Transport risks in the supply chains – Post COVID-19 challenges
Abstract
The COVID-19 pandemic has caused major disruptions in global supply chains with unforeseen and unpredictable consequences. However, the pandemic was not the only reason why supply chain risk management has become more crucial than ever before. In the last decade, the occurrence of previously merely theoretical risks has emphasised the importance of risk management in supply chains. This has increased interest in risk assessment and management, COVID-19 and other disaster impact studies and proposals for more stable and resilient supply chains. This article addresses the problem of transport risk in supply chains in the context of COVID-19. Particular attention is paid to quantitative approaches. Identifying and quantifying risks and modelling their interdependencies contribute to the stability of the supply chains. The analysis presents the current state of knowledge and can serve as a guide for further research. It highlights transport risk management in supply chain management as an important area of investigation. In light of the challenges of the COVID-19 pandemic, the article proposes an approach to transportation risk assessment based on quantitative assessment and interconnection of risk factors.
Keyword : supply chain, logistics, risk, management, COVID-19, transport, assessment, Data Envelopment Analysis
How to Cite
Chodakowska, E., Bazaras, D., Sokolovskij, E., Kuranovic, V., & Ustinovichius, L. (2024). Transport risks in the supply chains – Post COVID-19 challenges. Journal of Business Economics and Management, 25(2), 211–225. https://doi.org/10.3846/jbem.2024.21110
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Abdzadeh, B., Noori, S., & Ghannadpour, S. F. (2023). A comprehensive mathematical model for quality integration in a project supply chain with concentrating on material flow and transportation. Advanced Engineering Informatics, 57, Article 102034. https://doi.org/10.1016/j.aei.2023.102034
Akbar, U., Popp, J., Khan, H., Khan, M. A., & Oláh, J. (2020). Energy efficiency in transportation along with the belt and road countries. Energies, 13(10), Article 2607. https://doi.org/10.3390/en13102607
Al Haji, G. (2005). Towards a road safety development index (RSDI): Development of an international index to measure road safety performance. Linköping Studies in Science and Technology. Licentiate No. 1174. Linköping University, Sweden.
Alhawari, S., Karadsheh, L., Nehari Talet, A., & Mansour, E. (2012). Knowledge-based risk management framework for information technology project. International Journal of Information Management, 32(1), 50–65. https://doi.org/10.1016/j.ijinfomgt.2011.07.002
Allach, S., Benamrou, B., Ahmed, M. B., Boudhir, A. A., & Ouardouz, M. (2019). A new architecture based on ARIMA models for the safety classification of inter-city routes using meteorological metrics. In Proceedings of the 4th International Conference on Smart City Applications (pp. 1–9). Association for Computing Machinery. https://doi.org/10.1145/3368756.3369067
Azad, N., Saharidis, G. K. D., Davoudpour, H., Malekly, H., & Yektamaram, S. A. (2013). Strategies for protecting supply chain networks against facility and transportation disruptions: An improved Benders decomposition approach. Annals of Operations Research, 210(1), 125–163. https://doi.org/10.1007/s10479-012-1146-x
Azadi, M., Kazemi Matin, R., Emrouznejad, A., & Ho, W. (2022). Evaluating sustainably resilient supply chains: A stochastic double frontier analytic model considering Netzero. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04813-1
Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: State of the art and future research directions. International Journal of Production Research, 57(7), 2179–2202. https://doi.org/10.1080/00207543.2018.1530476
Batarlienė, N. (2008). Risk analysis and assessment for transportation of dangerous freight. Transport, 23(2), 98–103. https://doi.org/10.3846/1648-4142.2008.23.98-103
Batarlienė, N. (2018). Risk and damage assessment for transportation of dangerous freight. Transport and Telecommunication Journal, 19(4), 356–363. https://doi.org/10.2478/ttj-2018-0030
Bugert, N., & Lasch, R. (2018). Supply chain disruption models: A critical review. Logistics Research, 11(5), 1–35. https://doi.org/10.23773/2018_5
Chodakowska, E., & Nazarko, J. (2020). Assessing the performance of sustainable development goals of EU countries: Hard and soft data integration. Energies, 13(13), Article 3439. https://doi.org/10.3390/en13133439
Chodakowska, E., Nazarko, J., Nazarko, Ł., Rabayah, H. S., Abendeh, R. M., & Alawneh, R. (2023). ARIMA models in solar radiation forecasting in different geographic locations. Energies, 16(13), Article 5029. https://doi.org/10.3390/en16135029
