Open Access

Intelligent Supply Chain Management Using Artificial Intelligence: Models, Challenges, And Future Prospects

https://doi.org/10.55640/ijmcsit-v03i09-05

PDF

Mr. Raman Kumar ¹ ,

⁴ Assistant Professor, Department of Computer Science and Applications, Mandsaur(M.P.) 458001, India

Abstract

The world has become more global, markets are volatile, and the expectations of customers have grown with the increasing complexity of supply chain management and with constant disruptions to operations. Artificial Intelligence (AI) has become a game-changer for enhancing supply chain efficiency, resilience, and decision-making processes. This review covers the basics of how supply chain management works and the ability of AI-powered models to help optimize various aspects of the supply chain such as demand forecasting, inventory management, procurement, transportation, logistics, and risk management. The study covers machine learning (ML), deep learning (DL), predictive analytics and optimization techniques, which help businesses utilize vast amounts of real-time and historical data to support intelligent decision-making. Moreover, advanced supply chain concepts and practices, namely resilient supply chain, agile supply chain, green supply chain and cold supply chain, are examined in detail to discuss their role in providing sustainability and operational performance. The paper also reviews the latest research advances on AI for Supply Chain Optimization and highlights key challenges such as data quality concerns, interoperability, implementation complexity, and model interpretability. Finally, the future directions of AI-powered supply chains are explored, highlighting the need for scalable, explainable, and adaptive solutions that can contribute to building resilience, sustainability, and competitiveness in a dynamic business landscape.

Keywords

Artificial Intelligence, Supply Chain Management, Machine Learning

References

R. Sharma, A. Shishodia, A. Gunasekaran, H. Min, and Z. H. Munim, “The role of artificial intelligence in supply chain management: mapping the territory,” Int. J. Prod. Res., vol. 60, no. 24, pp. 7527–7550, Dec. 2022, doi: 10.1080/00207543.2022.2029611.

A. C. A. Ferreira, M. B. Francisco, and A. F. De Pinho, “The Use of Artificial Intelligence in Supply Chain Management: Systematic Literature Review and Future Research Directions,” IEEE Access, vol. 13, no. August, pp. 157828–157841, 2025, doi: 10.1109/ACCESS.2025.3603866.

J. Pap et al., “Decision Making: Applications in Management and Engineering Enhancing Supply Chain Safety and Security: A Novel AI-Assisted Supplier Selection Method,” Decis. Mak. Appl. Manag. Eng., vol. 8, no. 1, pp. 22–41, 2025.

A. R. Teixeira, J. V. Ferreira, and A. L. Ramos, “Intelligent Supply Chain Management: A Systematic Literature Review on Artificial Intelligence Contributions,” Inf., vol. 16, no. 5, 2025, doi: 10.3390/info16050399.

S. Kukkar, “Leveraging Price-Quote Data with AI for Smarter Configure-Price-Quote Systems,” in 2026 2nd International Conference on Artificial Intelligence Systems (AIS), San Antonio, TX, USA: IEEE, 2026, pp. 329–335, May. doi: 10.1109/AIS69919.2026.11621526.

D. Patel, “Integrating Price Elasticity and Reinforcement Learning: A Data-Driven Framework for Strategic E-commerce Pricing,” in 2026 IEEE 5th International Conference on AI in Cybersecurity (ICAIC), IEEE, Feb. 2026, pp. 1–6. doi: 10.1109/ICAIC67076.2026.11395747.

M. Basuki, “Supply Chain Management: A Review,” J. Ind. Eng. Halal Ind., vol. 2, no. 1, pp. 9–12, Sep. 2021, doi: 10.14421/jiehis.2651.

R. Palwe, “The Role of User Experience Design in Advancing Digital Sustainability,” Int. J. Sci. Res., vol. 14, no. 10, pp. 1168–1176, 2025.

P. Bhattacharya et al., “Towards Future Internet: The Metaverse Perspective for Diverse Industrial Applications,” Mathematics, vol. 11, no. 4, pp. 1–41, 2023, doi: 10.3390/math11040941.

