Abstract
The Coronavirus outbreak and its different variants have damaged the global supply chains and affected suppliers for both goods and service providers unprecedentedly. The post-COVID-19 era could be considered full of uncertainty based on many changes that have happened. Some new parameters are introduced because of the outbreak and bring out new circumstances. These new challenges consequently will increase the ambiguity around the supply chain networks. This study is designed to investigate and evaluate the vagueness of supply chain networks in the post-COVID-19 time. The paper aims to study the strength of the SCN systems and find the related disruption patterns for each of the SCNs and then recommend appropriate strategies to increase the resilience of SCN systems. In the literature review part, we reviewed many articles that categorized the challenges. To catch the goal of evaluating the resilience of supply chain networks, some significant challenges are identified based on the literature part. An algorithm consists of three stages, first defining the uncertainty, second pattern recognition of disruption patterns, and third strategy recommender system to increase SCN resilience is proposed based on the SFS aggregation operator and logarithmic f-similarity measure. An illustrative example of the SCN resilience problem is evaluated by the proposed algorithm under the spherical fuzzy structure to show the applicability and reliability of the proposed method. Finally, this paper provides guidelines and strategies for increasing the resilience of supply chain networks in the post-COVID-19 outbreak.
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Donthu, N., Gustafsson, A.: Effects of COVID-19 on business and research (2020)
Verma, S., Gustafsson, A.: Investigating the emerging COVID-19 research trends in the field of business and management: a bibliometric analysis approach. J. Bus. Res. 118, 253–261 (2020). https://doi.org/10.1016/j.jbusres.2020.06.057
Ivanov, D., Dolgui, A.: Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability: a position paper motivated by COVID-19 outbreak. Int. J. Prod. Res. 58, 2904 (2020). https://doi.org/10.1080/00207543.2020.1750727
Govindan, K., Mina, H., Alavi, B.: A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: a case study of coronavirus disease 2019 (COVID-19). Transp. Res. Part E: Logist. Transp. Rev. 138, 101967 (2020). https://doi.org/10.1016/j.tre.2020.101967
Farid, F., Donyatalab, Y.: Novel spherical fuzzy eco-holonic concept in sustainable supply chain of aviation fuel. In: Kahraman, C., Aydın, S. (eds.) Intelligent and Fuzzy Techniques in Aviation 4.0. SSDC, vol. 372, pp. 201–235. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-75067-1_9
Lopes de Sousa Jabbour, A.B., Chiappetta Jabbour, C.J., Hingley, M., Vilalta-Perdomo, E.L., Ramsden, G., Twigg, D.: Sustainability of supply chains in the wake of the coronavirus (COVID-19/SARS-CoV-2) pandemic: lessons and trends. Mod. Supply Chain Res. Appl. 2, 117–122 (2020). https://doi.org/10.1108/mscra-05-2020-0011
Ivanov, D.: Predicting the impacts of epidemic outbreaks on global supply chains: a simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case. Transp. Res. Part E: Logist. Transp. Rev. 136, 101922 (2020). https://doi.org/10.1016/j.tre.2020.101922
Filho, W.L., Brandli, L.L., Salvia, A.L., Rayman-Bacchus, L., Platje, J.: COVID-19 and the UN sustainable development goals: threat to solidarity or an opportunity? Sustain. 12, 5343 (2020). https://doi.org/10.3390/su12135343
Raj, A., Mukherjee, A.A., de Sousa Jabbour, A.B.L., Srivastava, S.K.: Supply chain management during and post-COVID-19 pandemic: mitigation strategies and practical lessons learned. J. Bus. Res. 142, 1125–1139 (2022). https://doi.org/10.1016/J.JBUSRES.2022.01.037
Queiroz, M.M., Ivanov, D., Dolgui, A., Fosso Wamba, S.: Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Ann. Oper. Res. (2020). https://doi.org/10.1007/s10479-020-03685-7
van Remko, H.: Research opportunities for a more resilient post-COVID-19 supply chain – closing the gap between research findings and industry practice. Int. J. Oper. Prod. Manag. 40, 341–355 (2020). https://doi.org/10.1108/IJOPM-03-2020-0165
Golan, M.S., Jernegan, L.H., Linkov, I.: Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic (2020)
Raj, A., Dwivedi, G., Sharma, A., de Sousa Jabbour, A.B.L., Rajak, S.: Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: an inter-country comparative perspective. Int. J. Prod. Econ. 224, 10754 (2020). https://doi.org/10.1016/j.ijpe.2019.107546
Zadeh, L.A.: Fuzzy sets. Inf. Control. (1965). https://doi.org/10.1016/S0019-9958(65)90241-X
Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. (1986). https://doi.org/10.1016/S0165-0114(86)80034-3
Torra, V.: Hesitant fuzzy sets. Int. J. Intell. Syst. (2010). https://doi.org/10.1002/int.20418
Yager, R.R.: Pythagorean fuzzy subsets. In: 2013 Joint IFSA World Congress NAFIPS Annual Meeting, vol. 2, pp. 57–61 (2013). https://doi.org/10.1109/IFSA-NAFIPS.2013.6608375
Cuong, B.C., Kreinovich, V.: Picture fuzzy sets - a new concept for computational intelligence problems. In: 2013 3rd World Congress on Information and Communication Technologies, WICT 2013 (2014)
Yager, R.R.: Generalized orthopair fuzzy sets. IEEE Trans. Fuzzy Syst. 25, 1222–1230 (2017)
Gündoǧdu, F.K., Kahraman, C.: Spherical fuzzy sets and spherical fuzzy TOPSIS method. J. Intell. Fuzzy Syst. (2019). https://doi.org/10.3233/JIFS-181401
Donyatalab, Y., Farrokhizadeh, E., Garmroodi, S.D.S., Shishavan, S.A.S.: Harmonic mean aggregation operators in spherical fuzzy environment and their group decision making applications. J. Mult. Log. Soft Comput. 33, 565–592 (2019)
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Donyatalab, Y. (2022). Supply Chain Network (SCN) Resilient Pattern Recognition and Intelligent Strategy Recommender Approach for the Post-COVID-19 Era. In: Kahraman, C., Tolga, A.C., Cevik Onar, S., Cebi, S., Oztaysi, B., Sari, I.U. (eds) Intelligent and Fuzzy Systems. INFUS 2022. Lecture Notes in Networks and Systems, vol 505. Springer, Cham. https://doi.org/10.1007/978-3-031-09176-6_35
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