(4): 182-99.
14- Bradley JR. An improved method for managing catastrophic supply chain disruptions. Bus. Horiz. 2014; 57(4): 483-495.
15- Mital M, Del Giudice M, Papa A. Comparing supply chain risks for multiple product categories with cognitive mapping and analytic hierarchy process. Technol Forecast Soc Change. 2018; 131: 159-170.
16- Xie C, Anumba CJ, Lee TR, Tummala R, Schoenherr T. Assessing and managing risks using the supply chain risk management process (SCRMP). Supply Chain Manag. 2011; 16(6): 474-483.
17- Kiani Mavi R, Goh M, Kiani Mavi N. Supplier selection with Shannon entropy and fuzzy TOPSIS in the context of supply chain risk management. 2016; 253: 216-225.
18- Jüttner U, Peck H, Christopher M. Supply chain risk management: outlining an agenda for future research. Int. J. Logist. Res. Appl. 2003; 6(4): 197-210.
19- Mandal S. The influence of organizational culture on healthcare supply chain resilience: moderating role of technology orientation. J. Bus. Ind. Mark. 2017; 32(8): 1021-1037.
20- Samani MR, Hosseini-Motlagh SM, Ghannadpour SF. A multilateral perspective towards blood network design in an uncertain environment: Methodology and implementation. Comput Ind Eng. 2019; 130: 450-471.
21- Cagliano AC, Grimaldi S, Mangano G, Rafele C. Risk Management in Hospital Wards: The Case of Blood Procurement and Handling. IFAC-PapersOnLine. 2017; 50(1): 4648-4653.
22- Burgmeier J. Failure mode and effect analysis: an application in reducing risk in blood transfusion. Jt Comm J Qual Improv. 2002; 28(6): 331-339.
23- Lu Y, Teng F, Zhou J, Wen A, Bi Y. Failure mode and effect analysis in blood transfusion: a proactive tool to reduce risks. Transfusion. 2013; 53(12): 3080-3087.
24- Mora A, Ayala L, Bielza R, Ataulfo Gonzalez F, Villegas A. Improving safety in blood transfusion using failure mode and effect analysis. Transfusion. 2019; 59(2): 516-523.
25- Najafpour Z, Hasoumi M, Behzadi F, Mohamadi E, Jafary M, Saeedi M. Preventing blood transfusion failures: FMEA, an effective assessment method. BMC Health Serv. Res. 2017; 17(1): 453.
26- Liu HC. FMEA using cluster analysis and prospect theory and its application to blood transfusion. In Improved FMEA Methods for Proactive Healthcare Risk Analysis. Singapore: Springer; 2019: 73-96.
27- Maskens C, Downie H, Wendt A, Lima A, Merkley L, Lin Y, Callum J. Hospital‐based transfusion error tracking from 2005 to 2010: identifying the key errors threatening patient transfusion safety. Transfusion. 2014; 54(1): 66-73.
28- Achmadi RE, Mansur A. Design mitigation of blood supply chain using supply chain risk management approach. IEOM. 2018.
29- Whyte G. Risk management in blood transfusion services. Vox Sang. 1998; 74: 105-9.
30- Chandrashekar S, Kantharaj A. Risk mitigation in blood transfusion services–A practical approach at the blood center level. Glob. J. Transfus. Med. 2019; 4(2):132.
31- Forati, E, Esparham, R, Dargahi, M. assessing and prioritizing the factors affecting the patient's trust in the physician from the point of view of experts in Ilam university of medical sciences in 2018 with the combined approach of DEMATEL and ANP. Aborz Univ Med J. 2020; 9(1): 9-20. [In Persian].
32- Conte KP, Davidson S. Using a ‘rich picture’ to facilitate systems thinking in research coproduction. Health Res. Policy Syst. 2020; 18(1): 1-14.
33- Wijnia Y. Asset Risk Management: Issues in the design and use of the risk matrix. In Engineering Asset Management and Infrastructure Sustainability. London: Springer; 2012: 1043-1059.
34- Aven T, Renn O. Risk management and governance: concepts, guidelines and applications. Germany: Springer Science & Business Media; 2010: 71-105.
35- Tavanti M, Wood L. A method for quantitative estimate of risk probability in use risk assessment. Proceedings of the Human Factors and Ergonomics Society Europe. 2017.
36- Kaya GK, Ward J, Clarkson J. A Review of Risk Matrices Used in Acute Hospitals in England. Risk Anal. 2019; 39(5): 1060-1070.
37- Al-Zuheri A, Amer Y, Vlachos I. Risk assessment and analysis of healthcare system using probability-impact matrix. Nur Primary Care. 2019; 3(4):1-4.
38- Ale B, Burnap P, Slater D. On the origin of PCDS – (Probability consequence diagrams). Safety science. 2015; 72: 229-239.
