Supply Chain Risk Assessment of Steel Industry Using Hybrid Type Fuzzy TOPSIS Approach and Fuzzy Hierarchical Analysis

محورهای موضوعی : Data Engineering

Sayed Muhammad Reza Parpinchi ¹ ( Department of Industrial Management, Zanjan Branch, Islamic Azad University, Zanjan, Iran )
Amir Najafi ² ( Department of Industrial Engineering, Zanjan Branch, Islamic Azad University, Zanjan, Iran )
Nabiallah Mohammadi ³ ( Department of Industrial Management, Zanjan Branch, Islamic Azad University, Zanjan, Iran )

تاریخ دریافت : 1402/04/06 تاریخ پذیرش : 1402/10/18 تاریخ انتشار : 1401/12/10

کلید واژه: Supply Chain, Risk, Fuzzy hierarchy, steel industry, Fuzzy TOPSIS Type-2,

چکیده مقاله :

In recent years, supply chain management has become critical due to the globalization of business markets. Shorter product life cycles, the emergence of new technologies, increased supplier relationships, and product development push the supply chain toward complexity. As complexity increases, the level of uncertainty and risk in the supply chain also increases. Risk in the supply chain is a potential event that prevents the natural flow of materials and information in the supply chain, leading to disruption in the supply chain. A wide range of risks in the supply chain may negatively affect supply chain performance. Due to the close relationship between supply chain members, disruption or risk in any part of the supply chain affects the entire supply chain and disrupts its performance; therefore, to overcome supply chain risks, organizations must use appropriate strategies to manage and control them. It is an exploratory mix (qualitative-quantitative) with a modelling approach. Therefore, it seeks to provide a new model for measuring the risk of the steel industry in the supply chain using fuzzy hierarchy and type-2 fuzzy TOPSIS. The results show that the first three ranks of risks in the steel industry supply chain are "raw material supplier performance risk", "production planning risk”, and "information systems inconsistency risk", respectively. Therefore, it is suggested that a comprehensive and executive plan be designed and implemented to properly control and monitor these risks properly.

چکیده انگلیسی:

In recent years, supply chain management has become critical due to the globalization of business markets. Shorter product life cycles, the emergence of new technologies, increased supplier relationships, and product development push the supply chain toward complexity. As complexity increases, the level of uncertainty and risk in the supply chain also increases. Risk in the supply chain is a potential event that prevents the natural flow of materials and information in the supply chain, leading to disruption in the supply chain. A wide range of risks in the supply chain may negatively affect supply chain performance. Due to the close relationship between supply chain members, disruption or risk in any part of the supply chain affects the entire supply chain and disrupts its performance; therefore, to overcome supply chain risks, organizations must use appropriate strategies to manage and control them. It is an exploratory mix (qualitative-quantitative) with a modelling approach. Therefore, it seeks to provide a new model for measuring the risk of the steel industry in the supply chain using fuzzy hierarchy and type-2 fuzzy TOPSIS. The results show that the first three ranks of risks in the steel industry supply chain are "raw material supplier performance risk", "production planning risk”, and "information systems inconsistency risk", respectively. Therefore, it is suggested that a comprehensive and executive plan be designed and implemented to properly control and monitor these risks properly.

منابع و مأخذ:

