How to cite this paper
Alimohammadi, M., Raad, S., Ahangar, A., Amiri, A & Kavianizadeh, R. (2025). Leveraging machine learning for supply chain disruption management: Insights from recent researc.Journal of Future Sustainability, 5(3), 195-204.
Refrences
Aboutorab, H., Hussain, O. K., Saberi, M., & Hussain, F. K. (2022). A reinforcement learning-based framework for dis-ruption risk identification in supply chains. Future Generation Computer Systems, 126, 110–122. https://doi.org/10.1016/j.future.2021.08.004
Alloghani, M., Al-Jumeily, D., Mustafina, J., Hussain, A., & Aljaaf, A. J. (2020). A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science. In M. W. Berry, A. Mohamed, & B. W. Yap (Eds.), Supervised and Unsupervised Learning for Data Science (pp. 3–21). Springer International Publishing. https://doi.org/10.1007/978-3-030-22475-2_1
Arias-Vargas, M., Sanchis, R., & Poler, R. (2022). Impact of Predicting Disruptive Events in Supply Planning for Enter-prise Resilience. IFAC-PapersOnLine, 55(10), 1864–1869. https://doi.org/10.1016/j.ifacol.2022.09.670
Ashraf, M., Eltawil, A., & Ali, I. (2024). Disruption detection for a cognitive digital supply chain twin using hybrid deep learning. Operational Research, 24(2), 23. https://doi.org/10.1007/s12351-024-00831-y
Badakhshan, E., & Ball, P. (2023). Applying digital twins for inventory and cash management in supply chains under physical and financial disruptions. International Journal of Production Research, 61(15), 5094–5116. https://doi.org/10.1080/00207543.2022.2093682
Badakhshan, E., & Ball, P. (2024). Deploying hybrid modelling to support the development of a digital twin for supply chain master planning under disruptions. International Journal of Production Research, 62(10), 3606–3637. https://doi.org/10.1080/00207543.2023.2244604
Bartschat, A., Reischl, M., & Mikut, R. (2019). Data mining tools. WIREs Data Mining and Knowledge Discovery, 9(4), e1309. https://doi.org/10.1002/widm.1309
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
Bodendorf, F., Sauter, M., & Franke, J. (2023). A mixed methods approach to analyze and predict supply disruptions by combining causal inference and deep learning. International Journal of Production Economics, 256, 108708. https://doi.org/10.1016/j.ijpe.2022.108708
Brintrup, A., Pak, J., Ratiney, D., Pearce, T., Wichmann, P., Woodall, P., & McFarlane, D. (2020). Supply chain data an-alytics for predicting supplier disruptions: A case study in complex asset manufacturing. International Journal of Production Research, 58(11), 3330–3341. https://doi.org/10.1080/00207543.2019.1685705
Camur, M. C., Ravi, S. K., & Saleh, S. (2024). Enhancing supply chain resilience: A machine learning approach for pre-dicting product availability dates under disruption. Expert Systems with Applications, 247, 123226. https://doi.org/10.1016/j.eswa.2024.123226
Cavalcante, I. M., Frazzon, E. M., Forcellini, F. A., & Ivanov, D. (2019). A supervised machine learning approach to da-ta-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management, 49, 86–97. https://doi.org/10.1016/j.ijinfomgt.2019.03.004
Chen, C.-M., Huang, S.-Y., Cai, Z.-X., Ou, Y.-H., & Lin, J. (2023). Detecting Supply Chain Attacks with Unsupervised Learning. 2023 9th International Conference on Applied System Innovation (ICASI), 232–234. https://doi.org/10.1109/ICASI57738.2023.10179583
Cho, Y. S., & Hong, P. C. (2023). Applying Machine Learning to Healthcare Operations Management: CNN-Based Model for Malaria Diagnosis. Healthcare, 11(12), 1779. https://doi.org/10.3390/healthcare11121779
Corsini, R. R., Costa, A., Fichera, S., & Framinan, J. M. (2024). Digital twin model with machine learning and optimiza-tion for resilient production–distribution systems under disruptions. Computers & Industrial Engineering, 191, 110145. https://doi.org/10.1016/j.cie.2024.110145
