artificial intelligence; Industry 4.0; resilience; risk management; supply chain management

Al-Banna, A., Yaqot, M., & Menezes, B. (2023). Roadmap to Digital Supply Chain Resilience under investment constraints. Production & Manufacturing Research, 11(1). https://doi.org/10.1080/21693277.2023.2194943 Aljabhan, B. (2023). Economic strategic plans with Supply Chain Risk Management (SCRM) for organizational growth and development. Alexandria Engineering Journal, 79, 411–426. https://doi.org/10.1016/j.aej.2023.08.020 Alvarenga, M. Z., Oliveira, M. P., & Oliveira, T. A. (2023). The impact of using digital technologies on Supply Chain Resilience and robustness: The role of memory under the COVID-19 Outbreak. Supply Chain Management: An International Journal, 28(5), 825–842. https://doi.org/10.1108/scm-06-2022-0217 Amentae, T. K., & Gebresenbet, G. (2021). Digitalization and future Agro-Food Supply Chain Management: A literature-based implications. Sustainability, 13(21), 12181. https://doi.org/10.3390/su132112181 Atek, S., Bianchini, F., De Vito, C., Cardinale, V., Novelli, S., Pesaresi, C., Eugeni, M., Mecella, M., Rescio, A., Petronzio, L., Vincenzi, A., Pistillo, P., Giusto, G., Pasquali, G., Alvaro, D., Villari, P., Mancini, M., & Gaudenzi, P. (2023). A predictive decision support system for coronavirus disease 2019 response management and medical logistic planning. DIGITAL HEALTH, 9. https://doi.org/10.1177/20552076231185475 Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2018). 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 Belhadi, A., Kamble, S., Jabbour, C. J., Gunasekaran, A., Ndubisi, N. O., & Venkatesh, M. (2021). Manufacturing and service supply chain resilience to the COVID-19 outbreak: Lessons learned from the automobile and Airline Industries. Technological Forecasting and Social Change, 163, 120447. https://doi.org/10.1016/j.techfore.2020.120447 Benešová, A., Hirman, M., Steiner, F., & Tupa, J. (2019). Determination of changes in process management within industry 4.0. Procedia Manufacturing, 38, 1691–1696. https://doi.org/10.1016/j.promfg.2020.01.112 Bettany-Saltikov, J. (2012). How to do a Systematic Literature Review in Nursing A step-by-step guide (New ed. Edition). Open University Press Blom, T., & Niemann, W. (2022). Managing reputational risk during supply chain disruption recovery: A Triadic Logistics Outsourcing Perspective. Journal of Transport and Supply Chain Management, 16. https://doi.org/10.4102/jtscm.v16i0.623 Burger, M., Kessler, M., & Arlinghaus, J. (2021). Aiming for industry 4.0 maturity? the risk of higher digitalization levels in buyer-supplier relationships. Procedia CIRP, 104, 1529–1534. https://doi.org/10.1016/j.procir.2021.11.258 Chari, A., Niedenzu, D., Despeisse, M., Machado, C. G., Azevedo, J. D., Boavida‐Dias, R., & Johansson, B. (2022). Dynamic capabilities for circular manufacturing supply chains—exploring the role of Industry 4.0 and resilience. Business Strategy and the Environment, 31(5), 2500–2517. https://doi.org/10.1002/bse.3040 Christy, A., & R, V. (2016). Risk assessment and management (RAM) in Enterprise Resource Planning (ERP) by Advanced System Engineering theory. International Journal of Business Intelligence and Data Mining, 11(3), 1. https://doi.org/10.1504/ijbidm.2016.10002433 de Assis Santos, L., & Marques, L. (2022). Big Data Analytics for Supply Chain Risk Management: Research Opportunities at process crossroads. Business Process Management Journal, 28(4), 1117–1145. https://doi.org/10.1108/bpmj-01-2022-0012 Debnath, B., Shakur, M. S., Bari, A. B., Saha, J., Porna, W. A., Mishu, M. J., Islam, A. R., & Rahman, M. A. (2023). Assessing the critical success factors for implementing industry 4.0 in the pharmaceutical industry: Implications for supply chain sustainability in emerging economies. PLOS ONE, 18(6). https://doi.org/10.1371/journal.pone.0287149 Eisinger, B.B., Gyurián Nagy, N., Gyurián, N. (2024). Perception and Social Acceptance of 5G Technology for Sustainability Development. Journal of Cleaner Production. 