Open Access
A Comprehensive Review of Machine Learning Techniques for Retail Supply Chain Optimizations
4
Department of Master Computer Application, Lakshmi Narain College of Technology, Bhopal, India
Abstract
The rapid growth of digital retail platforms, changing customer preferences, and increasing market uncertainty have created significant challenges in managing modern retail supply chains. Supply chain performance optimisation, real-time decision-making, effective inventory management, and accurate demand forecasting are crucial for staying competitive. By facilitating data-driven prediction, pattern recognition, and automated decision support across a variety of supply chain processes, machine learning (ML) approaches provide encouraging possibilities. With an emphasis on optimisation of logistics, supplier selection, demand forecasting, inventory management, warehouse automation, risk management, and ML, this article offers a thorough examination of machine learning's uses in retail supply chain optimisation. Existing ML approaches, including supervised learning, ensemble methods, deep learning models, and hybrid optimization techniques, are analyzed based on their capabilities, benefits, and limitations. The review highlights that ML-driven solutions improve forecasting accuracy, reduce operational costs, enhance resource utilization, and increase supply chain resilience. There are still major obstacles, though, and they have to do with data quality, the interpretability of models, scalability, deployment in real-time, and interface with current systems. Intelligent, explicable, and scalable ML frameworks are necessary to enable decision-making throughout the retail supply chain, according to a comprehensive review of current studies that found research gaps. The findings of this review provide insights into current advancements and future research directions for developing adaptive and transparent machine learning-based retail supply chain optimization systems.
Keywords
Retail Supply Chain Management,
Supply Chain Optimization,
Demand Forecasting
References
S. N. Bhagwat, “A Comprehensive Survey on Advanced Demand Forecasting Techniques : Statistical , Machine Learning , and Hybrid Approaches for Retail , Supply Chain , and E-Commerce,” Int. Adv. Res. J. Sci. Eng. Technol., vol. 12, no. 8, pp. 188–193, 2025, doi: 10.17148/IARJSET.2025.12823.
Paril Ghori, “A Systematic Review of Time Series and Machine Learning Techniques for Demand Forecasting and Inventory Management in Retail Supply Chains,” Int. J. Sci. Res. Sci. Eng. Technol., vol. 7, no. 1, pp. 360–369, Jan. 2020, [Online]. Available: https://ijsrset.com/
H. Ravilla, “Building Scalable Applications with Heroku and Salesforce Integration,” Am. J. Technol., vol. 4, no. 3, pp. 15–36, 2025.
Kshitij Dixit, “Predictive Analytics in Business Intelligence for Sales Forecasting,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 3, no. 1, p. 981, Sep. 2023, doi: 10.48175/IJARSCT-12750G.
R. Sohel, A. Osman, M. S. Ab. Halim, and A. Halim, “A Revisited Retail Supply Chain Management (SCM) Practices: A Conceptual Framework,” vol. 4, pp. 307–317, 2014.
K. K. Mohammed, “Leadership Practices of Data Engineering for AI and Machine Learning,” Int. J. Sci. Res. Eng. Trends, vol. 12, no. 1, pp. 1–5, 2026.
M. Sahu, “Strengthening Supply Chain for Growth of Organized Retail Industry,” IOSR J. Bus. Manag., vol. 19, no. 01, pp. 30–33, 2017, doi: 10.9790/487x-1901073033.
S. Krishna Anumula, “NEXT-GEN SUPPLY CHAINS: A PRODUCT LIFECYCLE MANAGEMENT BASED APPROACH TO RESILIENT AND SUSTAINABLE OPERATIONS,” Int. J. Manag. Value Supply Chain., vol. 16, no. 3, pp. 01–11, Sep. 2025, doi: 10.5121/ijmvsc.2025.16301.
A. Joon, S. A. Pahune, S. Mathur, and S. Rongala, “Designing Predictive Analytics Frameworks with ML for ERP-Supported E-Commerce Systems,” in 2025 International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC), IEEE, May 2025, pp. 1–6. doi: 10.1109/ASSIC64892.2025.11158452.
N. Sabilla and W. Novianto, “The Influence of Supply Chain Models on Retail Supply Chain Performance: A Review of the International Literature Based on Quantitative and Collaborative Models,” SSRN Electron. J., 2026, doi: 10.2139/ssrn.5929562.
