Evaluation of supply chain risks by fuzzy DEMATEL method: a case study of iron and steel industry in Turkey





Fuzzy DEMATEL, Iron and steel industry, Supply chain risk management, Risk assessment


Business practices to strengthen competitiveness increase the vulnerability of supply chains to risks. Risks that can adversely affect the effectiveness and efficiency of supply chain activities are events that disrupt the flow of information, materials, money, and products. Therefore, supply chain risk management is vital for companies. It is necessary to identify the risks that threaten the supply chain and prioritize them. In addition, examining the effects of risks on each other will determine the success of supply chain risk management. This study evaluates Turkey’s leading iron and steel company’s supply chain risk groups and sub-risks. The fuzzy DEMATEL method was used to determine the relative importance of the risks and the effects of the risks on each other. Results show that the most critical risk group is business risks. Business risk is followed by customer risks, supplier risks, transportation risks, environmental risks, and, finally, security risks. This study provides originality by evaluating the supply chain risks from a broader perspective.


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Author Biographies

Asuman Üstündağ, Sakarya University

School of Business

Sinan Çıkmak, Duzce University

Ph.D. Lecturer, Social Sciences Vocational School

Merve Çankaya Eyiol, Sakarya University

School of Business

Mustafa Cahit Ungan, Sakarya University

Professor, School of Business


Ali, S.M., Paul, S.K., Chowdhury, P., Agarwal, R., Fathollahi-Fard, A.M., Jabbour, C.J.C., & Luthra, S. (2021). Modelling of supply chain disruption analytics using an integrated approach: An emerging economy example. Expert Systems with Applications, 173, 114690. https://doi.org/10.1016/j.eswa.2021.114690

Alora, A., & Barua, M.K. (2022). Development of a supply chain risk index for manufacturing supply chains. International Journal of Productivity and Performance Management. 71(2), 477-503. https://doi.org/10.1108/IJPPM-11-2018-0422

Aqlan, F., & Lam, S.S. (2015). A fuzzy-based integrated framework for supply chain risk assessment. International Journal of Production Economics, 161, 54-63. https://doi.org/10.1016/j.ijpe.2014.11.013

Baykasoğlu, A., Kaplanoğlu, V., Durmuşoğlu, Z.D.U., & Şahin, C. (2013). Integrating fuzzy DEMATEL and fuzzy hierarchical TOPSIS methods for truck selection. Expert Systems with Applications, 40(3), 899-907. https://doi.org/10.1016/j.eswa.2012.05.046

Can Saglam, Y., Yildiz Çankaya, S., & Sezen, B. (2020). Proactive risk mitigation strategies and supply chain risk management performance: an empirical analysis for manufacturing firms in Turkey. Journal of Manufacturing Technology Management. 32(6), 1234-1244. https://doi.org/10.1108/JMTM-08-2019-0299

Ceryno, P.S., Scavarda, L.F., & Klingebiel, K. (2015). Supply chain risk: Empirical research in the automotive industry. Journal of Risk Research, 18(9), 1145-1164. https://doi.org/10.1080/13669877.2014.913662

Cheong, T., & Song, S.H. (2013). The value of information on supply risk under random yields. Transportation Research Part E: Logistics and Transportation Review, 60, 27-38. https://doi.org/10.1016/j.tre.2013.09.006

Chopra, S., & Sodhi, M.M.S. (2004). Managing risk to avoid supply-chain breakdown. MIT Sloan Management Review, 46(1), 53-61.

Christopher, M., & Peck, H. (2004). Building the Resilient Supply Chain. The International Journal of Logistics Management, 15(2), 1-14. https://doi.org/10.1108/09574090410700275

Chu, C.Y., Park, K., & Kremer, G.E. (2020). A global supply chain risk management framework: An application of text-mining to identify region-specific supply chain risks. Advanced Engineering Informatics, 45, 101053. https://doi.org/10.1016/j.aei.2020.101053

Deloitte. (2012). Supply Chain Resilience: A Risk Intelligent approach to managing global supply chains, https://www2.deloitte.com/global/en/pages/governance-risk-and-compliance/articles/risk-intelligent-approach-managing-supplychains.html

Dong, Q., & Cooper, O. (2016). An orders-of-magnitude AHP supply chain risk assessment framework. International Journal of Production Economics, 182, 144-156. https://doi.org/10.1016/j.ijpe.2016.08.021

Duong, A.T.B., Vo, V.X., Carvalho, M.D.S., Sampaio, P., & Truong, H.Q. (2022). Risks and supply chain performance: globalization and COVID-19 perspectives. International Journal of Productivity and Performance Management. https://doi.org/10.1108/IJPPM-03-2021-0179

