# Harnessing AI for Sustainable Shipping and Green Ports: Challenges and Opportunities
## 3 The Role of AI in Sustainable Shipping
### Operational Optimization
Predictive analytics also enables better inventory management, ensuring that goods are available when needed while minimizing overstock and reducing waste [143, 144, 145]. AI-powered supply chain management systems can optimize cargo-loading and -unloading processes, improving the efficiency of port operations. These systems use ML to determine the best ways to load containers, considering factors such as weight distribution, destination, and cargo type. In order to be responsible for the ship's loading plan, the AI would be integrated into the ship's cargo program, and the exchange of information about the current loading status  
Figure 4: The NAVDEC system on the m/v “Wolin” and proposed solution to the collision situation [138]. would be continuous and automatic. The AI would have access to information on the cargo's destination port, weight, and specifications as well as current hull strength criteria such as shear forces (SF), bending moments (BM), and torsional moments (TM). It would also have access to the vessel's current center of gravity along with the metacentric height (GM), draft, and trim values. Knowing all the stability requirements of the vessel (from ship's documentation), the AI would control the loading operations to ensure compliance with these requirements. Additionally, AI integrated into a ship's cargo program and its ballast system could plan for the extra ballast to be taken or discharged overboard to meet stability requirements and ensure safe draught. However, due to the complexity of the decision-making process and the need to meet legal requirements, such a high degree of autonomy may be highly controversial today. Optimized loading not only maximizes space utilization but also reduces the time ships spend in port, lowering fuel consumption and emissions associated with prolonged docking [146, 147, 148]. Furthermore, AI can enhance transparency and traceability in the supply chain. Blockchain technology, combined with AI, can create immutable records of transactions and movements of goods, ensuring that all stakeholders have access to accurate and up-to-date information. This transparency helps in identifying and addressing inefficiencies, reducing delays, and improving overall supply chain resilience [149, 150, 151]. AI also plays a crucial role in risk management within maritime logistics. ML algorithms can assess risks associated with various routes, cargo types, and geopolitical factors, enabling companies to make informed decisions that minimize disruptions and enhance the reliability of their operations [150, 152]. Lastly, AI can facilitate real-time tracking and monitoring of shipments, providing valuable insights into the status and condition of cargo. This capability is particularly important for perishable goods and high-value items, where timely delivery and proper handling are critical. Real-time monitoring helps in proactive issue resolution, reducing losses and ensuring customer satisfaction [153, 154, 155]. AI-driven advancements in autonomous shipping and logistics management are pivotal for optimizing maritime operations. By enhancing route planning, safety, cargo handling, and supply chain transparency, AI contributes to a more efficient, sustainable, and resilient maritime industry [156, 157].