Cuong, T. N., Long, L. N. B., Kim, H.-S., & You, S.-S. (2023). Data analytics and throughput forecasting in port man-agement systems against disruptions: A case study of Busan Port. Maritime Economics & Logistics, 25(1), 61–89. https://doi.org/10.1057/s41278-022-00247-5
García-Peñalvo, F. J. (2022). Desarrollo de estados de la cuestión robustos: Revisiones Sistemáticas de Literatura. Edu-cation in the Knowledge Society (EKS), 23, e28600. https://doi.org/10.14201/eks.28600
Ghorbani, R., & Ghousi, R. (2019). Predictive data mining approaches in medical diagnosis: A review of some diseases prediction. International Journal of Data and Network Science, 47–70. https://doi.org/10.5267/j.ijdns.2019.1.003
Handfield, R., Sun, H., & Rothenberg, L. (2020). Assessing supply chain risk for apparel production in low cost coun-tries using newsfeed analysis. Supply Chain Management: An International Journal, 25(6), 803–821. https://doi.org/10.1108/SCM-11-2019-0423
Huang, J.-C., Ko, K.-M., Shu, M.-H., & Hsu, B.-M. (2020). Application and comparison of several machine learning al-gorithms and their integration models in regression problems. Neural Computing and Applications, 32(10), 5461–5469. https://doi.org/10.1007/s00521-019-04644-5
Jafari-Nodoushan, A., Sadrabadi, M. H. D., Nili, M., Makui, A., & Ghousi, R. (2024). Designing a sustainable disrup-tion-oriented supply chain under joint pricing and resiliency considerations: A case study. Computers & Chemical Engineering, 180, 108481. https://doi.org/10.1016/j.compchemeng.2023.108481
Jain, V., & Chatterjee, J. M. (Eds.). (2020). Machine Learning with Health Care Perspective: Machine Learning and Healthcare (Vol. 13). Springer International Publishing. https://doi.org/10.1007/978-3-030-40850-3
Janjua, N. K., Nawaz, F., & Prior, D. D. (2023). A fuzzy supply chain risk assessment approach using real-time disrup-tion event data from Twitter. Enterprise Information Systems, 17(4), 1959652. https://doi.org/10.1080/17517575.2021.1959652
Katsaliaki, K., Galetsi, P., & Kumar, S. (2022). Supply chain disruptions and resilience: A major review and future re-search agenda. Annals of Operations Research, 319(1), 965–1002. https://doi.org/10.1007/s10479-020-03912-1
Khosrowabadi, N., Ghousi, R., & Makui, A. (2019). Decision support approach to occupational safety using data mining. International Journal of Industrial Engineering & Production Research, 30(2), 149–164.
Kleab, K. (2017). Important of supply chain management. International Journal of Scientific and Research Publica-tions, 7(9), 397–400.
Lasch, R. (2018). Supply Chain Disruption Models: A Critical Review (5th ed.). Bundesvereinigung Logistik (BVL) e.V. https://doi.org/10.23773/2018_5
Lorenc, A., KUŹNAR, M., LERHER, T., & SZKODA, M. (2020). Predicting the Probability of Cargo Theft for Individu-al Cases in Railway Transport. Tehnicki Vjesnik - Technical Gazette, 27(3). https://doi.org/10.17559/TV-20190320194915
Luo, J. (2023). Application of Machine Learning in Supply Chain Management. In S. Kadry, Y. Yan, & J. Xia (Eds.), Proceedings of the 2022 3rd International Conference on Big Data Economy and Information Management (BDEIM 2022) (Vol. 233, pp. 489–498). Atlantis Press International BV. https://doi.org/10.2991/978-94-6463-124-1_58
Malmstedt, A., & Bäckstrand, J. (2022). How to Predict Disruptions in the Inbound Supply Chain in a Volatile Envi-ronment. In A. H. C. Ng, A. Syberfeldt, D. Högberg, & M. Holm (Eds.), Advances in Transdisciplinary Engineering. IOS Press. https://doi.org/10.3233/ATDE220182
Nguyen, A., Pellerin, R., Lamouri, S., & Lekens, B. (2023). Managing demand volatility of pharmaceutical products in times of disruption through news sentiment analysis. International Journal of Production Research, 61(9), 2829–2840. https://doi.org/10.1080/00207543.2022.2070044
Nikolopoulos, K., Punia, S., Schäfers, A., Tsinopoulos, C., & Vasilakis, C. (2021). Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. European Journal of Op-erational Research, 290(1), 99–115. https://doi.org/10.1016/j.ejor.2020.08.001
