Artificial intelligence applications in logistics play a critical role in making supply chain processes end-to-end traceable, flexible, and predictable. Machine learning, deep learning, and forecasting models are extensively used in areas such as demand forecasting, stock and inventory management, warehouse layout planning, route and fleet optimization, shipment scheduling, and delivery performance. This allows businesses to make more accurate decisions, even under conditions of uncertainty, and increase service levels and customer satisfaction while reducing costs.
AI-powered systems not only improve operational efficiency but also offer significant contributions to supply chain risk management and resilience. In the face of geopolitical developments, energy price fluctuations, climate-related disruptions, or sudden demand shocks, scenario analysis, predictive analysis, and simulation techniques can be used to assess alternative supply, production, and distribution plans in advance. Furthermore, data-driven analytical approaches are becoming increasingly important in carbon footprint monitoring, green logistics design, and sustainability reporting.
The development of AI-based solutions in logistics, when considered alongside big data infrastructures, the Internet of Things (IoT), real-time location data, sensor technologies, and cloud computing, creates a multidisciplinary field of study. A rich research base exists for both theoretical model development and applied studies based on field data. Issues such as algorithmic transparency, data quality, ethics, and cybersecurity stand out as key topics to be discussed in the design and implementation of AI projects.
