Abstract

Artificial Intelligence (AI) in supply chain management is rapidly transforming how manufacturing companies optimize operations and make strategic decisions. However, manufacturers in low-income countries are fraught with persistent challenges, such as inadequate infrastructure, frequent supply disruptions and lack of technical know-how, that hinder AI supply chain management adoption. This study investigates the impact of internal and external factors on AI-driven supply chain management. Using multiple regression analysis, the results reveal that all five variables significantly and positively affect AI-driven supply chain management. Management support (β = 0.411, p < 0.05) emerged as the strongest predictor, underscoring the pivotal role of executive leadership in digital transformation. Workforce digital skill (β = 0.256, p < 0.05) and technology infrastructure (β = 0.215, p < 0.05) were also found to be critical enablers of effective AI-driven supply chain management. Additionally, market complexity (β = 0.103, p < 0.05) and competitive pressure (β = 0.295, p < 0.05) act as external motivators that push firms toward adopting AI technologies to maintain agility and competitiveness. The study concludes that a successful transition to AI-driven supply chains requires a holistic approach that combines internal readiness with strategic responses to external pressures. Therefore, for AI-driven supply chain management to succeed, manufacturing companies must ensure strong support from senior leadership, invest in workforce digital skills training and upgrade their digital infrastructure to support AI integration.

References

• Adekola, A.D., & Dada, S.A. (2024). Optimizing pharmaceutical supply chain management through AI driven predictive analytics: A conceptual framework. Computer Science & IT Research Journal, 5(11), 2580- 2593. DOI: 10.51594/csitrj.v5i11.1709.
• Aggarwal, P., & Aggarwal, A. (2023). AI-Driven Supply Chain Optimization in ERP Systems Enhancing Demand Forecasting and Inventory Management. International Journal of Management, IT & Engineering, 13(08), 107-124.
• Aich, M., Sengupta, D., & Pasam, V.R. (2025). The Future of Supply Chain Automation: How AI and Cloud Integration Are Transforming Logistics. International Journal for Multidisciplinary Research, 7(2), 1-18.
• Chenna, K. (2024). Optimizing decision-making in supply chains: A framework for AI and human collaboration using SAP technologies. International Journal of Research in Computer Applications and Information Technology, 7(2), 824-835. DOI: https://doi.org/10.5281/zenodo.14044969
• Dubey, D.K., Varshney, Y., Awasthi, R.K., Yadav, M.P., Kumar, S., & Kumar, M. (2025). Digital Transformation and its Environmental Implications in Supply Chain Management. Journal of Big Data Analytics and Business Intelligence, 2(2), 11-20.
• Ejjami, R., & Boussalham, K. (2024). Resilient Supply Chains in Industry 5.0: Leveraging AI for Predictive Maintenance and Risk Mitigation. International Journal for Multidisciplinary Research, 6(4), 1-32.
• Elghomri, B., Messaoudi, F., & Touti, N. (2025). The Role of AI in Driving Accountability and Transparency in Global Supply Chains Operations and Supply Chain Management 18(2) 275–287.
• Iseri, F., Iseri, H., Chrisandina, N.J., Iakovou, E., & Pistikopoulos, E.N. (2025). AI-based predictive analytics for enhancing data-driven supply chain optimization. Journal of Global Optimization https://doi.org/10.1007/s10898-025-01509-1
• Ivanov, D., & Dolgui, A. (2021). AI and machine learning in supply chain resilience. International Journal of Production Research, 59(16), 4873-4895.
• Mahabub, S., Hossain, R., & Snigdha, E.Z. (2025). Data-Driven Decision-Making and Strategic Leadership: AI-Powered Business Operations for Competitive Advantage and Sustainable Growth. Journal of Computer Science and Technology Studies, 7(1), 326-336. DOI: 10.32996/jcsts.2025.7.1.24
• Mahadevan, P., Choudhuri, S.S., Navaneethakrishnan, S.R., Arya, A., & Jakhar, R. (2024). Blockchain and AI for engineering supply chain optimization and transparency. ACTA Scientiae, 07(1), 691-705. DOI: 10.17648/acta.scientiae.6389
• Mohammad, A.A.S., Al-Ramadan, A.M., Mohammad, S.I., Oraini B., Vasudevan A., Alshurideh, M.T., Chen, Q., & Ali, I. (2025). Enhancing Metadata Management and Data-Driven Decision-Making in Sustainable Food Supply Chains Using Blockchain and AI Technologies. Data and Metadata.; 4:683. https://doi.org/10.56294/dm2025683
• Mohammed, I.A., Sofia, R., Radhakrishnan, G.V., Jha, S., & Said, N. (2025). The Role of Artificial Intelligence in Enhancing Business Efficiency and Supply Chain Management. Journal of Information Systems Engineering and Management, 10(10s), 509-518.
• Nahar, G., Tamilarasi, K., Nirmala, G., Rahman, A., Boruah, A.N., Naidu, S. T. (2024). Leveraging AI-Powered Automation in Cloud-Integrated Supply Chains: Enhancing Efficiency, Transparency, and Strategic Decision-Making in Management. Frontiers in Health Informatics, 13 (8) 591-599.
• Nyamekeh, R., Yusuf, S.O., Afoakwah, B., Oluwadare, O.E., Yusuf, N., & Eyaru, J. (2025). Leveraging AI for real-time sustainable supply chain visibility: Benefits and implementation barriers. World Journal of Advanced Research and Reviews, 26(02), 422-434. DOI: https://doi.org/10.30574/wjarr.2025.26.2.1536
• Odumbo, O.R., & Nimma, S.Z. (2025). Leveraging Artificial Intelligence to Maximize Efficiency in Supply Chain Process Optimization. International Journal of Research Publication and Reviews, 6(1), 3035-3050.
• Onukwulu, E.C., Agho, M.O., & Eyo-Udo, N.L. (2024). Developing a framework for AI-driven optimization of supply chains in energy sector. Global Journal of Advanced Research and Reviews, 2023, 01(02), 082-0101. DOI: https://doi.org/10.58175/gjarr.2023.1.2.0064
• Raghunath, V, Kunkulagunta, M., & Nadella, G.S. (2020). Artificial Intelligence in Business Analytics: Cloud-Based Strategies for Data Processing and Integration. International Journal of Sustainable Development in Computing Science, 2(4).