Optimizing Supply Chain with Artificial Intelligence in Business
DOI:
https://doi.org/10.54536/ajec.v4i3.5895Keywords:
Artificial Intelligence (AI), Business, Optimization, Supply ChainAbstract
This literature review states the theoretical foundations, current advancements, practical applications, and technological tools of Artificial Intelligence (AI) in supply chain management (SCM). The review also describes the outcomes from recent studies that shows Artificial intelligence’s evolution from a theoretical part to a transformative enabler of adaptive, data-driven supply chains. The theory illustrates AI’s roots in decision sciences, systems theory, and hybrid analytical models, which effect strategic planning under uncertainty. Besides that, current developments describe AI’s effects in predictive analytics, risk management, and real-time decision-making. It is very much crucial for this study. On the other hand, case studies from various sectors such as food logistics, production, and retail demonstrate tangible improvements in efficiency, resilience, and consumer satisfaction. Some excellent AI technology such as machine learning, NLP, IoT, and cloud-based platforms, noting their effects on operational excellence. Besides that, persistent challenges such as cost, infrastructure gaps, data silos, and limited sustainability focus continue to constrain extensive use. This review proclaims the demand for broad base AI strategies that clarify technical and organizational obstacles, clearing the way for more excellent and permanent supply chain management. The integration of advanced technologies in supply chain management has led improvements significantly across key performance indicators. Some peer reviewed articles showed that, forecast accuracy increased from 67–70% to 89–92% (Alomar, 2022; Wong et al., 2024; Abaku et al., 2024; Khoa et al., 2024), according to Fosso Wamba et al., 2022; Helo & Hao, 2022; Grover, 2025 inventory turnover rose from 4–5 to 5–6 times per year. Cost reduction ranged from 10–20%, while delivery time decreased by 15–25% (Hasan et al., 2024; Shamsuddoha et al., 2025; Eyo-Udo, 2024; Khan & Jalal, 2023). and on-time delivery improved by 10–18% (Thuraka, 2021; Vaka, 2024; Attah et al., 2024; Fatorachian, 2024). So, stock-out incidents were reduced by 15–30%, and consumer satisfaction increased by 18–22%.
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References
Adeniran, I. A., Efunniyi, C. P., Osundare, O. S., & Abhulimen, A. O. (2024). Optimizing logistics and supply chain management through advanced analytics: Insights from industries. Engineering Science & Technology Journal, 5(8).
Agrawal, B. P., Aronkar, P., Palav, M. R., Badre, S., Karumuri, V., & Bagale, G. S. (2025). Optimizing supply chain management with IoE and AI. In Interdisciplinary Approaches to AI, Internet of Everything, and Machine Learning (pp. 423-436). IGI Global Scientific Publishing.
Akhtar, P., Ghouri, A. M., Khan, H. U. R., Amin ul Haq, M., Awan, U., Zahoor, N., ... & Ashraf, A. (2023). Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions. Annals of operations research, 327(2), 633-657.
Anwar, H., Anwar, T., & Mahmood, G. (2023). Nourishing the future: AI-driven optimization of farm-to-consumer food supply chain for enhanced business performance. Innovative Computing Review, 3(2), 14-29.
Alomar M. A. (2022). Performance Optimization of Industrial Supply Chain Using Artificial Intelligence. Computational intelligence and neuroscience, 2022, 9306265. https://doi.org/10.1155/2022/9306265.
Attah, R. U., Garba, B. M. P., Gil-Ozoudeh, I., & Iwuanyanwu, O. (2024). Enhancing supply chain resilience through artificial intelligence: Analyzing problem-solving approaches in logistics management. International Journal of Management & Entrepreneurship Research, 5(12), 3248-3265.
Das, D., Datta, A., Kumar, P., Kazancoglu, Y., & Ram, M. (2022). Building supply chain resilience in the era of COVID-19: An AHP-DEMATEL approach. Operations Management Research, 15(1), 249-267.
Dey, P. K., Chowdhury, S., Abadie, A., Vann Yaroson, E., & Sarkar, S. (2024). Artificial intelligence-driven supply chain resilience in Vietnamese manufacturing small-and medium-sized enterprises. International Journal of Production Research, 62(15), 5417-5456.
Eling, M., Nuessle, D., & Staubli, J. (2022). The impact of artificial intelligence along the insurance value chain and on the insurability of risks. The Geneva Papers on Risk and Insurance-Issues and Practice, 47(2), 205-241.