467. 142964. 10.1016/j.jclepro.2024.142964. Fagundes, M. V., Teles, E. O., Vieira de Melo, S. A. B., & Freires, F. G. (2020). Decision-making models and support systems for Supply Chain Risk: Literature Mapping and Future Research Agenda. European Research on Management and Business Economics, 26(2), 63–70. https://doi.org/10.1016/j.iedeen.2020.02.001 Fakhry, D., Oger, R., & Lauras, M. (2022). Making decisions in highly uncertain and opportunistic environments: Towards a decision support system for sales and Operations Planning. IFAC-PapersOnLine, 55(10), 79–84. https://doi.org/10.1016/j.ifacol.2022.09.371 Fertier, A., Martin, G., Barthe-Delanoë, A.-M., Lesbegueries, J., Montarnal, A., Truptil, S., Bénaben, F., & Salatgé, N. (2021). Managing events to improve situation awareness and resilience in a supply chain. Computers in Industry, 132, 103488. https://doi.org/10.1016/j.compind.2021.103488 Ghobakhloo, M., Iranmanesh, M., Foroughi, B., Tseng, M.-L., Nikbin, D., & Khanfar, A. A. (2023). Industry 4.0 digital transformation and opportunities for Supply Chain Resilience: A comprehensive review and a strategic roadmap. Production Planning & Control, 1–31. https://doi.org/10.1080/09537287.2023.2252376 Hsu, C.-H., He, X., Zhang, T.-Y., Chang, A.-Y., Liu, W.-L., & Lin, Z.-Q. (2022). Enhancing supply chain agility with industry 4.0 enablers to mitigate ripple effects based on integrated QFD-MCDM: An empirical study of New Energy Materials Manufacturers. Mathematics, 10(10), 1635. https://doi.org/10.3390/math10101635 Hsu, C.-H., Li, M.-G., Zhang, T.-Y., Chang, A.-Y., Shangguan, S.-Z., & Liu, W.-L. (2022). Deploying big data enablers to strengthen supply chain resilience to mitigate sustainable risks based on Integrated Hoq-MCDM framework. Mathematics, 10(8), 1233. https://doi.org/10.3390/math10081233 Hu, Y., & Ghadimi, P. (2022). A review of Blockchain technology application on Supply Chain Risk Management. IFAC-PapersOnLine, 55(10), 958–963. https://doi.org/10.1016/j.ifacol.2022.09.472 Huang, K., Wang, K., Lee, P. K. C., & Yeung, A. C. L. (2023). The impact of Industry 4.0 on supply chain capability and Supply Chain Resilience: A dynamic resource-based view. International Journal of Production Economics, 262, 108913. https://doi.org/10.1016/j.ijpe.2023.108913 Ivanov, D., & Dolgui, A. (2019). New Disruption Risk Management Perspectives in supply chains: Digital Twins, the ripple effect, and resileanness. IFAC-PapersOnLine, 52(13), 337–342. https://doi.org/10.1016/j.ifacol.2019.11.138 Ivanov, D., & Dolgui, A. (2020). A digital supply chain twin for managing the disruption risks and resilience in the era of industry 4.0. Production Planning & Control, 32(9), 775–788. https://doi.org/10.1080/09537287.2020.1768450 Ivanov, D., Das, A., & Choi, T.-M. (2018). New flexibility drivers for manufacturing, supply chain and service operations. International Journal of Production Research, 56(10), 3359–3368. https://doi.org/10.1080/00207543.2018.1457813 Ivanov, D., Dolgui, A., Sokolov, B., & Ivanova, M. (2019). Intellectualization of Control: Cyber-physical Supply Chain Risk Analytics. IFAC-PapersOnLine, 52(13), 355–360. https://doi.org/10.1016/j.ifacol.2019.11.146 Khan, M. M., Bashar, I., Minhaj, G. M., Wasi, A. I., & Hossain, N. U. (2023). Resilient and sustainable supplier selection: An integration of SCOR 4.0 and machine learning approach. Sustainable and Resilient Infrastructure, 8(5), 453–469. https://doi.org/10.1080/23789689.2023.2165782 Kurdi, B. A., Alzoubi, H. M., Alshurideh, M. T., Alquqa, E. K., & Hamadneh, S. (2023). Impact of supply chain 4.0 and Supply Chain Risk on organizational performance: An empirical evidence from the UAE food manufacturing industry. Uncertain Supply Chain Management, 11(1), 111–118. https://doi.org/10.5267/j.uscm.2022.11.004 Lohmer, J., Bugert, N., & Lasch, R. (2020). Analysis of resilience strategies and ripple effect in blockchain-coordinated supply chains: An agent-based simulation study. International Journal of Production Economics, 228, 107882. https://doi.org/10.1016/j.ijpe.2020.107882 Mammun, A. A., Prayogo, A., & Buics, L. (2021). The Effects of the Application of Artificial Intelligence in Material Handling – A Systematic Literature Review. In 7th LIMEN Selected Papers (part of LIMEN conference collection) (pp. 139–150). http://doi.org/10.31410/LIMEN.S.P.2021.139 Meriton, R., Bhandal, R., Graham, G., & Brown, A. (2020). An examination of the generative mechanisms of value in big data-enabled Supply Chain Management Research. International Journal of Production Research, 59(23), 7283–7310. https://doi.org/10.1080/00207543.2020.1832273 Metzler, M. J., & Metz, G. A. (2010). Analyzing the barriers and supports of knowledge translation using the PEO model. Canadian Journal of Occupational Therapy, 77(3), 151–158. https://doi. org/10.2182/cjot.2010.77.3.4 Mogre, R., Talluri, S. S., & DAmico, F. (2016). A decision framework to mitigate supply chain risks: An application in the offshore-wind industry. IEEE Transactions on Engineering Management, 63(3), 316–325. https://doi.org/10.1109/tem.2016.2567539 Mubarik, M. S., Naghavi, N., Mubarik, M., Kusi-Sarpong, S., Khan, S. A., Zaman, S. I., & Kazmi, S. H. (2021). Resilience and cleaner production in industry 4.0: Role of supply chain mapping and visibility. Journal of Cleaner Production, 292, 126058. https://doi.org/10.1016/j.jclepro.2021.126058 Oger, R., Lauras, M., Benaben, F., & Montreuil, B. (2019). Strategic Supply Chain Planning and risk management: Experiment of a decision support system gathering business departments around a common vision. 2019 International Conference on Industrial Engineering and Systems Management (IESM). https://doi.org/10.1109/iesm45758.2019.8948116 Panetto, H., Iung, B., Ivanov, D., Weichhart, G., & Wang, X. (2019). Challenges for the cyber-physical manufacturing enterprises of the future. Annual Reviews in Control, 47, 200–213. https://doi.org/10.1016/j.arcontrol.2019.02.002 Peng, T., He, Q., Zhang, Z., Wang, B., & Xu, X. (2021). Industrial internet-enabled resilient manufacturing strategy in the wake of COVID-19 pandemic: A conceptual framework and implementations in China. Chinese Journal of Mechanical Engineering, 34(1). https://doi.org/10.1186/s10033-021-00573-4 Radanliev, P., & De Roure, D. (2023). Disease X vaccine production and supply chains: Risk Assessing Healthcare Systems operating with Artificial Intelligence and Industry 4.0. Health and Technology, 13(1), 11–15. https://doi.org/10.1007/s12553-022-00722-2 Raja Santhi, A., & Muthuswamy, P. (2022). Pandemic, war, natural calamities, and Sustainability: Industry 4.0 technologies to overcome traditional and contemporary supply chain challenges. Logistics, 6(4), 81. https://doi.org/10.3390/logistics6040081 Sathiya, V., Nagalakshmi, K., Jeevamalar, J., Anand Babu, R., Karthi, R., Acevedo-Duque, Á., Lavanya, R., & Ramabalan, S. (2023). Reshaping Healthcare Supply Chain using chain-of-things technology and key lessons experienced from covid-19 pandemic. Socio-Economic Planning Sciences, 85, 101510. https://doi.org/10.1016/j.seps.2023.101510 Sim, C., Zhang, H., & Marianne Louise Chang. (2022). Improving end-to-end traceability and pharma supply chain resilience