AI-Driven Demand Forecasting and Inventory Optimization in Supply Chain Management: Enhancing Efficiency and Reducing Operational Costs

Abstract

Background: In this study, the increased complexity of today supply chains and explain why conventional forecasting and inventory management techniques are inadequate in today's dynamic and uncertain market conditions. As globalization and data increase, AI has become a gamechanger in delivering better demand forecasting and inventory management, in turn driving a better operation and cost savings.

Objectives: This study seeks to assess the performance of AI-based demand forecasting models combined with inventory optimization methods on improving the overall performance of the supply chain.

Methods: A quantitative, data-driven methodology was employed, and secondary data were used, including historical demand, inventory levels, and other external data that included seasonality and economic indicators. Demand forecasting models: Advanced machine learning and deep learning models such as Long Short-Term Memory (LSTM), Random Forest and Gradient Boosting were used for demand forecasting. The results of the forecasts were fed into an inventory optimization system using reinforcement learning for dynamic decision-making. Standard deviations like Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used to measure the model's performance along with cost-performance analysis.

Results: The accuracy of the prediction is significantly higher in AI-based models, especially the LSTM model, than the traditional models, which decreases the errors of the prediction and enhances its responsiveness. AI-powered inventory optimization resulted in significant savings on inventory holding and shortage/cost of order, and improved service levels and inventory stockout rates. The use of external data had yet further improved predictive performance.

Conclusion: AI-powered demand forecasting and inventory optimization offer a solid solution to improve the efficiency of the supply chain, make intelligent decisions and minimize operational costs.

References

Ahn, H. I., Song, Y. C., Olivar, S., Mehta, H., & Tewari, N. (2024). GNN-based probabilistic supply and inventory predictions in supply chain networks. arXiv.

Albayrak Ünal, Ö., Erkayman, B., & Usanmaz, B. (2023). Applications of artificial intelligence in inventory management: A systematic review of the literature. Archives of Computational Methods in Engineering. Advance online publication. https://doi.org/10.1007/s11831-023-09977-2

Ayub, M. I., Gharami, A. K., Nitu, F. N., Uddin, M. N., Islam, M. I., Nijhum, A. M., … Yezdani, S. (2025). AI-driven demand forecasting for multi-echelon supply chains: Enhancing forecasting accuracy and operational efficiency through machine learning and deep learning techniques. Emerging Frontiers Library for The American Journal of Management and Economics Innovations, 7(7), 74–85.

Cannas, V. G., Ciano, M. P., Saltalamacchia, M., & Secchi, R. (2024). Artificial intelligence in supply chain and operations management: A multiple case study research. International Journal of Production Research. Advance online publication. https://doi.org/10.1080/00207543.2024.2330633

Choi, T. M. (2022). Supply chain analytics and AI-driven forecasting. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04652-6

Dolgui, A., Ivanov, D., & Sokolov, B. (2022). Reconfigurable supply chain systems. International Journal of Production Research, 60(2), 413–440. https://doi.org/10.1080/00207543.2021.1897179

Douaioui, K., Oucheikh, R., Benmoussa, O., & Mabrouki, C. (2024). Machine learning and deep learning models for demand forecasting in supply chain management: A critical review. Applied System Innovation, 7(2), 40. https://doi.org/10.3390/asi7020040

Fatima, A., & Salam, M. A. (2026). A data-driven predictive framework for inventory optimization using context-augmented machine learning models. arXiv.

Ghodake, S. P., Malkar, V. R., Santosh, K., Jabasheela, L., Abdufattokhov, S., & Gopi, A. (2024). Enhancing supply chain management efficiency: A data-driven approach using predictive analytics and machine learning algorithms. International Journal of Advanced Computer Science and Applications, 15(4).

Islam, M. K., Ahmed, H., Al Bashar, M., & Taher, M. A. (2024). Role of artificial intelligence and machine learning in optimizing inventory management across global industrial manufacturing and supply chain: A multi-country review. International Journal of Management Information Systems and Data Science, 1(2), 1–14.

Ivanov, D., & Dolgui, A. (2021). A digital supply chain twin for managing the disruption risks and resilience in the era of Industry 4.0. International Journal of Production Research, 59(18), 5633–5645. https://doi.org/10.1080/00207543.2020.1768450

Jin, Z. L., Maasoumy, M., Liu, Y., Zheng, Z., & Ren, Z. (2025). Stochastic optimization of inventory at large-scale supply chains. arXiv.

