- -

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

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

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

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: UstundaCkmakCankaya ...
Tamaño: 1.181Mb
Formato: PDF
Descripción: Versión editorial
Abrir
dc.contributor.author Üstündağ, Asuman es_ES
dc.contributor.author Çıkmak, Sinan es_ES
dc.contributor.author Çankaya Eyiol, Merve es_ES
dc.contributor.author Ungan, Mustafa Cahit es_ES
dc.date.accessioned 2022-09-12T11:24:11Z
dc.date.available 2022-09-12T11:24:11Z
dc.date.issued 2022-07-29
dc.identifier.uri http://hdl.handle.net/10251/185807
dc.description.abstract [EN] 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. es_ES
dc.language Inglés es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.ispartof International Journal of Production Management and Engineering es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Fuzzy DEMATEL es_ES
dc.subject Iron and steel industry es_ES
dc.subject Supply chain risk management es_ES
dc.subject Risk assessment es_ES
dc.title Evaluation of supply chain risks by fuzzy DEMATEL method: a case study of iron and steel industry in Turkey es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.4995/ijpme.2022.17169
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Üstündağ, A.; Çıkmak, S.; Çankaya Eyiol, M.; Ungan, MC. (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 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.4995/ijpme.2022.17169 es_ES
dc.description.upvformatpinicio 195 es_ES
dc.description.upvformatpfin 209 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 10 es_ES
dc.description.issue 2 es_ES
dc.identifier.eissn 2340-4876
dc.relation.pasarela OJS\17169 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references Chopra, S., & Sodhi, M.M.S. (2004). Managing risk to avoid supply-chain breakdown. MIT Sloan Management Review, 46(1), 53-61. es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references Gurtu, A., & Johny, J. (2021). Supply chain risk management: Literature review. Risks, 9(1), 16. https://doi.org/10.3390/risks9010016 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references Hopkin, P. (2018). Fundamentals of Risk Management: Understanding, Evaluating and Implementing Effective Risk Management (5th Edition). Kogan Page. es_ES
dc.description.references 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 es_ES
dc.description.references Iron Steel Sector Report. (2020). https://www.sanayi.gov.tr/assets/pdf/plan-program/DemirÇelikSektörRaporu2020.pdf (In Turkish) es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references Srivastava, M., & Rogers, H. (2021). Managing global supply chain risks: effects of the industry sector. International Journal of Logistics Research and Applications, 1-24. es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES
dc.description.references 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 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem