AI-Driven Optimization in Supply Chain Operations: A Machine Learning Framework for Demand Forecasting and Inventory Management
DOI:
https://doi.org/10.63503/acset.978-81-995593-9-4.73Keywords:
Supply Chain Optimisation, Machine Learning, LSTM, XGBoost, Demand Forecasting, Inventory Management, Deep LearningAbstract
Supply chain operations are among the most intricate sectors of modern business: their characteristics are high-dimensional data, time-variant demand dynamics, and cascading failures. In this work, a novel Hybrid Machine Learning framework comprising Long Short-Term Memory (LSTM) networks and an XGBoost ensemble is proposed to accurately forecast demand and optimise inventory replenishment. The framework is applied to a real retail supply chain dataset spanning 36 months and 500+ SKUs. The framework outperforms the benchmark by 34% (MAPE from 9.6% to 6.3%), with 28% fewer stockouts and 19% less excess inventory. The LSTM module consumes a 28-day sliding window of historical demand, using a 2-layer stacked architecture (128 and 64 units) to output a 64-dimensional temporal embedding that captures the long-range dependencies of the demand sequence. The embedding is concatenated with 42 engineered features that capture temporal indicators, lag and rolling features, promotion, supply-side data, and external features, producing a 106-dimensional feature vector for the XGBoost ensemble predictor. A downstream inventory replenishment module dynamically calculates safety stock, reorder point, and order-up-to level based on forecast error and substitutes static classical inventory policies with adaptive, data-driven strategies. The method is evaluated on an actual FMCG retail supply chain dataset, including 536 SKUs over 36 months and roughly 7.2M transaction records. HybridSCF reports a 6.32% MAPE for a 7-day forecast horizon, a 34% increase over ARIMA, reduces stockout occurrences by 27.4%, decreases the excess inventory ratio by 18.4%, and reduces the total annual inventory holding cost from $42.8M to $35.6M (16.8% reduction). Ablation studies demonstrate the effectiveness and individual contributions of the LSTM encoder and the dynamic safety stock module. SHAP-based interpretability is implemented to enable easy, accountable deployment of the automated replenishment solution in the real world.
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[110] Nagpal, S., Garg, P., Gaba, S., & Aggarwal, A. (2023). 13 An improved genetic quantum cryptography model for network communication. Quantum-Safe Cryptography Algorithms and Approaches: Impacts of Quantum Computing on Cybersecurity, 177.
[111] Yadav, M., Swami, V., Kumar, N., & Garg, P. (2025). Comparative study of Repairable Juice Plants using RPGT. Reliability: Theory & Applications, 20(2 (84)), 776-783.
[112] Gupta, A., Garg, P., & Yadav, P. (2025). Role of Generative AI Towards Education and Learning: Present & Future. TPM–Testing, Psychometrics, Methodology in Applied Psychology, 32(S6 (2025): Posted 15 Sept), 1059-1076.
[113] Dalal, P., Beniwal, G., Sharma, V., Garg, P., & Ahmed, K. (2025). Predicting Student Motivation and Engagement through Machine Learning Models. TPM–Testing, Psychometrics, Methodology in Applied Psychology, 32(S7 (2025): Posted 10 October), 393-411.
[114] Gupta, A., Mund, A., Roy, S., Garg, P., & Yadav, D. K. (2025). Trust in AI Systems: A Social-psychological Investigation of Human–AI Collaboration. TPM–Testing, Psychometrics, Methodology in Applied Psychology, 32(S7 (2025): Posted 10 October), 428-446.
[115] Bhardwaj, A., Das, A., Garg, P., & Yadav, S. (2025). Material-Driven Performance Analysis of a Vertical Nanowire Tunnel FET for Analogue Applications: Bhardwaj, Das, Garg, and Yadav. Journal of Electronic Materials, 1-12.
[116] Dalal, P., Sharma, B., Sharma, T., Garg, P., & Ahmed, K. (2025). Explainable AI for Understanding Human Decision-Making Patterns. TPM–Testing, Psychometrics, Methodology in Applied Psychology, 32(S7 (2025): Posted 10 October), 412-427.
[117] Sharma, K. K., Verma, P. K., Garg, P., & Shrotriya, V. K. (2025, October). Predicting costs and benefits of IoT-based energy management for optimising sustainable energy storage in rural areas. In AIP Conference Proceedings (Vol. 3343, No. 1, p. 040017). AIP Publishing LLC.
[118] Ahmed, K., Baranwal, A., Sharma, N., Garg, P., & Singh, N. (2026). The Role of Federated Learning in AI-Powered Integrated Healthcare Solutions. In Enabling Collaborative Health Intelligence With Federated Learning (pp. 421-448). IGI Global Scientific Publishing.
[119] Gupta, S., Garg, P., Agarwal, J., Thakur, H. K., & Yadav, S. P. (2025). Federated learning-based intelligent systems to handle issues and challenges in IoVs (Part 2). Bentham Science Publishers. https://doi.org/10.2174/97898153222241250301
[120] Garg, P., Pranav, S., & Prerna, A. (2021). Green internet of things (G-IoT): A solution for sustainable technological development. In Green Internet of Things for Smart Cities (pp. 23-46). CRC Press.
[121] Malik, A., Nandal, D., Gupta, V., Garg, P., & Nandal, V. INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING.
[122] Gupta, S., Garg, P., Agarwal, J., Thakur, H. K., & Yadav, S. P. (Eds.). (2025). Federated learning-based intelligent systems to handle issues and challenges in IoVs (Part 2).
[123] Garg, P., Bhatt, M., Parmar, R., & Arsalan, M. (2025). Generative AI: Evolution, Applications, Challenges, and Future Prospects. Applications, Challenges, and Future Prospects (May 17, 2025).
[124] Kumar, N., Kumar, Y., Khurana, D., Kumar, S., & Garg, P. (2025, November). A Hybrid Ensemble Learning Framework for Interpretable Student Performance Prediction Using Academic and Extracurricular Factors. In 2025 International Conference on Innovations and Emerging Technologies in AI & Communication Systems (IETACS) (pp. 666-672). IEEE.
[125] Khurana, D., Kumar, Y., Kumar, N., Kumar, S., & Garg, P. (2025, November). Transformer-Based Movie Recommendation System with Autoencoder-Enhanced Feature Compression. In 2025 International Conference on Innovations and Emerging Technologies in AI & Communication Systems (IETACS) (pp. 685-690). IEEE.
[126] Garg, P. (2025, November). Comparative Analysis of Various Neural Networks for Galaxy Classification. In 2025 International Conference on Innovations and Emerging Technologies in AI & Communication Systems (IETACS) (pp. 697-701). IEEE.
[127] Saggu, A. K., Babbar, N., & Garg, P. (2025, November). Health-Guard AI: Integrated Health Report Management and Analysis. In 2025 International Conference on Innovations and Emerging Technologies in AI & Communication Systems (IETACS) (pp. 614-623). IEEE.
[128] Kumar, S., Kumar, Y., Kumar, N., Khurana, D., & Garg, P. (2025, November). Hybrid FCM-DNN Model for Uncertainty-Aware Air Quality Classification Using Multi-Pollutant Data. In 2025 International Conference on Innovations and Emerging Technologies in AI & Communication Systems (IETACS) (pp. 679-684). IEEE.
[129] Babbar, N., Singh, H. V., Bendale, S., & Garg, P. (2025, November). Stock Market Price Prediction Using Big Data Analysis: A Performance Evaluation Study. In 2025, the 3rd International Conference on Computational Intelligence and Network Systems (CINS) (pp. 1-6). IEEE.
[130] Singh, A. K., Kori, G., Garg, P., & Srivastava, G. (2025, November). Bank Churn Prediction Using Machine Learning. In 2025, IEEE 7th International Conference on Computing, Communication and Automation (ICCCA) (pp. 1-6). IEEE.
[131] Bhardwaj, A., Das, A., Garg, P., & Yadav, S. (2026). Material-Driven Performance Analysis of a Vertical Nanowire Tunnel FET for Analogue Applications. Journal of Electronic Materials, 55(1), 1099-1110.