Dunn, J. E. (2021). COVID-19 and supply chains: A year of evolving disruption. Cleveland Fed District Data Briefs. Federal Reserve Bank of Cleveland. https://doi.org/10.26509/frbc-ddb-20210226
Emrouznejad, A., Abbasi, S., & Sıcakyüz, Ç. (2023). Supply chain risk management: A content analysis-based review of existing and emerging topics. Supply Chain Analytics, 3, Article 100031. https://doi.org/10.1016/j.sca.2023.100031
Erkhembayar, R., Dickinson, E., Badarch, D., Narula, I., Warburton, D., Thomas, G. N., Ochir, C., & Manaseki-Holland, S. (2020). Early policy actions and emergency response to the COVID-19 pandemic in Mongolia: Experiences and challenges. The Lancet Global Health, 8(9), e1234–e1241. https://doi.org/10.1016/S2214-109X(20)30295-3
Eurostat. (n.d.). https://ec.europa.eu/eurostat/
Fan, S., & Yang, Z. (2022). Safety and security co-analysis in transport systems: Current state and regulatory development. Transportation Research Part A: Policy and Practice, 166, 369–388. https://doi.org/10.1016/j.tra.2022.11.005
Gitelman, V., Doveh, E., & Hakkert, S. (2010). Designing a composite indicator for road safety. Safety Science, 48(9), 1212–1224. https://doi.org/10.1016/j.ssci.2010.01.011
Gu, B., & Liu, J. (2023). A systematic review of resilience in the maritime transport. International Journal of Logistics Research and Applications. https://doi.org/10.1080/13675567.2023.2165051
Gupta, S., Modgil, S., Meissonier, R., & Dwivedi, Y. K. (2022). Artificial intelligence and information system resilience to cope with supply chain disruption. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2021.3116770
Haque, Md. S., Uddin, S., Sayem, S. Md., & Mohib, K. M. (2021). Coronavirus disease 2019 (COVID-19) induced waste scenario: A short overview. Journal of Environmental Chemical Engineering, 9(1), Article 104660. https://doi.org/10.1016/j.jece.2020.104660
Hermans, E., Van Den Bossche, F., & Wets, G. (2008). Combining road safety information in a performance index. Accident Analysis & Prevention, 40(4), 1337–1344. https://doi.org/10.1016/j.aap.2008.02.004
Ho, S.-J., Xing, W., Wu, W., & Lee, C.-C. (2021). The impact of COVID-19 on freight transport: Evidence from China. MethodsX, 8, Article 101200. https://doi.org/10.1016/j.mex.2020.101200
Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part E: Logistics and Transportation Review, 125, 285–307. https://doi.org/10.1016/j.tre.2019.03.001
International Organization for Standardization. (2018). Risk Management – Guidelines (ISO 31000:2018).
Ivanov, D., & Das, A. (2020). Coronavirus (COVID-19/SARS-CoV-2) and supply chain resilience: A research note. International Journal of Integrated Supply Management, 13(1), Article 90. https://doi.org/10.1504/IJISM.2020.107780
Ivanov, D., Dolgui, A., Sokolov, B., & Ivanova, M. (2017). Literature review on disruption recovery in the supply chain. International Journal of Production Research, 55(20), 6158–6174. https://doi.org/10.1080/00207543.2017.1330572
Kiani Mavi, R., Kiani Mavi, N., Olaru, D., Biermann, S., & Chi, S. (2022). Innovations in freight transport: A systematic literature evaluation and COVID implications. The International Journal of Logistics Management, 33(4), 1157–1195. https://doi.org/10.1108/IJLM-07-2021-0360
Kogler, C., & Rauch, P. (2023). Lead time and quality driven transport strategies for the wood supply chain. Research in Transportation Business & Management, 47, Article 100946. https://doi.org/10.1016/j.rtbm.2023.100946
Kraude, R., Narayanan, S., & Talluri, S. (2022). Evaluating the performance of supply chain risk mitigation strategies using network data envelopment analysis. European Journal of Operational Research, 303(3), 1168–1182. https://doi.org/10.1016/j.ejor.2022.03.016
Li, Q., Bai, Q., Hu, A., Yu, Z., & Yan, S. (2022). How does COVID-19 affect traffic on highway network: Evidence from Yunnan Province, China. Journal of Advanced Transportation, 2022, 1–23. https://doi.org/10.1155/2022/7379334
Loske, D. (2020). The impact of COVID-19 on transport volume and freight capacity dynamics: An empirical analysis in German food retail logistics. Transportation Research Interdisciplinary Perspectives, 6, Article 100165. https://doi.org/10.1016/j.trip.2020.100165
Mansour, M. A., Beithou, N., Alsqour, M., Tarawneh, S. A., Rababa’a, K. A., AlSaqoor, S., & Chodakowska, E. (2023). Hierarchical risk communication management framework for construction projects. Engineering Management in Production and Services, 15(4), 104–115. https://doi.org/10.2478/emj-2023-0031
Nazarko, J., & Chodakowska, E. (2020). Assessing the performance of Polish regional funds for environmental protection and water management using DEA model. MATEC Web of Conferences, 312, Article 01001. https://doi.org/10.1051/matecconf/202031201001