Judijanto, L., Riandari, F., & Marsoit, P. T. (2024). Leveraging AI for optimization in supply chain decision support. Jurnal Teknik Informatika.

Kache, F., & Seuring, S. (2022). Challenges and opportunities of digital information at the intersection of big data analytics and supply chain management. International Journal of Operations & Production Management, 42(1), 1–30. https://doi.org/10.1108/IJOPM-02-2021-0129

Kagalwala, H., Radhakrishnan, G. V., Mohammed, I. A., Kothinti, R. R., & Kulkarni, N. (2025). Predictive analytics in supply chain management: The role of AI and machine learning in demand forecasting. Advances in Consumer Research, 2, 142–149.

Kamble, S. S., Gunasekaran, A., & Sharma, R. (2023). Modeling blockchain-enabled traceability in supply chains. International Journal of Information Management, 68, 102509. https://doi.org/10.1016/j.ijinfomgt.2022.102509

Kaul, D., & Khurana, R. (2022). AI-driven optimization models for e-commerce supply chain operations: Demand prediction, inventory management, and delivery time reduction with cost efficiency considerations. International Journal of Social Analytics, 7(12), 59–77. https://doi.org/10.4018/IJSA.315876

Liu, R., & Vakharia, V. (2024). Optimizing supply chain management using hybrid AI models. Journal of Organizational and End User Computing, 36(2), 1–18. https://doi.org/10.4018/JOEUC.347356

Min, H. (2022). Artificial intelligence in supply chain management: Theory and applications. International Journal of Logistics Research and Applications, 25(3), 289–303. https://doi.org/10.1080/13675567.2020.1849508

Mitta, N. R. (2023). AI-driven optimization of supply chain networks in manufacturing: Utilizing machine learning for demand forecasting, inventory management, and logistics efficiency. Los Angeles Journal of Intelligent Systems and Pattern Recognition, 3, 404–446.

Nweje, U., & Taiwo, M. (2025). Leveraging artificial intelligence for predictive supply chain management: Focus on how AI-driven tools are revolutionizing demand forecasting and inventory optimization. International Journal of Science and Research Archive, 14(1), 230–250.

Pasupuleti, V., Thuraka, B., Kodete, C. S., & Malisetty, S. (2024). Enhancing supply chain agility and sustainability through machine learning: Optimization techniques for logistics and inventory management. Logistics, 8(3), 73. https://doi.org/10.3390/logistics8030073

Patil, D. (2024). Artificial intelligence-driven supply chain optimization: Enhancing demand forecasting and cost reduction (SSRN Working Paper No. 5057408). SSRN. https://doi.org/10.2139/ssrn.5057408

Queiroz, M. M., & Telles, R. (2023). Big data analytics in supply chain management: A review. Transportation Research Part E: Logistics and Transportation Review, 170, 102987. https://doi.org/10.1016/j.tre.2022.102987

Sajja, G. S., Addula, S. R., Meesala, M. K., & Ravipati, P. (2025). Optimizing inventory management through AI-driven demand forecasting for improved supply chain responsiveness and accuracy. In AIP Conference Proceedings (Vol. 3306, No. 1, Article 050003). AIP Publishing.

Shahnawaz, M., & Safder, A. (2025). Stochastic learning-optimization model for resilient supply chains. arXiv.

Shen, L., & Zang, Z. (2024). Enterprise supply chain network optimization algorithm based on blockchain-distributed technology. Information Discovery and Delivery. Advance online publication.

Sodhi, M. S., & Tang, C. S. (2021). Supply chain management for extreme conditions. MIT Sloan Management Review, 62(2), 1–8.

Tang, W. (2024). Improvement of inventory management and demand forecasting by big data analytics in supply chain. Applied Mathematics and Nonlinear Sciences, 9(1).

Verma, P. (2024). Transforming supply chains through AI: Demand forecasting, inventory management, and dynamic optimization. Integrated Journal of Science and Technology, 1(3).

Waller, M. A., & Fawcett, S. E. (2021). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77–84. https://doi.org/10.1111/jbl.12010