An artificial intelligence-enabled machine learning model for supply chain management and cost control optimization
Supply chain management is an evolving industry, but one constant is the significance of effective inventory-management control in optimizing profits and customer satisfaction. The primary objective of the research is to identify the most effective method for a business to supply several products while maintaining customer service and keeping inventory costs low. The proposed stochastic gradient descent-optimized support vector regression (SGD-SVR) model estimates order and shortage quantities to control inventory management and inventory costs. The key goal is to reduce total inventory costs (including holding, ordering, and supply-chain costs) while maintaining excellent customer service. To achieve this, the proposed SGD-SVR combines SGD’s efficiency with a support vector machine’s predictability to improve predictions. We used a real-world consumer products supply-chain dataset to train and test the algorithm. The dataset includes three years of revenue, stock levels, vendors, and costs. We evaluated the methodology using mean absolute error (MAE), inventory turnover ratio (ITR), and service-level agreement (SLA), and optimized parameters using 5-fold cross-validation. The simulation model achieved an average MAE of 1.81 after 100 training epochs, substantially improving ITR and SLA. As it effectively and accurately balances inventory levels with customer requirements, the findings validate that the proposed SGD-SVR proves applicable to inventory-management optimization.
Aspan, H., Rosli, R., Prabowo, A., & Wahyuni, E. S. (2025). Assessment of lecturer work effectiveness: Mediating organizational citizen behavior based on organizational commitment and work motivation. Journal of Ecohumanism, 4(1), 1089–1101. https://doi.org/10.62754/joe.v4i1.5915
Basit, A., Bhaumik, A., & Niazi, A. A. K. (2025). Time-varying spectral coherence examination of consumer price indices in Pakistan: A wavelet transform. Pakistan Journal of Commerce and Social Sciences, 19(2), 385–406. https://doi.org/10.64534/comm.2025.016
Basit, A., Bhaumik, A., & Niazi, A. A. K. (2026). Investigating critical success factors in implementing marketing strategy in FMCG sector in Pakistan. Humanities and Social Sciences Letters, 14(1), 515–530. https://doi.org/10.18488/73.v14i1.4745
Bibi, H., Bhaumik, A., & Haider, S. A. (2025). Impact of inclusive leadership on project success through mediating role of project citizenship behavior: Project complexity as a moderator. Journal of Project Management, 10(4), 629–648. https://doi.org/10.5267/j.jpm.2025.8.006
Deng, C., & Liu, Y. (2021). A Deep Learning‐Based Inventory Management and Demand Prediction Optimization Method for Anomaly Detection. Wireless Communications and Mobile Computing, 2021(1):9969357. https://doi.org/10.1155/2021/9969357 .
Faiz, M. M. U., Oruganti, S. K., & Khekare, G. (2026). Divergent paths to global recognition: ABET accreditation strategies across three continents. In Driving institutional excellence through accreditation standards in higher education. Hershey, PA: IGI Global Scientific Publishing. (pp. 203–243). https://doi.org/10.4018/979-8-3373-2965-9.ch008
Fatima, A., & Salam, M. A. (2026). A data-driven predictive framework for inventory optimization using context-augmented machine learning models. International Journal of Computer Science & Information Technology, 17(6). https://doi.org/10.5121/ijcsit.2025.17603
Goel, A. V., Kumari, P., Vaghela, K., Nagabhyru, K. C., Karichalil, R. A., Salman, S. A., & Brahmane, P. (2025). Strategic change management in the era of digital disruption: An interdisciplinary study on organisational adaptability and innovation culture. Scientific Culture, 11(4), 236–246.