Nazarko, J., Chodakowska, E., & Nazarko, Ł. (2022). Evaluating the transition of the European Union member states towards a circular economy. Energies, 15(11), Article 3924. https://doi.org/10.3390/en15113924
Nazarko, J., Jurczuk, A., & Zalewski, W. (2005). ARIMA models in load modelling with clustering approach. In 2005 IEEE Russia Power Tech (pp. 1–6). IEEE. https://doi.org/10.1109/PTC.2005.4524719
Perkumienė, D., Pranskūnienė, R., Vienažindienė, M., & Grigienė, J. (2020). The right to a clean environment: Considering green logistics and sustainable tourism. International Journal of Environmental Research and Public Health, 17(9), Article 3254. https://doi.org/10.3390/ijerph17093254
Pires Ribeiro, J., & Barbosa-Povoa, A. (2018). Supply chain resilience: Definitions and quantitative modelling approaches – A literature review. Computers & Industrial Engineering, 115, 109–122. https://doi.org/10.1016/j.cie.2017.11.006
Rapaccini, M., Saccani, N., Kowalkowski, C., Paiola, M., & Adrodegari, F. (2020). Navigating disruptive crises through service-led growth: The impact of COVID-19 on Italian manufacturing firms. Industrial Marketing Management, 88, 225–237. https://doi.org/10.1016/j.indmarman.2020.05.017
Shareef, M. A., Dwivedi, Y. K., Kumar, V., Hughes, D. L., & Raman, R. (2022). Sustainable supply chain for disaster management: Structural dynamics and disruptive risks. Annals of Operations Research, 319(1), 1451–1475. https://doi.org/10.1007/s10479-020-03708-3
Sharma, S. K., & Bhat, A. (2012). Identification and assessment of supply chain risk: Development of AHP model for supply chain risk prioritisation. International Journal of Agile Systems and Management, 5(4), 350–369. https://doi.org/10.1504/IJASM.2012.050155
Shekarian, M., & Mellat Parast, M. (2021). An Integrative approach to supply chain disruption risk and resilience management: A literature review. International Journal of Logistics Research and Applications, 24(5), 427–455. https://doi.org/10.1080/13675567.2020.1763935
Subramanya, K., & Kermanshachi, S. (2021). Impact of COVID-19 on transportation industry: Comparative analysis of road, air, and rail transportation modes. In International Conference on Transportation and Development 2021 (pp. 230–242). https://doi.org/10.1061/9780784483534.020
Taghizadeh, E., & Venkatachalam, S. (2023). Two-stage risk-averse stochastic programming approach for multi-item single source ordering problem: CVaR minimisation with transportation cost. International Journal of Production Research, 61(7), 2129–2146. https://doi.org/10.1080/00207543.2022.2060770
Tang, C. S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics, 103(2), 451–488. https://doi.org/10.1016/j.ijpe.2005.12.006
Ulutaş, A., Meidute-Kavaliauskiene, I., Topal, A., & Demir, E. (2021). Assessment of collaboration-based and non-collaboration-based logistics risks with plithogenic SWARA method. Logistics, 5(4), Article 82. https://doi.org/10.3390/logistics5040082
World Economic Forum. (2023). The Global Risks Report 2023 (18 ed.). https://www.weforum.org/publications/global-risks-report-2023/
Xiang, S., Rasool, S., Hang, Y., Javid, K., Javed, T., & Artene, A. E. (2021). The effect of COVID-19 pandemic on service sector sustainability and growth. Frontiers in Psychology, 12, Article 633597. https://doi.org/10.3389/fpsyg.2021.633597
Xu, Z., Elomri, A., Kerbache, L., & El Omri, A. (2020). Impacts of COVID-19 on global supply chains: Facts and perspectives. IEEE Engineering Management Review, 48(3), 153–166. https://doi.org/10.1109/EMR.2020.3018420
Yan, R., Yang, Y., & Du, Y. (2023). Stochastic optimization model for ship inspection planning under uncertainty in maritime transportation. Electronic Research Archive, 31(1), 103–122. https://doi.org/10.3934/era.2023006
Yang, M., Lim, M. K., Qu, Y., Ni, D., & Xiao, Z. (2023). Supply chain risk management with machine learning technology: A literature review and future research directions. Computers & Industrial Engineering, 175, Article 108859. https://doi.org/10.1016/j.cie.2022.108859
Zeng, Z., Chen, P.-J., & Lew, A. A. (2020). From high-touch to high-tech: COVID-19 drives robotics adoption. Tourism Geographies, 22(3), 724–734. https://doi.org/10.1080/14616688.2020.1762118
Akbar, U., Popp, J., Khan, H., Khan, M. A., & Oláh, J. (2020). Energy efficiency in transportation along with the belt and road countries. Energies, 13(10), Article 2607. https://doi.org/10.3390/en13102607
Al Haji, G. (2005). Towards a road safety development index (RSDI): Development of an international index to measure road safety performance. Linköping Studies in Science and Technology. Licentiate No. 1174. Linköping University, Sweden.