S. Kukar, “Explainable AI Models for Transparent and Ethical Customer Relationship Management Decision,” in 2026 IEEE 2nd International Conference on Secure IoT, Assured and Trusted Computing (SATC), IEEE, Mar. 2026, pp. 1–7. doi: 10.1109/SATC69565.2026.11542554.
M. Mittal, “AI IN RETAIL: TRANSFORMING THE CUSTOMER EXPERIENCE THROUGH INTELLIGENT AUTOMATION,” Int. Res. J. Mod. Eng. Technol. Sci., vol. 07, no. 03, 2025, doi: https://www.doi.org/10.56726/IRJMETS69338.
S. Krishna Anumula, “DESIGN-BASED SUPPLY CHAIN OPERATIONS RESEARCH MODEL: FOSTERING RESILIENCE AND SUSTAINABILITY IN MODERN SUPPLY CHAINS,” in Artificial Intelligence and Applications, Academy & Industry Research Collaboration, Oct. 2025, pp. 345–355. doi: 10.5121/csit.2025.151927.
R. Palwe, “The Role of User Experience Design in Advancing Digital Sustainability,” Int. J. Sci. Res., vol. 14, no. 10, pp. 1168–1176, 2025.
S. Singh, S. A. Pahune, P. Chatterjee, and R. Sura, “Advanced Machine Learning Methods for Churn Prediction and Classification in Telecom Sector,” in 2025 IEEE 6th India Council International Subsections Conference (INDISCON), IEEE, Aug. 2025, pp. 1–7. doi: 10.1109/INDISCON66021.2025.11252233.
A. Ahmed --, A. Ahmed, and A. Professor, “Artificial Intelligence’s Integration in Supply Chain Management: A Comprehensive Review,” Issue, vol. 13, no. 3, pp. 2323–5233, 2023.
J. C. P. Yu, “Optimal deteriorating items inventory model with a three-echelon supply chain strategic alliance,” Asia-Pacific J. Oper. Res., 2010, doi: 10.1142/S0217595910002958.
R. I. Muhamedyev, “Machine learning methods: An overview,” Comput. Model. New Technol., vol. 19, no. 6, pp. 14–29, 2015.
R. I. Omer, R. Hassan, and M. S. A. Aziz, “A Review and Comparative Analysis of Predictive Models for Supply Chain Demand Forecasting,” in 2025 10th International Conference on Information and Communication Technology for the Muslim World (ICT4M), 2025, pp. 1–7. doi: 10.1109/ICT4M68001.2025.11363509.
I. Vlachos and P. G. Reddy, “Machine learning in supply chain management: systematic literature review and future research agenda,” Int. J. Prod. Res., vol. 63, no. 16, pp. 5987–6016, Aug. 2025, doi: 10.1080/00207543.2025.2466062.
Y. Zhao, X. Wang, X. Feng, H. Gao, D. Liang, and Q. Fu, “Research on Supply Chain Data Security Optimization Algorithm Based on Artificial Intelligence,” in Proceedings - 2025 International Conference on Algorithm, Artificial Intelligence and Computer Vision, AAICV 2025, 2025. doi: 10.1109/AAICV66571.2025.00064.
C. B. Mupparaju, M. Nalluri, A. S. Rongali, and B. Ananthan, “An Efficient Food Distribution and Logistics Based on Supply Chain,” in 2024 3rd International Conference on Artificial Intelligence for Internet of Things, AIIoT 2024, 2024. doi: 10.1109/AIIoT58432.2024.10574746.
N. R. Tungare and D. G. Jha, “A Framework for Development of Retail Data Analytic App for a Supermarket Chain,” in 2023 Somaiya International Conference on Technology and Information Management, SICTIM 2023, 2023. doi: 10.1109/SICTIM56495.2023.10104810.
K. G. M. Madhushan and C. A. Kavirathna, “Network Design Optimization for Retail Distribution Supply Chain Considering Capacities of Distribution Centers under Disruptions,” in Proceedings - International Research Conference on Smart Computing and Systems Engineering, SCSE 2022, 2022. doi: 10.1109/SCSE56529.2022.9905180.
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