Hussain, M. S., Shabana, Bhaumik, A., Duraipandi, O., & Ansari, S. (2026). Smart last-mile delivery systems: Technological advances, operational strategies, and future directions. International Review of Management and Marketing, 16(4), 426–440. https://doi.org/10.32479/irmm.23132
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, 2025, 1-28. https://doi.org/10.1007/s10898-025-01509-1
Iwadi, I., & Ali, D. (2025). Management by walking around and its relationship with quality of work life in Palestinian schools. TEM Journal, 14(4), 3599–3610. https://doi.org/10.18421/TEM144-65
Jia, Z., & Duraipandi, O. (2025). AI-enhanced strategic management of foreign direct investment in China: Decoding the mediating effects of labor productivity and infrastructure development on economic growth. Future Technology, 4(4), 185–204. https://doi.org/10.55670/fpll.futech.4.4.16
Kaliappan, S., Maranan, R., Malladi, A., & Yamsani, N. (2024). Optimizing resource allocation in healthcare systems for efficient pandemic management using machine learning and artificial neural networks. In: Proceedings of the 2024 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC). January 27-19, 2024, Bhubaneswar, India. IEEE. (pp. 1–7).https://doi.org/10.1109/ASSIC60049.2024.10507961
Kaur, A., & Prakash, G. (2026). Adaptive inventory strategies using deep reinforcement learning for dynamic agri-food supply chains. OPSEARCH, 1-35. https://doi.org/10.1007/s12597-026-01097-y
Khan, M. R. I. (2026). AI-powered predictive analytics for enhancing supply chain resilience and delivery outcomes. Discover Analytics, 4, 13. https://doi.org/10.1007/s44257-026-00065-6
Maitra, S. (2026). Agentic automation-driven reinforcement learning for inventory optimization. Journal of Modeling in Management, 1-36. https://doi.org/10.1108/JM2-07-2025-0347
Manju, N., Sasikala, S., Srinivasan, K., & Rajkamal, S. (2025). The impact of artificial intelligence on supply chain management and logistics optimization. European Economic Letters, 15(4), 110–113. https://www.eelet.org.uk/index.php/journal/article/view/3564
Mao, H. The optimization strategy and application practice of business management supply chain based on artificial intelligence technology. (2025). Procedia Computer Science, 261, 707–715. https://doi.org/10.1016/j.procs.2025.04.324
Nadia, N. Y., Arif, M. H., Rabby, H. R., Tanvir, M. I. M., Hossen, M. J., & Mridha, M. F. (2026). Hybrid deep learning approach for coupled demand forecasting and supply chain optimization. Computers, Materials & Continua, 88(3), 21. https://doi.org/10.32604/cmc.2026.074236
Nanda, N., Misra, S., Bhaumik, A., & Mohapatra, S. (2026). Tourist destinations and organisational ambidexterity: The South Asian perspective. International Journal of Business Information Systems, 51(1), 50–58. https://doi.org/10.1504/IJBIS.2026.150761
Pant, G. D., Duraipandi, O., Ojha, S. K., Lama, P. B., Niroula, A. K., & Hamal, J. B. (2025). The influence of trade unions on employee performance: Insights from the Nepalese cement manufacturing industry. Problems and Perspectives in Management, 23(4), 90–99. https://doi.org/10.21511/ppm.23(4).2025.07
Patil, D. T., Bhaumik, A., & Kolte, A. (2026). Optimizing inventory management in commercial construction through IoT for enhanced cost efficiency. International Journal of Critical Infrastructures, 22(1), 92–116. https://doi.org/10.1504/IJCIS.2026.151575
Perez, H. D., Hubbs, C. D., Li, C., & Grossmann, I. E. (2021). Algorithmic approaches to inventory management optimization. Processes, 9(1), 102. https://doi.org/10.3390/pr9010102
Pirhooshyaran, M., & Snyder, L. V. (2020). Simultaneous decision-making for stochastic multi-echelon inventory optimization with deep neural networks as decision-makers. arXiv. arXiv:2006.05608. https://doi.org/10.48550/arXiv.2006.05608
Qiu, R., Sun, Y., Fan, Z. P., & Sun, M. (2020). Robust multi-product inventory optimization under support vector clustering-based data-driven demand uncertainty set. Soft Computing, 24(9), 6259–6275. https://doi.org/10.1007/s00500-019-03927-2
