INTEGRATION OF LITTLE’S LAW AND SIMULATION MODELS FOR SMART LOGISTICS OPTIMIZATION

Abstract

In the era of Industry 4.0, logistics systems are becoming increasingly complex and dynamic, necessitating intelligent strategies for performance optimization. This study explores the integration of Little’s Law, a foundational theorem in queuing theory, with discrete-event simulation models to optimize operations in smart logistics networks. The research focuses on modeling key logistics parameters such as arrival rates, service times, and system capacity, enabling accurate predictions of system behavior under varying load conditions. By applying Little’s Law within simulation environments, the study identifies bottlenecks, quantifies average wait times, and enhances throughput across multiple logistics scenarios including warehouse management, last-mile delivery, and order fulfillment. The proposed hybrid approach demonstrates improved operational efficiency and decision-making capabilities when compared to traditional analytics methods. Results are validated using real-time logistics data, showcasing the practicality and robustness of the integrated model for real-world applications. This paper contributes a novel methodology for logistics planners and operations managers aiming to develop data-driven, adaptive, and efficient logistics ecosystems.