Year: 2025
Type: article
Abstract: Integrating Large Language Models (LLMs) into enterprise-scale microservices architectures presents transformative opportunities for the logistics sector to enhance operational efficiency and customer experience. This article introduces a comprehensive architectural framework that seamlessly incorporates LLMs into complex, high-throughput logistics environments built on modern cloud-native technologies. The framework addresses critical integration challenges across three core logistics functions: automated data processing workflows, intelligent customer support systems, and operational optimization mechanisms. By leveraging established microservices patterns with Spring Boot, Spring Cloud, and message-driven architectures using Kafka, the framework enables LLMs to augment existing services while maintaining system scalability and reliability. The architectural design emphasizes asynchronous communication patterns, distributed caching strategies, and robust security measures to ensure enterprise-grade performance across AWS, Azure, GCP, and VMware Tanzu platforms. Key technical considerations include latency optimization through strategic service placement, data consistency management across distributed systems, and security frameworks that protect sensitive logistics data while enabling AI-driven insights. The framework demonstrates significant improvements in system automation, customer responsiveness, and operational cost optimization, providing logistics enterprises with a proven blueprint for LLM integration that accelerates digital transformation initiatives while maintaining the robustness required for mission-critical operations.
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4411464259