Durowoju, O.A., Chan, H.K., & Wang, X. (2012). Entropy assessment of supply chain disruption. Journal of Manufacturing Technology Management, 23(8), 998-1014. https://doi.org/10.1108/17410381211276844

Govindan, K., & Chaudhuri, A. (2016). Interrelationships of risks faced by third party logistics service providers: A DEMATEL based approach. Transportation Research Part E: Logistics and Transportation Review, 90, 177-195. https://doi.org/10.1016/j.tre.2015.11.010

Gurtu, A., & Johny, J. (2021). Supply chain risk management: Literature review. Risks, 9(1), 16. https://doi.org/10.3390/risks9010016

Hachicha, W., & Elmsalmi, M. (2014). An integrated approach based-structural modeling for risk prioritization in supply network management. Journal of Risk Research, 17(10), 1301-1324. https://doi.org/10.1080/13669877.2013.841734

Hallikas, J., Karvonen, I., Pulkkinen, U., Virolainen, V.M., & Tuominen, M. (2004). Risk management processes in supplier networks. International Journal of Production Economics, 90(1), 47-58. https://doi.org/10.1016/j.ijpe.2004.02.007

Hashemi, S.H., Karimi, A., & Tavana, M. (2015). An integrated green supplier selection approach with analytic network process and improved Grey relational analysis. International Journal of Production Economics, 159, 178-191. https://doi.org/10.1016/j.ijpe.2014.09.027

Hermoso-Orzáez, M.J., & Garzón-Moreno, J. (2021). Risk management methodology in the supply chain: a case study applied. Annals of Operations Research, 1-25. https://doi.org/10.1007/s10479-022-04583-w

Ho, W., Zheng, T., Yildiz, H., & Talluri, S. (2015). Supply chain risk management: A literature review. International Journal of Production Research, 53(16), 5031-5069. https://doi.org/10.1080/00207543.2015.1030467

Hopkin, P. (2018). Fundamentals of Risk Management: Understanding, Evaluating and Implementing Effective Risk Management (5th Edition). Kogan Page.

Hsu, C.-W., Kuo, T.-C., Chen, S.-H., & Hu, A.H. (2013). Using DEMATEL to develop a carbon management model of supplier selection in green supply chain management. Journal of Cleaner Production, 56, 164-172. https://doi.org/10.1016/j.jclepro.2011.09.012

Iron Steel Sector Report. (2020). https://www.sanayi.gov.tr/assets/pdf/plan-program/DemirÇelikSektörRaporu2020.pdf (In Turkish)

Ji, G., & Zhu, C. (2012). A study on emergency supply chain and risk based on urgent relief service in disasters. Systems Engineering Procedia, 5, 313-325. https://doi.org/10.1016/j.sepro.2012.04.049

Jüttner, U., Peck, H., & Christopher, M. (2003). Supply chain risk management: outlining an agenda for future research. International Journal of Logistics Research and Applications, 6(4), 197-210. https://doi.org/10.1080/13675560310001627016

Kabak, Ö., Ülengin, F., Çekyay, B., Önsel, Ş., & Özaydın, Ö. (2016). Critical success factors for the Iron and Steel Industry in Turkey: A Fuzzy DEMATEL Approach. International Journal of Fuzzy Systems, 18(3), 523-536. https://doi.org/10.1007/s40815-015-0067-7

Khan, S., Haleem, A., & Khan, M.I. (2021a). Assessment of risk in the management of Halal supply chain using fuzzy BWM method. Supply Chain Forum: An International Journal, 22(1), 57-73. https://doi.org/10.1080/16258312.2020.1788905

Khan, S., Haleem, A., & Khan, M.I. (2021b). Risk management in Halal supply chain: an integrated fuzzy Delphi and DEMATEL approach. Journal of Modelling in Management, 16(1), 172-214. https://doi.org/10.1108/JM2-09-2019-0228

Khilwani, N., Tiwari, M.K., & Sabuncuoglu, I. (2011). Hybrid Petri-nets for modelling and performance evaluation of supply chains. International Journal of Production Research, 49(15), 4627-4656. https://doi.org/10.1080/00207543.2010.497173

Kumar, G., Singh, R.K., Jain, R., & Kain, R. (2020). Analysis of demand risks for the Indian automotive sector in globally competitive environment. International Journal of Organizational Analysis, 30(4), 836-863. https://doi.org/10.1108/IJOA-03-2020-2076

Kumar, M., Vrat, P., & Shankar, R. (2004). A fuzzy goal programming approach for vendor selection problem in a supply chain. Computers and Industrial Engineering, 46(1), 69-85. https://doi.org/10.1016/j.cie.2003.09.010

Kumar, S.K., Tiwari, M.K., & Babiceanu, R.F. (2010). Minimisation of supply chain cost with embedded risk using computational intelligence approaches. International Journal of Production Research, 48(13), 3717-3739. https://doi.org/10.1080/00207540902893425

