Integration of Information Technology and Machine Learning to Improve the Efficiency of IoT-Based Logistics Systems
Abstract
In today's digital era, efficiency in supply chain management and logistics is the main key to maintaining business competitiveness. This article discusses the integration of Information Technology (IT) and Machine Learning (ML) in Internet of Things (IoT)-based logistics systems to improve operational efficiency. By leveraging IoT sensors for real-time data collection and ML algorithms for predictive analysis, the system is able to optimize inventory management, route planning, and preventive maintenance. The case studies discussed in this article show that the use of ML in IoT-based logistics systems can reduce delivery times, lower operational costs, and increase responsiveness to changes in market demand. The results of this study are expected to provide insight for system developers and logistics managers in implementing advanced technologies to address challenges in the modern logistics industry.
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