with Blockchain. Blockchain in Healthcare Today. https://doi.org/10.30953/bhty.v5.231 Singh, D., & Chaddah, J. K. (2021). A study on application of blockchain technology to control counterfeit drugs, enhance data privacy and improve distribution in online pharmacy. Asia Pacific Journal of Health Management, 16(3), 59–66. https://doi.org/10.24083/apjhm.v16i3.1013 Sobb, T., Turnbull, B., & Moustafa, N. (2020). Supply chain 4.0: A survey of cyber security challenges, solutions and Future Directions. Electronics, 9(11), 1864. https://doi.org/10.3390/electronics9111864 Spieske, A., & Birkel, H. (2021). Improving supply chain resilience through industry 4.0: A systematic literature review under the impressions of the COVID-19 pandemic. Computers & Industrial Engineering, 158, 107452. https://doi.org/10.1016/j.cie.2021.107452 Spieske, A., Gebhardt, M., Kopyto, M., Birkel, H., & Hartmann, E. (2023). The Future of Industry 4.0 and supply chain resilience after the COVID-19 pandemic: Empirical evidence from a delphi study. Computers & Industrial Engineering, 181, 109344. https://doi.org/10.1016/j.cie.2023.109344 Tanaka, K., Gu, S.-M., & Zhang, J. (2016). Designing multi-agent simulation with Big Time Series data for a global supply chain system. International Journal of Automation Technology, 10(4), 632–638. https://doi.org/10.20965/ijat.2016.p0632 Tortorella, G. L., Prashar, A., Antony, J., Fogliatto, F. S., Gonzalez, V., & Godinho Filho, M. (2023). Industry 4.0 adoption for healthcare supply chain performance during COVID-19 pandemic in Brazil and India: The mediating role of resilience abilities development. Operations Management Research. https://doi.org/10.1007/s12063-023-00366-z Trabucco, M., & De Giovanni, P. (2021). Achieving resilience and business sustainability during COVID-19: The Role of Lean Supply Chain practices and digitalization. Sustainability, 13(22), 12369. https://doi.org/10.3390/su132212369 Vieira, A. A. C., Figueira, J. R., & Fragoso, R. (2023). A multi-objective simulation-based decision support tool for Wine Supply Chain Design and risk management under sustainability goals. Expert Systems with Applications, 232, 120757. https://doi.org/10.1016/j.eswa.2023.120757 Wang, Y., Skeete, J.-P., Barker, J., & Filimonov, M. (2022). Building resilience and innovation through intelligent diverse supplier engagement. IFAC-PapersOnLine, 55(10), 2390–2395. https://doi.org/10.1016/j.ifacol.2022.10.066 Wong, W. P., Saw, P. S., Jomthanachai, S., Wang, L. S., Ong, H. F., & Lim, C. P. (2023). Digitalization enhancement in the pharmaceutical supply network using a supply chain risk management approach. Scientific Reports, 13(1). https://doi.org/10.1038/s41598-023-49606-z Wu, T., & Zuo, M. (2023). Green supply chain transformation and emission reduction based on machine learning. Science Progress, 106(1), 003685042311656. https://doi.org/10.1177/00368504231165679 Yassine El Khayyam et al., Y.E. (2018) ‘CCAHP, a new method for group decision making application on Supply Chain Dashboard Design’, International Journal of Mechanical and Production Engineering Research and Development, 8(2), pp. 1303–1318. doi:10.24247/ijmperdapr2018150. Zamani, E. D., Smyth, C., Gupta, S., & Dennehy, D. (2022). 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 Zhou, H., Sun, G., Fu, S., Fan, X., Jiang, W., Hu, S., & Li, L. (2020). A distributed approach of big data mining for financial fraud detection in a supply chain. Computers, Materials & Continua, 64(2), 1091–1105. https://doi.org/10.32604/cmc.2020.09834 Zimmermann, M., Rosca, E., Antons, O., & Bendul, J. C. (2019). Supply chain risks in times of industry 4.0: Insights from German cases. IFAC-PapersOnLine, 52(13), 1755–1760. https://doi.org/10.1016/j.ifacol.2019.11.455