Lahane, S., & Kant, R. (2021). Evaluation and ranking of solutions to mitigate circular supply chain risks. Sustainable Production and Consumption, 27, 753-773. https://doi.org/10.1016/j.spc.2021.01.034

Lin, C.J., & Wu, W.W. (2008). A causal analytical method for group decision-making under fuzzy environment. Expert Systems with Applications, 34(1), 205-213. https://doi.org/10.1016/j.eswa.2006.08.012

Lockamy III, A., & McCormack, K. (2009). Examining Operational Risks in Supply Chains. Supply Chain Forum: An International Journal, 10(1), 2-14. https://doi.org/10.1080/16258312.2009.11517204

Manuj, I., & Mentzer, J.T. (2008). Global supply chain risk management. Journal of Business Logistics, 29(1), 133-155. https://doi.org/10.1002/j.2158-1592.2008.tb00072.x

Mital, M., Del Giudice, M., & Papa, A. (2018). Comparing supply chain risks for multiple product categories with cognitive mapping and Analytic Hierarchy Process. Technological Forecasting and Social Change, 131, 159-170. https://doi.org/10.1016/j.techfore.2017.05.036

Mostafa, A.I., Rashed, A.M., Alsherif, Y.A., Enien, Y.N., Kaoud, M., & Mohib, A. (2021, October). Supply Chain Risk Assessment Using Fuzzy Logic. In 2021 3rd Novel Intelligent and Leading Emerging Sciences Conference (NILES) (pp. 246-251). IEEE. https://doi.org/10.1109/NILES53778.2021.9600100

Munir, M., Jajja, M.S.S., Chatha, K.A., & Farooq, S. (2020). Supply chain risk management and operational performance: The enabling role of supply chain integration. International Journal of Production Economics, 227, 107667. https://doi.org/10.1016/j.ijpe.2020.107667

Mzougui, I., Carpitella, S., Certa, A., El Felsoufi, Z., & Izquierdo, J. (2020). Assessing supply chain risks in the automotive industry through a modified MCDM-Based FMECA. Processes, 8(5), 579. https://doi.org/10.3390/pr8050579

Oke, A., & Gopalakrishnan, M. (2009). Managing disruptions in supply chains: A case study of a retail supply chain. International Journal of Production Economics, 118(1), 168-174. https://doi.org/10.1016/j.ijpe.2008.08.045

Oliveira, F.L., Junior, A.D.R.O., & Rebelo, L.M.B. (2017). Adapting transport modes to supply chains classified by the uncertainty supply chain model: A case study at Manaus Industrial Pole. International Journal of Production Management and Engineering, 5(1), 39-43. https://doi.org/10.4995/ijpme.2017.5775

Opricovic, S., & Tzeng, G.H. (2003). Defuzzification within a multi-criteria decision model. International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, 11(5), 635-652. https://doi.org/10.1142/S0218488503002387

Oturakçı, M., & Yıldırım, R.S. (2022). Analysis of supply chain risks by structural equation model and fuzzy analytical hierarchy process. Pamukkale University Journal of Engineering Sciences, 28(1), 117-127. https://doi.org/10.5505/pajes.2021.34119

Parast, M.M., & Subramanian, N. (2021). An examination of the effect of supply chain disruption risk drivers on organizational performance: evidence from Chinese supply chains. Supply Chain Management: An International Journal, 26(4), 548-562. https://doi.org/10.1108/SCM-07-2020-0313

Paul, S., Kabir, G., Ali, S.M., & Zhang, G. (2020). Examining transportation disruption risk in supply chains: A case study from Bangladeshi pharmaceutical industry. Research in Transportation Business & Management, 37, 100485. https://doi.org/10.1016/j.rtbm.2020.100485

Pfohl, H.C., Gallus, P., & Thomas, D. (2011). Interpretive structural modeling of supply chain risks. International Journal of Physical Distribution and Logistics Management, 41(9), 839-859. https://doi.org/10.1108/09600031111175816

Prakash, S., Soni, G., & Rathore, A.P.S. (2017). A critical analysis of supply chain risk management content: a structured literature review. Journal of Advances in Management Research, 14(1), 69-90. https://doi.org/10.1108/JAMR-10-2015-0073

Pujawan, I.N., & Bah, A.U. (2022). Supply chains under COVID-19 disruptions: literature review and research agenda. Supply Chain Forum: An International Journal, 23(1), 81-95. https://doi.org/10.1080/16258312.2021.1932568

Punniyamoorthy, M., Thamaraiselvan, N., & Manikandan, L. (2013). Assessment of supply chain risk: Scale development and validation. Benchmarking, 20(1), 79-105. https://doi.org/10.1108/14635771311299506