D. I. and A. Dolgui, “Digital supply chain twins: Concepts, applications and impact on supply chain resilience,” vol. 59, no. 15, 2021.

N. Kühl, M. Goutier, R. Hirt, and G. Satzger, “Machine Learning in Artificial Intelligence: Towards a Common Understanding,” in Proceedings of the Annual Hawaii International Conference on System Sciences, 2019. doi: 10.24251/HICSS.2019.630.

M. R. C. Mukkolakkal, “Deploy, Calibrate, Monitor, Heal -- No Human Required: An Autonomous AI SRE Agent for Elasticsearch,” arXiv Prepr., pp. 1–8, Apr. 2026.

S. Ghidalia, O. L. Narsis, A. Bertaux, and C. Nicolle, “Combining Machine Learning and Ontology: A Systematic Literature Review,” 2024.

A. C. A. Ferreira, M. B. Francisco, and A. F. De Pinho, “The Use of Artificial Intelligence in Supply Chain Management: Systematic Literature Review and Future Research Directions,” IEEE Access, vol. 13, pp. 157828–157841, 2025, doi: 10.1109/ACCESS.2025.3603866.

S. Remella, S. Gupta, J. Lingam, V. K. Gujja, P. Waditwar, and R. R. Gudipati, “Intelligent Cost Governance in Cloud Supply Chains,” in 2025 1st International Conference on Advancement in Futuristic Technologies (ICAFT), 2025, pp. 1–7. doi: 10.1109/ICAFT66710.2025.11452715.

Z. Mujkić, A. Qorri, and A. Kraslawski, “Sustainability and optimization of supply chains: A literature review,” Oper. Supply Chain Manag., vol. 11, no. 4, pp. 186–199, 2018, doi: 10.31387/oscm0350213.

K. N. Amirkolaii, A. Baboli, M. K. Shahzad, and R. Tonadre, “Demand Forecasting for Irregular Demands in Business Aircraft Spare Parts Supply Chains by using Artificial Intelligence (AI),” IFAC-PapersOnLine, vol. 50, no. 1, pp. 15221–15226, Jul. 2017, doi: 10.1016/j.ifacol.2017.08.2371.

A. Daios, N. Kladovasilakis, A. Kelemis, and I. Kostavelis, “AI Applications in Supply Chain Management: A Survey,” Appl. Sci., vol. 15, no. 5, p. 2775, Mar. 2025, doi: 10.3390/app15052775.

R. S. Asl, M. B. Khajeh, M. Pasban, and R. Rostamzadeh, “A systematic literature review on supply chain approaches,” J. Model. Manag., vol. 18, no. 2, pp. 372–415, Mar. 2023, doi: 10.1108/JM2-04-2021-0089.

H. Narne, “Optimizing Supply Chain Management with Machine Learning Algorithms,” Int. J. Adv. Res. Eng. Technol., vol. 12, no. 3, pp. 979–991, 2021.

A. Barhmi, F. Slamti, S. Laghzaoui, and M.-R. Rouijel, “Digital learning, big data analytics and mechanisms for stabilizing and improving supply chain performance,” Int. J. Inf. Syst. Proj. Manag., vol. 12, no. 2, pp. 30–47, 2024, doi: 10.12821/ijispm120202.

S. Kukar, “A Generative AI-Based Framework for Retail Sales Forecasting and Demand Prediction,” in 2026 2nd International Conference on Artificial Intelligence Systems (AIS), IEEE, May 2026, pp. 306–311. doi: 10.1109/AIS69919.2026.11621469.

R. Amar Jyoti Borah1, Bidyut Kumar Das1, Sanjose A Thomas2 and D. K. K. D. Saravanan3, Dr. Syed Salim4, “Embracing artificial intelligence in supply chain management,” Util. AI Technol. Supply Chain Manag., vol. 14, pp. 42–57, 2024, doi: 10.4018/979-8-3693-3593-2.ch004.