Rahmani, A. M., Yousefpoor, E., Yousefpoor, M. S., Mehmood, Z., Haider, A., Hosseinzadeh, M., & Ali Naqvi, R. (2021). Machine Learning (ML) in Medicine: Review, Applications, and Challenges. Mathematics, 9(22), 2970. https://doi.org/10.3390/math9222970
Romero, C., & Ventura, S. (2010). Educational Data Mining: A Review of the State of the Art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601–618. https://doi.org/10.1109/TSMCC.2010.2053532
Sen, P. C., Hajra, M., & Ghosh, M. (2020). Supervised Classification Algorithms in Machine Learning: A Survey and Review. In J. K. Mandal & D. Bhattacharya (Eds.), Emerging Technology in Modelling and Graphics (Vol. 937, pp. 99–111). Springer Singapore. https://doi.org/10.1007/978-981-13-7403-6_11
Shahzad, U., Si Mohammed, K., Schneider, N., Faggioni, F., & Papa, A. (2023). GDP responses to supply chain disrup-tions in a post-pandemic era: Combination of DL and ANN outputs based on Google Trends. Technological Fore-casting and Social Change, 192, 122512. https://doi.org/10.1016/j.techfore.2023.122512
Shutaywi, M., & Kachouie, N. N. (2021). Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering. Entropy, 23(6), 759. https://doi.org/10.3390/e23060759
Soltani, H., & Bhandari, P. (2023, March 9). The Use of Machine Learning in Supply Chain Management, A Systematic Review. Proceedings of the International Conference on Industrial Engineering and Operations Management. 13th Annual International International Conference on Industrial Engineering and Operations Management, Manila, Philippines. https://doi.org/10.46254/AN13.20230529
Stephan, T., Al-Turjman, F., Ravishankar, M., & Stephan, P. (2022). Machine learning analysis on the impacts of COVID-19 on India’s renewable energy transitions and air quality. Environmental Science and Pollution Research, 29(52), 79443–79465. https://doi.org/10.1007/s11356-022-20997-2
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
Thomas, A., & Panicker, V. V. (2023). Application of Machine Learning Algorithms for Order Delivery Delay Predic-tion in Supply Chain Disruption Management. In B. B. V. L. Deepak, M. V. A. R. Bahubalendruni, D. R. K. Parhi, & B. B. Biswal (Eds.), Intelligent Manufacturing Systems in Industry 4.0 (pp. 491–500). Springer Nature Singapore. https://doi.org/10.1007/978-981-99-1665-8_42
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222.
Wu, T., Blackhurst, J., & O’grady, P. (2007). Methodology for supply chain disruption analysis. International Journal of Production Research, 45(7), 1665–1682. https://doi.org/10.1080/00207540500362138
Xu, S., Zhang, X., Feng, L., & Yang, W. (2020). Disruption risks in supply chain management: A literature review based on bibliometric analysis. International Journal of Production Research, 58(11), 3508–3526. https://doi.org/10.1080/00207543.2020.1717011
Yan, R., Chu, Z., Wu, L., & Wang, S. (2024). Predicting vessel service time: A data-driven approach. Advanced Engi-neering Informatics, 62, 102718. https://doi.org/10.1016/j.aei.2024.102718
Yang, S., Ogawa, Y., Ikeuchi, K., Shibasaki, R., & Okuma, Y. (2024). Post-hazard supply chain disruption: Predicting firm-level sales using graph neural network. International Journal of Disaster Risk Reduction, 110, 104664. https://doi.org/10.1016/j.ijdrr.2024.104664
Yang, Y., Chen, J., Dong, C., & Xu, Y. (2023). Process switching strategies for sustainable production of personal pro-tective equipment. Annals of Operations Research. https://doi.org/10.1007/s10479-023-05669-9
Zamani, E. D., Smyth, C., Gupta, S., & Dennehy, D. (2023). Artificial intelligence and big data analytics for supply chain resilience: A systematic literature review. Annals of Operations Research, 327(2), 605–632. https://doi.org/10.1007/s10479-022-04983-y
Zamiela, C., Hossain, N. U. I., & Jaradat, R. (2022). Enablers of resilience in the healthcare supply chain: A case study of U.S healthcare industry during COVID-19 pandemic. Research in Transportation Economics, 93, 101174. https://doi.org/10.1016/j.retrec.2021.101174
Zdolsek Draksler, T., Cimperman, M., & Obrecht, M. (2023). Data-Driven Supply Chain Operations—The Pilot Case of Postal Logistics and the Cross-Border Optimization Potential. Sensors, 23(3), 1624. https://doi.org/10.3390/s23031624
Alloghani, M., Al-Jumeily, D., Mustafina, J., Hussain, A., & Aljaaf, A. J. (2020). A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science. In M. W. Berry, A. Mohamed, & B. W. Yap (Eds.), Supervised and Unsupervised Learning for Data Science (pp. 3–21). Springer International Publishing. https://doi.org/10.1007/978-3-030-22475-2_1
Arias-Vargas, M., Sanchis, R., & Poler, R. (2022). Impact of Predicting Disruptive Events in Supply Planning for Enter-prise Resilience. IFAC-PapersOnLine, 55(10), 1864–1869. https://doi.org/10.1016/j.ifacol.2022.09.670
Ashraf, M., Eltawil, A., & Ali, I. (2024). Disruption detection for a cognitive digital supply chain twin using hybrid deep learning. Operational Research, 24(2), 23. https://doi.org/10.1007/s12351-024-00831-y
Badakhshan, E., & Ball, P. (2023). Applying digital twins for inventory and cash management in supply chains under physical and financial disruptions. International Journal of Production Research, 61(15), 5094–5116. https://doi.org/10.1080/00207543.2022.2093682
Badakhshan, E., & Ball, P. (2024). Deploying hybrid modelling to support the development of a digital twin for supply chain master planning under disruptions. International Journal of Production Research, 62(10), 3606–3637. https://doi.org/10.1080/00207543.2023.2244604
Bartschat, A., Reischl, M., & Mikut, R. (2019). Data mining tools. WIREs Data Mining and Knowledge Discovery, 9(4), e1309. https://doi.org/10.1002/widm.1309
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
Bodendorf, F., Sauter, M., & Franke, J. (2023). A mixed methods approach to analyze and predict supply disruptions by combining causal inference and deep learning. International Journal of Production Economics, 256, 108708. https://doi.org/10.1016/j.ijpe.2022.108708
Brintrup, A., Pak, J., Ratiney, D., Pearce, T., Wichmann, P., Woodall, P., & McFarlane, D. (2020). Supply chain data an-alytics for predicting supplier disruptions: A case study in complex asset manufacturing. International Journal of Production Research, 58(11), 3330–3341. https://doi.org/10.1080/00207543.2019.1685705
Camur, M. C., Ravi, S. K., & Saleh, S. (2024). Enhancing supply chain resilience: A machine learning approach for pre-dicting product availability dates under disruption. Expert Systems with Applications, 247, 123226. https://doi.org/10.1016/j.eswa.2024.123226
Cavalcante, I. M., Frazzon, E. M., Forcellini, F. A., & Ivanov, D. (2019). A supervised machine learning approach to da-ta-driven simulation of resilient supplier selection in digital manufacturing. International Journal of Information Management, 49, 86–97. https://doi.org/10.1016/j.ijinfomgt.2019.03.004
Chen, C.-M., Huang, S.-Y., Cai, Z.-X., Ou, Y.-H., & Lin, J. (2023). Detecting Supply Chain Attacks with Unsupervised Learning. 2023 9th International Conference on Applied System Innovation (ICASI), 232–234. https://doi.org/10.1109/ICASI57738.2023.10179583
Cho, Y. S., & Hong, P. C. (2023). Applying Machine Learning to Healthcare Operations Management: CNN-Based Model for Malaria Diagnosis. Healthcare, 11(12), 1779. https://doi.org/10.3390/healthcare11121779
Corsini, R. R., Costa, A., Fichera, S., & Framinan, J. M. (2024). Digital twin model with machine learning and optimiza-tion for resilient production–distribution systems under disruptions. Computers & Industrial Engineering, 191, 110145. https://doi.org/10.1016/j.cie.2024.110145
Cuong, T. N., Long, L. N. B., Kim, H.-S., & You, S.-S. (2023). Data analytics and throughput forecasting in port man-agement systems against disruptions: A case study of Busan Port. Maritime Economics & Logistics, 25(1), 61–89. https://doi.org/10.1057/s41278-022-00247-5
