COMPARATIVE ANALYSIS OF DECENTRALIZED LAST MILE DELIVERY SYSTEMS

Decentralized last-mile delivery is a modern logistics approach that optimizes the delivery process by using local hubs or microhubs located near the final recipients. This reduces the distance between the hubs and the delivery location, which reduces transportation time and operating costs. At the same time, customers receive their orders faster, which increases their satisfaction with the service. This approach is extremely relevant amid the growing popularity of e-commerce, where speed and accuracy of delivery are crucial to maintaining customer loyalty. However, the successful operation of a decentralized delivery model requires a sophisticated logistics management system. Artificial intelligence technologies come to the rescue to solve a number of tasks, such as route optimization, inventory management, and demand forecasting. For example, machine learning algorithms are able to adapt courier routes in real time to changes in traffic, weather conditions, and road conditions, which reduces fuel costs and reduces the number of vehicles on the road. This is especially important in large urban areas with heavy traffic, where even small delays can significantly affect the overall delivery time. AI also plays an important role in inventory management, allowing for more accurate planning of replenishment of goods in individual local hubs, avoiding excessive accumulation of products or their shortage. By analyzing historical sales data and consumer behavioral patterns, AI systems can predict the demand for different categories of goods in certain areas. This helps minimize storage costs and ensure that products are available in the right place and at the right time, which in turn increases delivery efficiency. Thus, a decentralized last-mile delivery model involving artificial intelligence allows logistics companies to significantly improve the quality of service, increase the efficiency of all logistics processes, and reduce the negative impact on the environment.