Mudda Mokshitha, Poojitha M L, Anushree T C, Prof. Ananda Shankar A
Abstract
This project focuses on developing an AI-based system for optimizing logistics and supply chain operations. The system leverages machine learning algorithms to predict demand, optimize delivery routes, and manage inventory intelligently. Using data from multiple sources, the AI model identifies patterns and provides actionable insights for better decision-making. The project aims to demonstrate how AI can transform traditional logistics into a smart, adaptive, and efficient system, ensuring timely delivery, resource optimization, and sustainability.
Keywords
logistics, machine learning algorithms and optimized delivery routes.
Conclusion
This research set out to chart how artificial intelligence continues to reshape the field of logistics and supply chain management. Drawing from current literature, systematic reviews, and emerging technological advancements, a clear trend emerges: AI has evolved from a supplementary analytical tool into the core driver of both strategic and operational outcomes. At every stage—planning, sourcing, production, logistics, and even reverse logistics—AI enhances forecasting, increases efficiency, bolsters resilience, and elevates decision-making capabilities. Technologies such as machine learning, deep learning, reinforcement learning, digital twins, multi-agent systems, and generative AI each add distinctive value. Together, they are redefining the structure and performance of modern supply chains.
However, this transformation comes with significant challenges. Implementing AI depends on high-quality data, integrated systems, skilled personnel, robust ethical frameworks, and thoughtful strategy. Many organizations struggle with fragmented data, legacy systems, a lack of technical expertise, or unclear ROI metrics. These challenges are substantial—they create a real divide between the promise of AI and the results most businesses experience. Closing this gap requires strong investment in digital infrastructure, development of human capabilities, and effective change management. Additionally, as autonomous logistics and decentralized multi-agent systems advance, new perspectives on oversight, accountability, and risk become critical.
The AI-driven supply chain framework introduced here offers a roadmap—a holistic strategy that unites data, intelligence, automation, and human factors. It underscores the necessity of a unified data foundation, intelligent analytics layers, autonomous operations, and governance structures that ensure AI remains responsible, secure, and sustainable. This framework is more than conceptual; it provides researchers and practitioners with actionable guidance for developing and scaling AI-enabled supply chains.Looking ahead, several research avenues are particularly important. First, as global uncertainty grows—due to geopolitical instability, climate events, and rare disruptive incidents—it is crucial to explore how AI can enhance supply chain resilience. Existing models often rely heavily on historical data, which is insufficient for unprecedented situations. Creating adaptive, self-learning AI capable of managing sparse or rapidly changing data is a key research need. Second, progress on sustainability must accelerate. AI offers tools to monitor emissions, optimize closed-loop systems, and incorporate environmental metrics into real-time decision-making. With stricter regulations on the horizon, sustainable AI is not optional—it is essential.
Third, the emergence of autonomous supply chain systems raises pressing ethical and governance questions. As AI assumes greater responsibility for decisions, issues of fairness, transparency, and explainability become increasingly important. Research into interpretable AI, frameworks for algorithmic accountability, and robust AI security is needed to maintain trust. Finally, much remains to be learned about the collaboration between humans and AI. How do supply chain professionals engage with these systems, adapt to them, and retain oversight as autonomy grows? The answers will shape not only future workplaces but also the very tools and support structures these professionals depend on.AI is fundamentally transforming logistics and supply chain management, with effects that are both wide-ranging and enduring. Organizations that commit to investing in data, cultivating human expertise, and establishing effective governance will be better positioned—more agile and resilient in a rapidly changing world.
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How to Cite This Paper
Mudda Mokshitha, Poojitha M L, Anushree T C, Prof. Ananda Shankar A (2026). AI-Based Logistics and Supply Chain Management. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