Alhawari, S., Karadsheh, L., Nehari Talet, A., & Mansour, E. (2012). Knowledge-based risk management framework for information technology project. International Journal of Information Management, 32(1), 50–65. https://doi.org/10.1016/j.ijinfomgt.2011.07.002
Allach, S., Benamrou, B., Ahmed, M. B., Boudhir, A. A., & Ouardouz, M. (2019). A new architecture based on ARIMA models for the safety classification of inter-city routes using meteorological metrics. In Proceedings of the 4th International Conference on Smart City Applications (pp. 1–9). Association for Computing Machinery. https://doi.org/10.1145/3368756.3369067
Azad, N., Saharidis, G. K. D., Davoudpour, H., Malekly, H., & Yektamaram, S. A. (2013). Strategies for protecting supply chain networks against facility and transportation disruptions: An improved Benders decomposition approach. Annals of Operations Research, 210(1), 125–163. https://doi.org/10.1007/s10479-012-1146-x
Azadi, M., Kazemi Matin, R., Emrouznejad, A., & Ho, W. (2022). Evaluating sustainably resilient supply chains: A stochastic double frontier analytic model considering Netzero. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04813-1
Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: State of the art and future research directions. International Journal of Production Research, 57(7), 2179–2202. https://doi.org/10.1080/00207543.2018.1530476
Batarlienė, N. (2008). Risk analysis and assessment for transportation of dangerous freight. Transport, 23(2), 98–103. https://doi.org/10.3846/1648-4142.2008.23.98-103
Batarlienė, N. (2018). Risk and damage assessment for transportation of dangerous freight. Transport and Telecommunication Journal, 19(4), 356–363. https://doi.org/10.2478/ttj-2018-0030
Bugert, N., & Lasch, R. (2018). Supply chain disruption models: A critical review. Logistics Research, 11(5), 1–35. https://doi.org/10.23773/2018_5
Chodakowska, E., & Nazarko, J. (2020). Assessing the performance of sustainable development goals of EU countries: Hard and soft data integration. Energies, 13(13), Article 3439. https://doi.org/10.3390/en13133439
Chodakowska, E., Nazarko, J., Nazarko, Ł., Rabayah, H. S., Abendeh, R. M., & Alawneh, R. (2023). ARIMA models in solar radiation forecasting in different geographic locations. Energies, 16(13), Article 5029. https://doi.org/10.3390/en16135029
Dunn, J. E. (2021). COVID-19 and supply chains: A year of evolving disruption. Cleveland Fed District Data Briefs. Federal Reserve Bank of Cleveland. https://doi.org/10.26509/frbc-ddb-20210226
Emrouznejad, A., Abbasi, S., & Sıcakyüz, Ç. (2023). Supply chain risk management: A content analysis-based review of existing and emerging topics. Supply Chain Analytics, 3, Article 100031. https://doi.org/10.1016/j.sca.2023.100031
Erkhembayar, R., Dickinson, E., Badarch, D., Narula, I., Warburton, D., Thomas, G. N., Ochir, C., & Manaseki-Holland, S. (2020). Early policy actions and emergency response to the COVID-19 pandemic in Mongolia: Experiences and challenges. The Lancet Global Health, 8(9), e1234–e1241. https://doi.org/10.1016/S2214-109X(20)30295-3
Eurostat. (n.d.). https://ec.europa.eu/eurostat/
Fan, S., & Yang, Z. (2022). Safety and security co-analysis in transport systems: Current state and regulatory development. Transportation Research Part A: Policy and Practice, 166, 369–388. https://doi.org/10.1016/j.tra.2022.11.005
Gitelman, V., Doveh, E., & Hakkert, S. (2010). Designing a composite indicator for road safety. Safety Science, 48(9), 1212–1224. https://doi.org/10.1016/j.ssci.2010.01.011
Gu, B., & Liu, J. (2023). A systematic review of resilience in the maritime transport. International Journal of Logistics Research and Applications. https://doi.org/10.1080/13675567.2023.2165051