Rahman Sabbir, M. M., Akhter, S., & Islam, M. S. (2025). Business analytics and machine learning-based demand forecasting and inventory optimization in the grocery supply chain. ACADEMIA International Journal for Social Sciences, 4(4), 2765–2774. https://doi.org/10.63056/ACAD.004.04.1130
Rekabi, S., Sazvar, Z., & Tavakkoli-Moghaddam, R. (2025). A data-driven approach to optimize a blood supply chain within the Industry 5.0 framework: A stochastic optimization model. Expert Systems with Applications, 289, 127960. https://doi.org/10.1016/j.eswa.2025.127960
Shukla, S., & Pillai, V. M. (2022). Stock-out prediction in multi-echelon supply chain using machine learning. In: Proceedings of the 2nd Indian International Conference on Industrial Engineering and Operations Management. August 16-18, ,2022, Warangal, India. (pp. 1258–1270). https://doi.org/10.46254/in02.20220368
Surya, M., & Manohar, S. (2024). An efficient framework for secure agricultural block supply chain for farmer with crop yield and demand prediction. In: Proceedings of the 2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV). March 11-12, 2024, Tirunelveli, India. IEEE. (pp. 395–398). https://doi.org/10.1109/ICICV62344.2024.00068
Tirkolaee, E. B., Sadeghi, S., Mooseloo, F. M., Vandchali, H. R., & Aeini, S. (2021). Application of machine learning in supply chain management: A comprehensive overview of the main areas. Mathematical Problems in Engineering, 2021, 1476043. https://doi.org/10.1155/2021/1476043
Villegas, M. A., Pedregal, D. J., & Trapero, J. R. (2018). A support vector machine for model selection in demand forecasting applications. Computers & Industrial Engineering, 121, 1–7. https://doi.org/10.1016/j.cie.2018.04.042
Wang, J., Li, Y., Zhou, S., Zhang, Y., Xiong, X., & Zhai, W. (2024). Traceability and performance optimization: Application of generative AI, digital twin, and DRL in the recycling process of WEEE. IEEE Internet of Things Magazine, 7(3), 22–28. https://doi.org/10.1109/IOTM.001.2300261
Wang, Y., Wang, L., Huang, K., Hu, Y., Lu, Q., Hou, W., & Zhang, J. (2024). Research on pharmaceutical supply chain decision-making model considering output and demand fluctuations. IEEE Access, 12, 61629–61641. https://doi.org/10.1109/ACCESS.2024.3395120
Win, A. T., Nordin, B. B., Ameen, A., & Bhaumik, A. (2025). Entrepreneurial intention among undergraduates of business private universities in Yangon, Myanmar. International Journal of Public Sector Performance Management, 15(1), 34–42. https://doi.org/10.1504/IJPSPM.2025.143567
Wu, G., Salman, S. A., Huang, B., & Wang, T. (2026). Green innovation and firm performance in China: Empirical insights from A-share listed manufacturers. E a M: Ekonomie a Management, 29(2), 107–123. https://doi.org/10.15240/tul/001/2026-2-008
Wu, R. (2026). Inventory optimization under supply chain disruptions: Leveraging large language models for human-AI collaborative decision-making. Journal of King Saud University – Computer and Information Sciences, 38(2):73. https://doi.org/10.1007/s44443-025-00458-9
Wu, Y. (2024). OEM supply chain elastic operation and improvement strategies under the background of Internet of Things. In: Proceedings of the 2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT). March 15-16, 2024, Bengaluru, India. IEEE. (pp. 1–5). https://doi.org/10.1109/ICDCOT61034.2024.10515431
Yang, D., & Rosli, R. (2026). CSR dimensions and their impact on employee performance: The mediating role of organisational commitment in Beijing’s universities. International Journal of Law and Management, 68(1), 139–154. https://doi.org/10.1108/IJLMA-06-2024-0198
Zohdi, M., Rafiee, M., Kayvanfar, V., & Salamiraad, A. (2022). Demand forecasting based machine learning algorithms on customer information: An applied approach. International Journal of Information Technology, 14(4), 1937–1947. https://doi.org/10.1007/s41870-022-00875-3