In today’s fast-moving, disrupted business environment, supply chain risk management is crucial. More critically, Industry 4.0 has conferred competitive advantages on supply chains through the integration of digital technologies into manufacturing and logistics, but it also implies several challenges and opportunities regarding the management of these risks. This paper looks at some ways emerging technologies, especially Artificial Intelligence (AI), help address pressing concerns about the management of risk and sustainability in logistics and supply chains. The study, using a systemic literature review (SLR) backed by a mapping study based on the Scopus database, reveals the main themes and gaps of prior studies. The findings indicate that AI can substantially enhance resilience through early risk identification, optimizing operations, enriching decision-making, and ensuring transparency throughout the value chain. The key message from the study is to bring out what technology contributes to rendering supply chains resilient against today’s uncertainties.

1. 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
2. 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
3. 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
4. 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
5. 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
6. 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
7. 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
8. 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
9. Bettany-Saltikov, J. (2012). How to do a Systematic Literature Review in Nursing A step-by-step guide (New ed. Edition). Open University Press
10. 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
11. 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
12. 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
13. 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
14. 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
15. 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
16. 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.
17. 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
18. 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
19. 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
20. 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
21. 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
22. 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
23. 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
24. 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
25. 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
26. 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
27. 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
28. 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
29. 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
30. 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
31. 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
32. 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
33. 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
34. 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
35. 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
36. 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
37. 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
38. 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
39. 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
40. 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
41. 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
42. 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
43. 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
44. 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
45. 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
46. 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
47. 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
48. 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
49. 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
50. 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
51. 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
52. 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
53. 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
54. 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
55. 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.
56. 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
57. 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
58. 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