García-Peñalvo, F. J. (2022). Desarrollo de estados de la cuestión robustos: Revisiones Sistemáticas de Literatura. Edu-cation in the Knowledge Society (EKS), 23, e28600. https://doi.org/10.14201/eks.28600
Ghorbani, R., & Ghousi, R. (2019). Predictive data mining approaches in medical diagnosis: A review of some diseases prediction. International Journal of Data and Network Science, 47–70. https://doi.org/10.5267/j.ijdns.2019.1.003
Handfield, R., Sun, H., & Rothenberg, L. (2020). Assessing supply chain risk for apparel production in low cost coun-tries using newsfeed analysis. Supply Chain Management: An International Journal, 25(6), 803–821. https://doi.org/10.1108/SCM-11-2019-0423
Huang, J.-C., Ko, K.-M., Shu, M.-H., & Hsu, B.-M. (2020). Application and comparison of several machine learning al-gorithms and their integration models in regression problems. Neural Computing and Applications, 32(10), 5461–5469. https://doi.org/10.1007/s00521-019-04644-5
Jafari-Nodoushan, A., Sadrabadi, M. H. D., Nili, M., Makui, A., & Ghousi, R. (2024). Designing a sustainable disrup-tion-oriented supply chain under joint pricing and resiliency considerations: A case study. Computers & Chemical Engineering, 180, 108481. https://doi.org/10.1016/j.compchemeng.2023.108481
Jain, V., & Chatterjee, J. M. (Eds.). (2020). Machine Learning with Health Care Perspective: Machine Learning and Healthcare (Vol. 13). Springer International Publishing. https://doi.org/10.1007/978-3-030-40850-3
Janjua, N. K., Nawaz, F., & Prior, D. D. (2023). A fuzzy supply chain risk assessment approach using real-time disrup-tion event data from Twitter. Enterprise Information Systems, 17(4), 1959652. https://doi.org/10.1080/17517575.2021.1959652
Katsaliaki, K., Galetsi, P., & Kumar, S. (2022). Supply chain disruptions and resilience: A major review and future re-search agenda. Annals of Operations Research, 319(1), 965–1002. https://doi.org/10.1007/s10479-020-03912-1
Khosrowabadi, N., Ghousi, R., & Makui, A. (2019). Decision support approach to occupational safety using data mining. International Journal of Industrial Engineering & Production Research, 30(2), 149–164.
Kleab, K. (2017). Important of supply chain management. International Journal of Scientific and Research Publica-tions, 7(9), 397–400.
Lasch, R. (2018). Supply Chain Disruption Models: A Critical Review (5th ed.). Bundesvereinigung Logistik (BVL) e.V. https://doi.org/10.23773/2018_5
Lorenc, A., KUŹNAR, M., LERHER, T., & SZKODA, M. (2020). Predicting the Probability of Cargo Theft for Individu-al Cases in Railway Transport. Tehnicki Vjesnik - Technical Gazette, 27(3). https://doi.org/10.17559/TV-20190320194915
Luo, J. (2023). Application of Machine Learning in Supply Chain Management. In S. Kadry, Y. Yan, & J. Xia (Eds.), Proceedings of the 2022 3rd International Conference on Big Data Economy and Information Management (BDEIM 2022) (Vol. 233, pp. 489–498). Atlantis Press International BV. https://doi.org/10.2991/978-94-6463-124-1_58
Malmstedt, A., & Bäckstrand, J. (2022). How to Predict Disruptions in the Inbound Supply Chain in a Volatile Envi-ronment. In A. H. C. Ng, A. Syberfeldt, D. Högberg, & M. Holm (Eds.), Advances in Transdisciplinary Engineering. IOS Press. https://doi.org/10.3233/ATDE220182
Nguyen, A., Pellerin, R., Lamouri, S., & Lekens, B. (2023). Managing demand volatility of pharmaceutical products in times of disruption through news sentiment analysis. International Journal of Production Research, 61(9), 2829–2840. https://doi.org/10.1080/00207543.2022.2070044
Nikolopoulos, K., Punia, S., Schäfers, A., Tsinopoulos, C., & Vasilakis, C. (2021). Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. European Journal of Op-erational Research, 290(1), 99–115. https://doi.org/10.1016/j.ejor.2020.08.001
Rahmani, A. M., Yousefpoor, E., Yousefpoor, M. S., Mehmood, Z., Haider, A., Hosseinzadeh, M., & Ali Naqvi, R. (2021). Machine Learning (ML) in Medicine: Review, Applications, and Challenges. Mathematics, 9(22), 2970. https://doi.org/10.3390/math9222970