[1]. Azar, Adel., Rajabzadeh, Ali. (2002). Applied decisions MADM approach Publish knowledge look.
[2]. Al-Ibrahim, Nader; Nasiri, Javad,. (2001). Global Market and Home Appliance Industry of Iran, Presented at the Third Conference of Research and Development Centers of Industries and Mines, 700-689.
[3]. Ibrahim Nejad, Saadullah; Mousavi, Seyed Meysam; Ghorbani Kia, Arash (2007). Identification and analysis of risks of supply chain in the framework of fuzzy multi-criteria decision making, the first international conference on supply chain management and information systems
[4]. Ahmadi, Hussein (2005). Supply Chain Management, Iran Industrial Training and Research Center, First Edition.
[5]. Asgharpour, Mohammad Javad. (1376). Advanced Operations Research. Tehran: University of Tehran Press.
[6]. Asgharpour, Mohammad Javad (2011). Multi-criteria decision making, University of Tehran Press, Tehran.
[7]. Asgharpour, Mohammad Javad. (2010). Multi-criteria decisions. Tehran: University of Tehran Press, eighth edition.
[8]. Baharestani, Pedram, Rezaei Nik, Ebrahim, (2017), Presenting a model for evaluating and ranking supply chain risk responses using the combined DEMATEL-ANP method in a fuzzy environment. First International Conference on Systems Optimization and Business Management ( 2017).
[9]. Heidari Qarabolagh, Hadi. (2008). Supply Chain Management Implementation Model in Small and Medium Enterprises, Parks and Growth Quarterly, No. 17, 43-50
[10]. Khalili Iraqi, Maryam (2005). Credit risk management using decision models, Economic Research Journal, No. 16, 183 - 212.
[11]. Rajab Adeh Ali, Khadivar Ameneh and Abdolazim Kazemi (2007). Investigating the effect of supply chain model on improving the quality of customer service and compiling the main components, Quarterly Journal of Business Research, No. 43, pp. 185-223.
[12]. Zand Hesami, Hesam and Ava, Savoji (2012). Risk Management in Supply Chain Management, Development and Transformation Management Quarterly, 9, 37-44.
[13]. Sramad, Zohreh, Bazargan, Abbas and Elahe Hejazi (2011). Research Methods in Behavioral Sciences. Twenty-first edition, Tehran, Ad Publishing.
[14]. Adalat Sarvestani, Mohammad, Shahraki, Mohammadreza (2015), Comparison and ranking of effective factors in risk management of supply chain by fuzzy I method and Jack Knife method with interval analysis. Article 9 Volume 4, Number 2, Fall and Winter 2015, pp. 117-107.
[15]. Shahbandarzadeh, Hamid; Mosalla Nejad, Leila. (2012). Presenting a hierarchical model to identify the factors affecting risk management in the supply chain, the third national conference on industrial and system engineering.
[16]. Mania, Ali, (2017), Identifying and ranking the factors affecting the risk of the leasing industry by the Analytic Hierarchy Process (AHP) (Case study of Vespari Mellat Company (Public Joint Stock Company), Quarterly Journal of Investment Knowledge Research Year 6 / Number 24 / Winter 2017.
[17]. Roodpashti Guide, Fereydoun, Hamed Tajmir Riahi, Farzaneh Ash'arion Qomizadeh, (2016), Ranking of Tehran Stock Exchange Industries Based on Risk Criteria from the Perspective of Institutional Investors; Data Envelopment Analysis (DEA), Asset Management and Financing Approach, Fourth Summer 2016 No. 2 (3 in a row)
[18]. Fakursiyeh, Amir Mohammad, Zahra, Ulfat; (2014), Risk management of supply chain, Identifying and Dealing with Zaba Damage Points Using Fuzzy TOPSIS, Journal of Tomorrow Management / Year 13 / Issue / Spring 2014.
[19]. Abdullah, L., &Najib, L. (2014). A new type-2 fuzzy set of linguistic variables for the fuzzy analytic hierarchy process. Expert Systems with Applications, 41, 3297–3305.
[20]. Abdullah, L., Sunadia, J., & Imran, T. (2009). A new analytic hierarchy process in multi-attribute group decision making. International Journal of Soft Computing, 4(5), 208–2014.
[21]. Asgarpour, M. J., (2008). Multiple criteria decision making (8rd ed.). University of Tehran press, 2008.
[22]. Azar, A., Rajabzadeh, A., (2002). Applied decision making (Eds.). NegaheDanesh Publisher: Tehran.
[23]. Azaron, A., Brown, K. N., Tarim, S. A., Modarres, M., (2008). A multi-objective stochastic programming approach for supply chain design considering risk. Production Economics, 116(1), 129-138.
[24]. Baas, S. M., &Kwakernaak, H. (1977). Rating and ranking of multiple aspect alternative using fuzzy sets. Automatica, 13, 47–58.
[25]. barriers. Expert Systems with Applications, 41, 679–693.
[26]. Bartolome, M. J., & Mercedes, U. G. (2013). Human resource management approaches in Spanish hotels: An introductory analysis. International Journal of Hospitality Management, 35, 339–347.