Emmanuel Adeyemi Abaku, Tolulope Esther Edunjobi, & Agnes Clare Odimarha. (2024). Theoretical approaches to AI in supply chain optimization: Pathways to efficiency and resilience. International Journal of Science and Technology Research Archive, 6(1), 092-107. https://doi.org/10.53771/ijstra.2024.6.1.0033
Eyo-Udo, N. (2024). Leveraging artificial intelligence for enhanced supply chain optimization. Open Access Research Journal of Multidisciplinary Studies, 7(2), 001-015.
Fatorachian, H. (2024). Leveraging artificial intelligence for optimizing logistics performance: a comprehensive review. Global Journal of Business Social Sciences Review (GATR-GJBSSR), 12(3).
Fosso Wamba, S., Queiroz, M. M., Guthrie, C., & Braganza, A. (2022). Industry experiences of artificial intelligence (AI): benefits and challenges in operations and supply chain management. Production planning & control, 33(16), 1493-1497.
Goswami, S. S., Mondal, S., Sarkar, S., Gupta, K. K., Sahoo, S. K., & Halder, R. (2025). Artificial intelligence-enabled supply chain management: Unlocking new opportunities and challenges. Artificial Intelligence and Applications, 3(1), 110–121. https://doi.org/10.32996/jefas.2024.6.4.7
Grover, N. (2025). AI-enabled supply chain optimization. International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), 5(3). https://doi.org/10.1109/i2ct57861.2023.10126484
Hasan, M. R., Reza, E. R. S., Rahman, A., Mukaddim, A. A., MD, A. K., Mohammad, A. H., & MD Abdul, F. Z. (2024). Optimizing Sustainable Supply Chains: Integrating Environmental Concerns and Carbon Footprint Reduction through AI-Enhanced Decision-Making in the USA. Journal of Economics, Finance, and Accounting Studies, 6(4), 57-71.
Hendriksen, C. (2023). Artificial intelligence for supply chain management: Disruptive innovation or innovative disruption? Journal of Supply Chain Management, 59(3), 65-76.
Helo, P., & Hao, Y. (2022). Artificial intelligence in operations management and supply chain management: An exploratory case study. Production planning & control, 33(16), 1573-1590.
Jones, J. (2025). Exploring the role of artificial intelligence in optimizing supply chain operations.
Kalusivalingam, A. K., Sharma, A., Patel, N., & Singh, V. (2020). Enhancing supply chain visibility through AI: Implementing neural networks and reinforcement learning algorithms. International Journal of AI and ML, 1(2).
Khan, A., & Jalal, A. (2023). Supply Chain Optimization through Technology Integration: Riding the Digital Wave to Efficiency. Abbottabad University Journal of Business and Management Sciences, 1(01), 53-63.
Khoa, B. Q., Nguyen, H. T., Anh, D. B. H., & Ngoc, N. M. (2024). Impact of artificial intelligence’s part in supply chain planning and decision-making optimization. International Journal of Multidisciplinary Research and Growth Evaluation, 5(6), 837-856.
Kumari, N., Chaudhary, D., Kaur, H., & Yadav, A. L. (2023, June). Artificial intelligence in supply chain optimization. In 2023 International Conference on IoT, Communication and Automation Technology (ICICAT) (pp. 1-6). IEEE.
Liu, J., Mao, S., Lu, L., Jing, Y., Yang, X., Xu, H., & Ren, Y. (2025). The impact of digital economy on the supply chain resilience of cross-border healthcare e-commerce. Frontiers in public health, 13, 1570338. https://doi.org/10.3389/fpubh.2025.1570338
Lei, Y., Qiaoming, H., & Tong, Z. (2023). Research on Supply Chain Financial Risk Prevention Based on Machine Learning. Computational intelligence and neuroscience, 2023, 6531154. https://doi.org/10.1155/2023/6531154
Madancian, M., Taherdoost, H., Javadi, M., Khan, I. U., Kalantari, A., & Kumar, D. (2023, November). The impact of artificial intelligence on supply chain management in modern business. In The international conference on artificial intelligence and smart environment (pp. 566-573). Cham: Springer Nature Switzerland.
Modgil, S., Singh, R. K., & Hannibal, C. (2022). Artificial intelligence for supply chain resilience: learning from Covid-19. The international journal of logistics management, 33(4), 1246-1268.
Mohsen, B. M. (2023). Impact of artificial intelligence on supply chain management performance. Journal of Service Science and Management, 16(1), 44-58.
Mwangi, J. (2024). Analyzing the role of artificial intelligence and machine learning in optimizing supply chain processes in Kenya. International Journal of Supply Chain Management, 9(1), 39-50.
Naz, F., Agrawal, R., Kumar, A., Gunasekaran, A., Majumdar, A., & Luthra, S. (2022). Reviewing the applications of artificial intelligence in sustainable supply chains: Exploring research propositions for future directions. Business Strategy and the Environment, 31(5), 2400-2423.