[132] Srivastava, A. K., Shankdhar, D., Ror, R., & Garg, P. (2026). Harnessing YOLOv5 for real-time object detection: A cloud-based approach. In Recent Advances in Computational Methods in Science and Technology (pp. 441-450). CRC Press.
[133] Srivastava, A. K., Shukla, A., Gupta, H., Saxena, K., & Garg, P. (2026). Towards an intelligent attendance management system with face recognition using the LBPH algorithm. In Recent Advances in Computational Methods in Science and Technology (pp. 8-15). CRC Press.
[134] Srivastava, A. K., Garg, P., & Pandey, H. (2026). Vedcure: Towards intelligent ayurvedic drug recommendation and disease prediction. In Recent Advances in Computational Methods in Science and Technology (pp. 16-23). CRC Press.
[135] Upadhyay, D., Garg, P., & Babbar, N. (2026). A blockchain- and IoT-based smart contract framework for efficient and secure product life management. Discover Internet of Things.
[136] Singh, A., Parmar, R., Bhardwaj, P., Sharma, V., & Garg, P. (2026). Fusion of Aerial Networks with Advanced Computing Paradigms. Edge Computing and Aerial Platforms, 355-367.
[137] Kumari, M., Baranwal, A., Sonal, & Garg, P. (2026). Application of Aerial Edge Computing in Disaster Management. Edge Computing and Aerial Platforms, 103-122.
[138] Aditi, Saraswat, P., Sharma, V., & Garg, P. (2026). Advances in Aerial Platforms and Edge Computing. Edge Computing and Aerial Platforms, 123-143.
[139] Garg, P., Arora, K., Bawane, R., Gupta, C., & Ahmed, K. (2025). Detection and Prevention of Cyber Attacks and Threats using AI.
[140] Ahmed, K., Ahmed, A., Khan, J., Garg, P., Seth, S., & Mallik, S. (2025). Principal Component Analysis-Based Clustering of Insecticides and Molecular Docking of Pyrethroid Insecticides.
[141] Kumar, B., Kumar, A., Nanwal, J., Garg, P., & Patnaik, P. (2025, November). Ensemble of YOLOv5 and Segment Anything Model for Brain Tumour Detection. In 2025, the 2nd International Conference on Advanced Computing and Emerging Technologies (ACET) (pp. 1-5). IEEE.
[142] Arsalan, M., Anas, M., & Garg, P. (2025). Transparent AI for Drug Discovery and Development. Available at SSRN 5844242.
[143] Singh, A., Bhardwaj, P., Garg, P., & Singh, N. (2026). Introduction to explainable artificial intelligence in healthcare. In Explainable AI in Clinical Practice (pp. 23-44). Academic Press.
[144] Kapoor, S., Singh, A., Garg, P., & Ramasamy, L. K. (2026). Explainable artificial intelligence in a diagnostic support system. In Explainable AI in Clinical Practice (pp. 131-145). Academic Press.
[145] Ahmed, K., Anas, M., & Garg, P. (2026). Case studies on unlocking the potential of Industry 4.0 for sustainable manufacturing through generative AI-driven innovations. Available at SSRN 6356958.
[146] Garg, P., & Oruganti, S. K. (2026, March). AI Assisted Routing Optimisation in Opportunistic IoT Networks using Machine Learning: A Comprehensive Review on Protocols & Simulators. In Sustainable Global Societies Initiative (Vol. 1, No. 4). Vibrasphere Technologies.
[147] Arsalan, M., Pokhrel, L., & Garg, P. (2026). Architecture, Components, and tools in Integrated AI-Augmented Intelligence: A design perspective. Components and tools in Integrated AI-Augmented Intelligence: A design perspective (March 19, 2026).
[148] Singh, H., Ahmed, K., & Garg, P. (2026). Human Versus Machine Customer Behaviour and Functional Differences. Available at SSRN 6441098.
[149] Saraswat, P., & Garg, P. (2026). Soft Computing In AI Agents.
[150] Saraswat, P., & Garg, P. (2026). Water Quality Prediction Using IOT Sensors and Deep Networks.
