OPTIMIZING LOGISTICS WITH AI, IOT, AND COPRAS: REAL-TIME SHIPMENT TRACKING

Abstract

The rapid growth of global logistics and e-commerce has heightened the demand for efficient shipment tracking systems. This paper explores the integration of Artificial Intelligence (AI) and the Internet of Things (IoT) to provide real-time shipment tracking and predictive analytics. Leveraging IoT devices for data collection and AI algorithms for analysis, this hybrid model enhances operational efficiency and minimizes delays. Key contributions include a proposed hybrid IoT-AI model for tracking and predictive delay management, which aims to redefine shipment tracking processes by offering actionable insights. Introduction: Real-time shipment tracking using Artificial Intelligence (AI) and the Internet of Things (IoT) revolutionizes logistics by providing accurate, real-time visibility into supply chain operations. IoT-enabled sensors and GPS devices continuously monitor shipment location, temperature, humidity, and security, while AI analyzes data to predict delays, optimize routes, and enhance decision-making. This integration improves efficiency, reduces losses, and enhances customer satisfaction by providing live updates and proactive alerts. AI-driven automation and predictive analytics further streamline logistics, ensuring secure, efficient, and transparent shipment management. The combination of AI and IoT is transforming global supply chains with smarter, data-driven solutions. Research significance: The significance of research on Real-Time Shipment Tracking Using AI and IoT lies in its ability to enhance efficiency, security, and transparency in supply chain management. AI-driven analytics improve predictive insights, optimize routes, and reduce delays, while IoT-enabled sensors provide real-time data on shipment location, temperature, and condition. This integration minimizes losses, enhances customer satisfaction, and streamlines logistics operations. The research contributes to smarter, data-driven decision-making, reducing operational costs and improving overall supply chain resilience. Additionally, AI and IoT technologies support automation and risk mitigation, making global logistics more reliable and adaptive to disruptions. Methodology: The methodology for Real-Time Shipment Tracking Using AI and IoT involves integrating smart sensors, GPS, and AI-driven analytics to monitor shipments in real-time. IoT-enabled devices collect data on location, temperature, humidity, and security status, transmitting it to a cloud-based system. AI algorithms analyze this data for anomalies, route optimization, and predictive insights, enhancing shipment security and efficiency. Machine learning models predict delays and risks, while blockchain ensures data integrity. A user-friendly dashboard provides stakeholders with live updates, alerts, and reports. This methodology ensures seamless logistics management, reduced losses, and enhanced decision-making for supply chain operations. Alternative: SmartTrack Pro, IoT Ship Monitor, AI-Logistics Hub, Track Sense 360, TrackSense 360 Evaluation preference: Accuracy (%), Response Speed (ms), Scalability, User Satisfaction Results: SmartTrack Pro, IoT Ship Monitor, AI-Logistics Hub, Track Sense 360, Accuracy (%), Response Speed (ms), Scalability