Romero, C., & Ventura, S. (2010). Educational Data Mining: A Review of the State of the Art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601–618. https://doi.org/10.1109/TSMCC.2010.2053532
Sen, P. C., Hajra, M., & Ghosh, M. (2020). Supervised Classification Algorithms in Machine Learning: A Survey and Review. In J. K. Mandal & D. Bhattacharya (Eds.), Emerging Technology in Modelling and Graphics (Vol. 937, pp. 99–111). Springer Singapore. https://doi.org/10.1007/978-981-13-7403-6_11
Shahzad, U., Si Mohammed, K., Schneider, N., Faggioni, F., & Papa, A. (2023). GDP responses to supply chain disrup-tions in a post-pandemic era: Combination of DL and ANN outputs based on Google Trends. Technological Fore-casting and Social Change, 192, 122512. https://doi.org/10.1016/j.techfore.2023.122512
Shutaywi, M., & Kachouie, N. N. (2021). Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering. Entropy, 23(6), 759. https://doi.org/10.3390/e23060759
Soltani, H., & Bhandari, P. (2023, March 9). The Use of Machine Learning in Supply Chain Management, A Systematic Review. Proceedings of the International Conference on Industrial Engineering and Operations Management. 13th Annual International International Conference on Industrial Engineering and Operations Management, Manila, Philippines. https://doi.org/10.46254/AN13.20230529
Stephan, T., Al-Turjman, F., Ravishankar, M., & Stephan, P. (2022). Machine learning analysis on the impacts of COVID-19 on India’s renewable energy transitions and air quality. Environmental Science and Pollution Research, 29(52), 79443–79465. https://doi.org/10.1007/s11356-022-20997-2
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
Thomas, A., & Panicker, V. V. (2023). Application of Machine Learning Algorithms for Order Delivery Delay Predic-tion in Supply Chain Disruption Management. In B. B. V. L. Deepak, M. V. A. R. Bahubalendruni, D. R. K. Parhi, & B. B. Biswal (Eds.), Intelligent Manufacturing Systems in Industry 4.0 (pp. 491–500). Springer Nature Singapore. https://doi.org/10.1007/978-981-99-1665-8_42
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222.
Wu, T., Blackhurst, J., & O’grady, P. (2007). Methodology for supply chain disruption analysis. International Journal of Production Research, 45(7), 1665–1682. https://doi.org/10.1080/00207540500362138
Xu, S., Zhang, X., Feng, L., & Yang, W. (2020). Disruption risks in supply chain management: A literature review based on bibliometric analysis. International Journal of Production Research, 58(11), 3508–3526. https://doi.org/10.1080/00207543.2020.1717011
Yan, R., Chu, Z., Wu, L., & Wang, S. (2024). Predicting vessel service time: A data-driven approach. Advanced Engi-neering Informatics, 62, 102718. https://doi.org/10.1016/j.aei.2024.102718
Yang, S., Ogawa, Y., Ikeuchi, K., Shibasaki, R., & Okuma, Y. (2024). Post-hazard supply chain disruption: Predicting firm-level sales using graph neural network. International Journal of Disaster Risk Reduction, 110, 104664. https://doi.org/10.1016/j.ijdrr.2024.104664
Yang, Y., Chen, J., Dong, C., & Xu, Y. (2023). Process switching strategies for sustainable production of personal pro-tective equipment. Annals of Operations Research. https://doi.org/10.1007/s10479-023-05669-9
Zamani, E. D., Smyth, C., Gupta, S., & Dennehy, D. (2023). Artificial intelligence and big data analytics for supply chain resilience: A systematic literature review. Annals of Operations Research, 327(2), 605–632. https://doi.org/10.1007/s10479-022-04983-y
Zamiela, C., Hossain, N. U. I., & Jaradat, R. (2022). Enablers of resilience in the healthcare supply chain: A case study of U.S healthcare industry during COVID-19 pandemic. Research in Transportation Economics, 93, 101174. https://doi.org/10.1016/j.retrec.2021.101174
Zdolsek Draksler, T., Cimperman, M., & Obrecht, M. (2023). Data-Driven Supply Chain Operations—The Pilot Case of Postal Logistics and the Cross-Border Optimization Potential. Sensors, 23(3), 1624. https://doi.org/10.3390/s23031624