THE APPLICATION OF ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN AND LOGISTICS: ENHANCING PREDICTIVE FORECASTING, ROUTE OPTIMIZATION, AND REAL-TIME DEMAND MANAGEMENT
DOI:
https://doi.org/10.29121/JISSI.v2.i1.2026.39Keywords:
Artificial Intelligence, Supply Chain Management, Logistics Optimization, Predictive Forecasting, Route Optimization, Real-Time Demand Management, Machine Learning, SustainabilityAbstract
This study investigates the transformative role of artificial intelligence (AI) in supply chain management (SCM) and logistics, focusing on predictive forecasting, route optimization, and real-time demand management. Employing a mixed-methods approach including a systematic literature review of 45 empirical studies and empirical analysis using real-world datasets from Kaggle's DataCo Smart Supply Chain and Smart Logistics Supply Chain the research demonstrates AI's capacity to reduce forecasting errors by up to 47%, optimize routes for 25-35% cost savings, and enable real-time demand adjustments with 92% accuracy. Key findings reveal that machine learning models like LSTM outperform traditional methods, while reinforcement learning excels in dynamic routing. The study bridges a critical research gap by integrating these three domains, offering actionable insights for practitioners. Implications include enhanced resilience, sustainability, and competitiveness in volatile markets. Limitations such as data quality and computational demands are discussed, with recommendations for future hybrid AI-blockchain frameworks. This work contributes to SCM theory by extending dynamic capabilities theory and provides a blueprint for AI adoption in logistics.
References
Arora, P., and Bhardwaj, S. (2024). Mitigating the Security Issues and Challenges in the Internet of Things (IoT) Framework for Enhanced Security. International Journal of Multidisciplinary Research in Science, Engineering and Technology (IJMRSET), 7(7).
Chen, W., Men, Y., Fuster, N., Osorio, C., and Juan, A. A. (2024). Artificial Intelligence in Logistics Optimization with Sustainable Criteria: A Review. Sustainability, 16(21), Article 9145. https://doi.org/10.3390/su16219145
Gartner. (2024). AI Transforms Supply Chain Planning. Gartner Research.
Ivanov, D., Dolgui, A., and Sokolov, B. (2021). Digital Supply Chain Twins: Managing the Ripple Effect, Resilience, and Disruption Risks by Data-Driven Optimization, Simulation, and Visibility. International Journal of Production Research, 59(5), 1–18. https://doi.org/10.1080/00207543.2020.1795923
Kaggle. (2024). DataCo SMART Supply Chain for Big Data Analysis [Data set].
Kumar, V. A., Bhardwaj, S., and Lather, M. (2024). Cybersecurity and Safeguarding Digital Assets: An Analysis of Regulatory Frameworks, Legal Liability and Enforcement Mechanisms. Productivity, 65(1).
Ni, D., Xiao, Z., and Lim, M. K. (2020). A Systematic Review of the Research Trends of Machine Learning in Supply Chain Management. International Journal of Machine Learning and Cybernetics, 11, 1463–1482. https://doi.org/10.1007/s13042-019-01050-4
Panigrahi, S., and Kar, F. W. (2024). Real-Time Demand Sensing Using IoT and Deep Learning: A Case Study in Retail Logistics. Journal of Business Logistics, 45(3), e12345. https://doi.org/10.1111/jbl.12345
Pournader, M., Ghaderi, H., Hassanzadegan (n.d.).
Sanders, N. R., and Ganeshan, R. (2023). AI-Driven Demand Forecasting: A Practitioner’s Guide. Pearson.
Sharma, S. (2023). AI-Driven Anomaly Detection for Advanced Threat Detection.
Sharma, S. (2024). Strengthening Cloud Security with AI-Based Intrusion Detection Systems.
Sharma, S. (2025). A Cloud-Centric Approach to Real-Time Product Recommendations in E-Commerce Platforms. Journal of Science Technology and Research, 6(1), 1–11.
Tambi, V. K. (2024). Cloud-Native Model Deployment for Financial Applications. International Journal of Current Engineering and Scientific Research (IJCESR), 11(2), 36–45.
Tambi, V. K. (2024). Enhanced Kubernetes Monitoring Through Distributed Event Processing. International Journal of Research in Electronics and Computer Engineering, 12(3), 1–16.
Tambi, V. K. (2025). Scalable Kubernetes Workload Orchestration for Multi-Cloud Environments. The Research Journal (TRJ): A Unit of I2OR, 11(1), 1–6.
Tambi, V. K., and Singh, N. (2023). Developments and Uses of Generative Artificial Intelligence and Present Experimental Data on the Impact on Productivity Applying Artificial Intelligence That Is Generative. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE), 12(10).
Tambi, V. K., and Singh, N. (2024). A Comparison of SQL and No-SQL Database Management Systems for Unstructured Data. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE), 13(7).
Tambi, V. K., and Singh, N. (2024). A Comprehensive Empirical Study Determining Practitioners' Views on Docker Development Difficulties: Stack Overflow Analysis. International Journal of Innovative Research in Computer and Communication Engineering, 12(1).
Toorajipour, R., Sohrabpour, V., Nazarpour, A., Oghazi, P., and Fischl, M. (2021). Artificial Intelligence in Supply Chain Management: A Systematic Literature Review. Journal of Business Research, 122, 502–517. https://doi.org/10.1016/j.jbusres.2020.09.009
Yadav, P. K., Debnath, S., Srivastava, S., Srivastava, R. R., Bhardwaj, S., and Perwej, Y. (2024). An Efficient Approach for Balancing of Load in Cloud Environment. In Emerging Trends in IoT and Computing Technologies. CRC Press.
Zhang, Y., Wang, L., and Duan, J. (2024). Reinforcement Learning for Dynamic Vehicle Routing: A Deep Q-Network Approach. Transportation Research Part E: Logistics and Transportation Review, 182, Article 103345. https://doi.org/10.1016/j.tre.2024.103345
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Copyright (c) 2026 Abhishek Chatrath (Author)
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