Nozari, H., Szmelter-Jarosz, A., & Ghahremani-Nahr, J. (2022). Analysis of the challenges of artificial intelligence of things (AIoT) for the smart supply chain (case study: FMCG industries). Sensors, 22(8), 2931.
Nsisong Louis Eyo-Udo. (2024). Leveraging artificial intelligence for enhanced supply chain optimization. Open Access Research Journal of Multidisciplinary Studies, 7(2), 001-015. https://doi.org/10.53022/oarjms.2024.7.2.0044
Ojadi, J. O., Odionu, C., Onukwulu, E., & Owulade, O. (2024). Big data analytics and AI for optimizing supply chain sustainability and reducing greenhouse gas emissions in logistics and transportation. International Journal of Multidisciplinary Research and Growth Evaluation, 5(1), 1536-1548.
Olufemi-Phillips, A. Q., Ofodile, O. C., Toromade, A. S., Eyo-Udo, N. L., & Adewale, T. T. (2020). Optimizing FMCG supply chain management with IoT and cloud computing integration. International Journal of Management & Entrepreneurship Research, 6(11), 1-15.
Onukwulu, E. C., Agho, M. O., & Eyo-Udo, N. L. (2023). Developing a framework for AI-driven optimization of supply chains in energy sector. Global Journal of Advanced Research and Reviews, 1(2), 82-101.
Pallathadka, H., Ramirez-Asis, E. H., Loli-Poma, T. P., Kaliyaperumal, K., Ventayen, R. J. M., & Naved, M. (2023). Applications of artificial intelligence in business management, e-commerce and finance. Materials Today: Proceedings, 80, 2610-2613.
Rodriguez, J. M. P., Reambonanza, H. V., & Palallos, L. Q. (2025). Optimizing Operation Processes and Supply Chain Management for Enhanced Service and Product Quality in Quick Service Restaurants. The Southeast Asian Journal of Management, 19(1), 0_1,100-122. https://1l21qesfn-mp02-y-https-doi-org.proxy.lirn.net/10.7454/seam.v19i1.1788
Rolf, B., Jackson, I., Müller, M., Lang, S., Reggelin, T., & Ivanov, D. (2023). A review on reinforcement learning algorithms and applications in supply chain management. International Journal of Production Research, 61(20), 7151-7179.
Schniederjans, D. G., Curado, C., & Khalajhedayati, M. (2020). Supply chain digitisation trends: An integration of knowledge management. International Journal of Production Economics, 220, 107439.
Shamsuddoha, M., Eijaz, A. K., Md Maruf, H. C., & Nasir, T. (2025). Revolutionizing Supply Chains: Unleashing the Power of AI-Driven Intelligent Automation and Real-Time Information Flow. Information, 16(1), 26. https://1l21qesfv-mp02-y-https-doi-org.proxy.lirn.net/10.3390/info16010026
Stephen, G. (2025). Leveraging AI for Strategic Decision-Making in Biopharmaceutical Program Management: A Framework for Risk and Opportunity Analysis. International Journal of Management Technology, 12(4), 1-26. https://doi.org/10.37745/ijmt.2013/vol12n4126
Stewart, O. (2023). AI-Powered Supply Chain Optimization: Enhancing Resilience through Predictive Analytics. International Journal of AI, BigData, Computational and Management Studies, 4(2), 9-20.
Trong, H. B., & Kim, U. B. T. (2020). Application of information and technology in supply chain management: case study of artificial intelligence–a mini review. European Journal of Engineering and Technology Research, 5(12), 19-23.
Thuraka, B. (2021). AI-Driven Adaptive Route Optimization for Sustainable Urban Logistics and Supply Chain Management. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 7, 667-684.
Vaka, D. K. (2024). From Complexity to Simplicity: AI’s Route Optimization in Supply Chain Management. Journal of Artificial Intelligence, Machine Learning and Data Science, 2(1), 386-389.
Wong, L. W., Tan, G. W. H., Ooi, K. B., Lin, B., & Dwivedi, Y. K. (2024). Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis. International Journal of Production Research, 62(15), 5535-5555.
Yenugula, M., Sahoo, S., & Goswami, S. (2023). Cloud computing in supply chain management: Exploring the relationship. Management Science Letters, 13(3), 193-210.
Yerra, S. (2025). Optimizing supply chain efficiency using AI-driven predictive analytics in logistics. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 11(2), 1212-1220.
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Copyright (c) 2025 Md Mustafijur Rahaman, Jimmy Maruri, Malan Begum, SM Toufiqur Rahman
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