Gupta, S., Modgil, S., Meissonier, R., & Dwivedi, Y. K. (2022). Artificial intelligence and information system resilience to cope with supply chain disruption. IEEE Transactions on Engineering Management. https://doi.org/10.1109/TEM.2021.3116770
Haque, Md. S., Uddin, S., Sayem, S. Md., & Mohib, K. M. (2021). Coronavirus disease 2019 (COVID-19) induced waste scenario: A short overview. Journal of Environmental Chemical Engineering, 9(1), Article 104660. https://doi.org/10.1016/j.jece.2020.104660
Hermans, E., Van Den Bossche, F., & Wets, G. (2008). Combining road safety information in a performance index. Accident Analysis & Prevention, 40(4), 1337–1344. https://doi.org/10.1016/j.aap.2008.02.004
Ho, S.-J., Xing, W., Wu, W., & Lee, C.-C. (2021). The impact of COVID-19 on freight transport: Evidence from China. MethodsX, 8, Article 101200. https://doi.org/10.1016/j.mex.2020.101200
Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part E: Logistics and Transportation Review, 125, 285–307. https://doi.org/10.1016/j.tre.2019.03.001
International Organization for Standardization. (2018). Risk Management – Guidelines (ISO 31000:2018).
Ivanov, D., & Das, A. (2020). Coronavirus (COVID-19/SARS-CoV-2) and supply chain resilience: A research note. International Journal of Integrated Supply Management, 13(1), Article 90. https://doi.org/10.1504/IJISM.2020.107780
Ivanov, D., Dolgui, A., Sokolov, B., & Ivanova, M. (2017). Literature review on disruption recovery in the supply chain. International Journal of Production Research, 55(20), 6158–6174. https://doi.org/10.1080/00207543.2017.1330572
Kiani Mavi, R., Kiani Mavi, N., Olaru, D., Biermann, S., & Chi, S. (2022). Innovations in freight transport: A systematic literature evaluation and COVID implications. The International Journal of Logistics Management, 33(4), 1157–1195. https://doi.org/10.1108/IJLM-07-2021-0360
Kogler, C., & Rauch, P. (2023). Lead time and quality driven transport strategies for the wood supply chain. Research in Transportation Business & Management, 47, Article 100946. https://doi.org/10.1016/j.rtbm.2023.100946
Kraude, R., Narayanan, S., & Talluri, S. (2022). Evaluating the performance of supply chain risk mitigation strategies using network data envelopment analysis. European Journal of Operational Research, 303(3), 1168–1182. https://doi.org/10.1016/j.ejor.2022.03.016
Li, Q., Bai, Q., Hu, A., Yu, Z., & Yan, S. (2022). How does COVID-19 affect traffic on highway network: Evidence from Yunnan Province, China. Journal of Advanced Transportation, 2022, 1–23. https://doi.org/10.1155/2022/7379334
Loske, D. (2020). The impact of COVID-19 on transport volume and freight capacity dynamics: An empirical analysis in German food retail logistics. Transportation Research Interdisciplinary Perspectives, 6, Article 100165. https://doi.org/10.1016/j.trip.2020.100165
Mansour, M. A., Beithou, N., Alsqour, M., Tarawneh, S. A., Rababa’a, K. A., AlSaqoor, S., & Chodakowska, E. (2023). Hierarchical risk communication management framework for construction projects. Engineering Management in Production and Services, 15(4), 104–115. https://doi.org/10.2478/emj-2023-0031
Nazarko, J., & Chodakowska, E. (2020). Assessing the performance of Polish regional funds for environmental protection and water management using DEA model. MATEC Web of Conferences, 312, Article 01001. https://doi.org/10.1051/matecconf/202031201001
Nazarko, J., Chodakowska, E., & Nazarko, Ł. (2022). Evaluating the transition of the European Union member states towards a circular economy. Energies, 15(11), Article 3924. https://doi.org/10.3390/en15113924
Nazarko, J., Jurczuk, A., & Zalewski, W. (2005). ARIMA models in load modelling with clustering approach. In 2005 IEEE Russia Power Tech (pp. 1–6). IEEE. https://doi.org/10.1109/PTC.2005.4524719