[27]. Fontela, E., &Gabus, A. (1976). The DEMATEL observer, DEMATEL 1976 report. Switzerland Geneva: Battelle Geneva Research Center.for science and technology (HRST) based on an integrated fuzzy AHP and fuzzy DEMATEL approach. Applied Soft Computing, 12, 64–71.
[28]. Gabus, A. &Fontela, E. (1973). Perceptions of the world problematique: Communication procedure, communicating with those bearing collective responsibility (DEMATEL report no. 1. Battelle Geneva Research Centre, Switzerland Geneva.
[29]. Kleindorfer, P. R., Saad. G. H., (2005). Managing Disruption Risks in Supply Chains. Production and Operations Management, 14 (1), 53-58.
[30]. Lee, A. H. I. (2009). A fuzzy supplier selection model with the consideration of
[31]. Lee, E. S., & Li, R. L. (1988). Comparison of fuzzy numbers based on the probability
[32]. Lin, C. L., & Wu, W. W. (2004). A fuzzy extension of the DEMATEL method for group
[33]. Lin, R. J. (2013). Using fuzzy DEMATEL to evaluate the green supply chain
[34]. Liou, J. J. H., Tzeng, G. H., & Chang, H. C. (2007). Airline safety measurements using a
[35]. Liu, Z., Lai, M., Zhou, T., Zhou, Y., (2009). A Supply Chain Risk Assessment Model Based on Multistage Influence Diagram. 6th International Conference on Service Systems and Service Management, 8-10 June, Pp. 72-75. IEEE, E-ISBN: 978-1-4244-3662-0.
[36]. Mahdavi, I., Mahdavi-Amiri, N., Heidarzade, A., Nourifar, R., (2008). Designing a model of fuzzy TOPSIS in multiple criteria decision making. Applied Mathematics and Computation, 206 (2), 607–617.
[37]. Manuj, I., Mentzer, J. T., (2008). Global risk management of supply chain strategies. International Journal of Physical Distribution & Logistics Management, 38 (3), 192-223
[38]. Marbini, A. H., &Tavana, M. (2011). An extension of the Electre I method for group Mathematical Modeling, 37, 4948–4971.
[39]. Matook, S., Lasch, R., Tamaschke, R., (2009). Supplier development with benchmarking as part of a comprehensive supplier risk management framework. International Journal of Operations & Production Management, 29 (3), 241-267.
[40]. Patil, S. K., & Kant, R. (2013). A fuzzy AHP-TOPSIS framework for ranking the
[41]. Shaw, K., Shankar, R., Yadav, S. S., & Thakur, L. S. (2012). Supplier selection using simple. IEEE Transactions on Fuzzy Systems, 14(6), 808–821.
[42]. JuditNagy,Lóránt Venter,(2018), How risk management in supply chains affects supply chain performance? The participation in the conference is supported by TÁMOP-4.2.1/B-09/1/KMR-2018-005.
[43]. Wu, W. W. (2012). Segmenting critical factors for successful knowledge management implementation using the fuzzy DEMATEL method. Applied Soft Computing, 12, 527–535.
[44]. Yeap, J. A. L., Ignatius, J., &Ramayah, T. (2014). Determining consumers’ most preferred eWOM platform for movie reviews: A fuzzy analytic hierarchy process approach. Computers in Human Behavior, 31, 250–258.
[45]. Yan Coelho Albertin ,(2017),RISK MANAGEMENT OF SUPPLY CHAIN , Understanding and Facing the Main Risks on the Chain Bachelor’s thesis Logistics Engineering ,2017.
[46]. Zhang, Z., & Zhang, S. (2013). A novel approach to multi attribute group decision
[47]. Zhao, S., & Du, J. (2012). Thirty-two years of development of human resource management in China: Review and prospects. Human Resource Management Review, 22(3), 179–188.
[48]. Zheng, G., Zhu, N., Tian, Z., Chen, Y., & Sun, B. (2012). Application of trapezoidal fuzzy AHP method for work safety evaluation and early warning rating of hot and humid environments. Safety Science, 50, 228–239
[49]. Abbas Nasiri et al., Ayandegan Institute of Higher Education; International Journal of Research in Industrial Engineering, Vol. 11, Issue 2.
[50]. Mohammad Ghasempoor Anaraki et al., Research Expansion Alliance (REA) on behalf of Ayandegan Institute of Higher Education; Journal of Fuzzy Extension and Applications, Issn 2783-1442, Vol. 2, Issue 1.
[51]. Ayandegan Institute of Higher Education; International Journal of Research in Industrial Engineering, Issn 2783-1337, Vol. 10. Issue 4.
[52]. Ayandegan Institute of Higher Education; International Journal of Research in Industrial Engineering, Issn 2783-1337, Vol. 10. Issue 3.
[53]. Research Expansion Alliance (REA) on behalf of Ayandegan Institute of Higher Education; Journal of Fuzzy Extension and Applications, Issn 2783-1442, Volu.1, Issue 1.
[54]. Cigdem Sıcakyuz; Department of Industrial Engineering, Ankara Science University, 06570 Ankara, Turkey, Journal of Operational and Strategic Analytics, Vol. 1, Issue 1, 2023, Pages 14-24.
[55]. Research Expansion Alliance (REA) on behalf of Ayandegan Institute of Higher Education, Journal of Applied Research on Industrial Engineering, Issn 2538-5100, Vol. 10, Issue 3.
[56]. Amulya Gurtu & Jestin Johny, 2021. "Risk management of supply chain: Literature Review," Risks, MDPI, vol. 9(1), pages 1-16, January.