Rajesh, R., & Ravi, V. (2017). Analyzing drivers of risks in electronic supply chains: a grey-DEMATEL approach. International Journal of Advanced Manufacturing Technology, 92(1-4), 1127-1145. https://doi.org/10.1007/s00170-017-0118-3

Rangel, D.A., De Oliveira, T.K., & Leite, M.S.A. (2015). Supply chain risk classification: Discussion and proposal. International Journal of Production Research, 53(22), 6868-6887. https://doi.org/10.1080/00207543.2014.910620

Samvedi, A., Jain, V., & Chan, F.T.S. (2013). Quantifying risks in a supply chain through integration of fuzzy AHP and fuzzy TOPSIS. International Journal of Production Research, 51(8), 2433-2442. https://doi.org/10.1080/00207543.2012.741330

Schoen, Q., Sanchis, R., Poler, R., Lauras, M., Fontanili, F., & Truptil, S. (2018). Categorisation of the main disruptive events in the sensitive products transportation supply chains. International Journal of Production Management and Engineering, 6(2), 79-89. https://doi.org/10.4995/ijpme.2018.10369

Shahbaz, M.S., RM Rasi, R.Z., & Bin Ahmad, M.F. (2019). A novel classification of supply chain risks: Scale development and validation. Journal of Industrial Engineering and Management, 12(1), 201. https://doi.org/10.3926/jiem.2792

Sharma, S., & Routroy, S. (2016). Modeling information risk in supply chain using Bayesian networks. Journal of Enterprise Information Management, 29(2), 238-254. https://doi.org/10.1108/JEIM-03-2014-0031

Sodhi, M.S., & Tang, C.S. (2012). Managing Supply Chain Risk. In Springer Science & Business Media. https://doi.org/10.1007/978-1-4614-3238-8

Sreedevi, R., Saranga, H., & Gouda, S.K. (2021). Impact of a country's logistical capabilities on supply chain risk. Supply Chain Management: An International Journal, https://doi.org/10.1108/SCM-09-2020-0504

Srivastava, M., & Rogers, H. (2021). Managing global supply chain risks: effects of the industry sector. International Journal of Logistics Research and Applications, 1-24.

Tarei, P.K., Thakkar, J.J., & Nag, B. (2018). A hybrid approach for quantifying supply chain risk and prioritizing the risk drivers: A case of Indian petroleum supply chain. Journal of Manufacturing Technology Management, 29(3), 533-569. https://doi.org/10.1108/JMTM-10-2017-0218

Trkman, P., & McCormack, K. (2009). Supply chain risk in turbulent environments-A conceptual model for managing supply chain network risk. International Journal of Production Economics, 119(2), 247-258. https://doi.org/10.1016/j.ijpe.2009.03.002

Tukamuhabwa, B.R., Stevenson, M., Busby, J., & Zorzini, M. (2015). Supply chain resilience: Definition, review and theoretical foundations for further study. International Journal of Production Research, 53(18), 5592-5623. https://doi.org/10.1080/00207543.2015.1037934

Tummala, R., & Schoenherr, T. (2011). Assessing and managing risks using the Supply Chain Risk Management Process (SCRMP). Supply Chain Management, 16(6), 474-483. https://doi.org/10.1108/13598541111171165

Venkatesh, V.G., Rathi, S., & Patwa, S. (2015). Analysis on supply chain risks in Indian apparel retail chains and proposal of risk prioritization model using Interpretive structural modeling. Journal of Retailing and Consumer Services, 26, 153-167. https://doi.org/10.1016/j.jretconser.2015.06.001

Wagner, S.M., & Bode, C. (2008). An empirical examination of supply chain performance along several dimensions of risk. Journal of Business Logistics, 29(1), 307-325. https://doi.org/10.1002/j.2158-1592.2008.tb00081.x

World Bank. 2022. The Impact of the War in Ukraine on Global Trade and Investment. Washington, DC. World Bank. https://openknowledge.worldbank.org/handle/10986/37359, License: CC BY 3.0 IGO

Zimmer, K., Fröhling, M., Breun, P., & Schultmann, F. (2017). Assessing social risks of global supply chains: A quantitative analytical approach and its application to supplier selection in the German automotive industry. Journal of Cleaner Production, 149, 96-109. https://doi.org/10.1016/j.jclepro.2017.02.041




How to Cite

Üstündağ, A., Çıkmak, S., Çankaya Eyiol, M., & Ungan, M. C. (2022). Evaluation of supply chain risks by fuzzy DEMATEL method: a case study of iron and steel industry in Turkey. International Journal of Production Management and Engineering, 10(2), 195–209. https://doi.org/10.4995/ijpme.2022.17169