Perkumienė, D., Pranskūnienė, R., Vienažindienė, M., & Grigienė, J. (2020). The right to a clean environment: Considering green logistics and sustainable tourism. International Journal of Environmental Research and Public Health, 17(9), Article 3254. https://doi.org/10.3390/ijerph17093254
Pires Ribeiro, J., & Barbosa-Povoa, A. (2018). Supply chain resilience: Definitions and quantitative modelling approaches – A literature review. Computers & Industrial Engineering, 115, 109–122. https://doi.org/10.1016/j.cie.2017.11.006
Rapaccini, M., Saccani, N., Kowalkowski, C., Paiola, M., & Adrodegari, F. (2020). Navigating disruptive crises through service-led growth: The impact of COVID-19 on Italian manufacturing firms. Industrial Marketing Management, 88, 225–237. https://doi.org/10.1016/j.indmarman.2020.05.017
Shareef, M. A., Dwivedi, Y. K., Kumar, V., Hughes, D. L., & Raman, R. (2022). Sustainable supply chain for disaster management: Structural dynamics and disruptive risks. Annals of Operations Research, 319(1), 1451–1475. https://doi.org/10.1007/s10479-020-03708-3
Sharma, S. K., & Bhat, A. (2012). Identification and assessment of supply chain risk: Development of AHP model for supply chain risk prioritisation. International Journal of Agile Systems and Management, 5(4), 350–369. https://doi.org/10.1504/IJASM.2012.050155
Shekarian, M., & Mellat Parast, M. (2021). An Integrative approach to supply chain disruption risk and resilience management: A literature review. International Journal of Logistics Research and Applications, 24(5), 427–455. https://doi.org/10.1080/13675567.2020.1763935
Subramanya, K., & Kermanshachi, S. (2021). Impact of COVID-19 on transportation industry: Comparative analysis of road, air, and rail transportation modes. In International Conference on Transportation and Development 2021 (pp. 230–242). https://doi.org/10.1061/9780784483534.020
Taghizadeh, E., & Venkatachalam, S. (2023). Two-stage risk-averse stochastic programming approach for multi-item single source ordering problem: CVaR minimisation with transportation cost. International Journal of Production Research, 61(7), 2129–2146. https://doi.org/10.1080/00207543.2022.2060770
Tang, C. S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics, 103(2), 451–488. https://doi.org/10.1016/j.ijpe.2005.12.006
Ulutaş, A., Meidute-Kavaliauskiene, I., Topal, A., & Demir, E. (2021). Assessment of collaboration-based and non-collaboration-based logistics risks with plithogenic SWARA method. Logistics, 5(4), Article 82. https://doi.org/10.3390/logistics5040082
World Economic Forum. (2023). The Global Risks Report 2023 (18 ed.). https://www.weforum.org/publications/global-risks-report-2023/
Xiang, S., Rasool, S., Hang, Y., Javid, K., Javed, T., & Artene, A. E. (2021). The effect of COVID-19 pandemic on service sector sustainability and growth. Frontiers in Psychology, 12, Article 633597. https://doi.org/10.3389/fpsyg.2021.633597
Xu, Z., Elomri, A., Kerbache, L., & El Omri, A. (2020). Impacts of COVID-19 on global supply chains: Facts and perspectives. IEEE Engineering Management Review, 48(3), 153–166. https://doi.org/10.1109/EMR.2020.3018420
Yan, R., Yang, Y., & Du, Y. (2023). Stochastic optimization model for ship inspection planning under uncertainty in maritime transportation. Electronic Research Archive, 31(1), 103–122. https://doi.org/10.3934/era.2023006
Yang, M., Lim, M. K., Qu, Y., Ni, D., & Xiao, Z. (2023). Supply chain risk management with machine learning technology: A literature review and future research directions. Computers & Industrial Engineering, 175, Article 108859. https://doi.org/10.1016/j.cie.2022.108859
Zeng, Z., Chen, P.-J., & Lew, A. A. (2020). From high-touch to high-tech: COVID-19 drives robotics adoption. Tourism Geographies, 22(3), 724–734. https://doi.org/10.1080/14616688.2020.1762118