S. Chatterjee and N. Rane, “Supply chain management using machine learning, deep learning, and blockchain techniques: A review,” Int. J. Appl. Resil. Sustain., vol. 2, pp. 537–571, 2026, doi: 10.70593/deepsci.0202021.

C. Smyth, D. Dennehy, S. Fosso Wamba, M. Scott, and A. Harfouche, “Artificial intelligence and prescriptive analytics for supply chain resilience: a systematic literature review and research agenda,” Int. J. Prod. Res., vol. 62, no. 23, pp. 8537–8561, 2024, doi: 10.1080/00207543.2024.2341415.

G. Merkuryeva, A. Valberga, and A. Smirnov, “Demand forecasting in pharmaceutical supply chains: A case study,” Procedia Comput. Sci., vol. 149, pp. 3–10, 2019, doi: 10.1016/j.procs.2019.01.100.

[27] M. Atwani, M. Hlyal, and J. E. L. Alami, “AI-Based Demand Forecasting Models: A Systematic Literature Review,” Int. J. Ind. Eng. Prod. Res., vol. 35, no. 2, pp. 1–20, 2024, doi: 10.22068/ijiepr.35.2.1974.

V. Chaturvedi, R. Narra, and S. K. Chintagunta, Applied AI Engineering for Developers: Building Intelligent Applications at Scale. Wissira Research Lab, 2026. doi: 10.63345/WP-978-93-7559-963-0.

A. Balasubramanian and N. Gurushankar, “AI-Driven Supply Chain Risk Management : Integrating Hardware and Software for Real-Time Prediction in Critical Industries,” IJIRMPS, vol. 8, no. 3, 2020, doi: 10.5281/zenodo.14565873.

N. Sayeed, U. S. Saha, F. Karim, M. Mustafizur, S. M. Muhive Uddin, and M. Asikur Rahman Chy, “Artificial Intelligence Models for End-to-End Supply Chain Optimization,” in 2026 IEEE International Conference for Convergence in Computing Technology (I3CTCON), 2026, pp. 1–8. doi: 10.1109/I3CTCON68242.2026.11507407.

S. M. Raza, S. Dey, M. Raza, S. F. Raza, and S. Ali, “The Role of Artificial Intelligence in Enhancing Triple Bottom Line Sustainability in UK SME Supply Chains,” in 2026 5th International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 2026, pp. 1–7. doi: 10.1109/iCoMET69771.2026.11591813.

K. K and D. N, “Routing Optimization of the Internet of Vehicle with Artificial Intelligence,” in 2025 International Conference on Emerging Technologies in Engineering Applications (ICETEA), 2025, pp. 1–4. doi: 10.1109/ICETEA64585.2025.11099718.

D. Srinivas, Twinkle, P. D. Sawant, F. Rabby, M. S. Vasu, and A. Roniboss, “Supply Chain Optimization: Machine Learning Applications in Inventory Management for E-commerce,” in 2024 7th International Conference on Contemporary Computing and Informatics (IC3I), 2024, pp. 457–461. doi: 10.1109/IC3I61595.2024.10829302.

A. Banjar, M. Jemmali, L. K. B. Melhim, W. Boulila, T. Ladhari, and A. Y. Sarhan, “Intelligent Scheduling Algorithms for the Enhancement of Drone-Based Innovative Logistic Supply Chain Systems,” IEEE Access, vol. 11, pp. 102418–102429, 2023, doi: 10.1109/ACCESS.2023.3315694.

M. Pattnaik, N. S. Vijayalakshmi, M. Sharma, A. Kumar, and K. S. Sharaschandra, “A Novel Paradigm to Artificial Intelligence in Transforming Supply Chain Management in the Agile Business World,” in 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), 2022, pp. 1–6. doi: 10.1109/ICSES55317.2022.9914144.

Most read articles by the same author(s)

1 2 3 4 5 > >>

International Journal of Modern Computer Science and IT Innovations

Intelligent Supply Chain Management Using Artificial Intelligence: Models, Challenges, And Future Prospects

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles