Intelligent Fleet and Delivery Management: AI Applications for Real-Time Routing, Scheduling, and Performance Control

Abstract

This study examined the impact of artificial intelligence (AI) applications on intelligent fleet and delivery management, focusing specifically on real-time routing, adaptive scheduling, and performance control systems. The research aimed to determine how these AI-driven technologies influenced fleet operational performance in terms of efficiency, reliability, cost reduction, and asset utilization. A quantitative research design was employed, and data were collected from fleet managers and logistics professionals using structured questionnaires. Statistical analyses, including descriptive statistics, reliability testing, correlation analysis, and multiple regression analysis, were conducted to evaluate the relationships among variables. The findings revealed that AI-based real-time routing significantly enhanced route optimization and reduced delivery delays. Adaptive scheduling systems improved responsiveness to fluctuating demand and resource allocation challenges. Performance control mechanisms, particularly predictive maintenance and telematics monitoring, demonstrated the strongest positive influence on operational performance. The regression model indicated that AI applications collectively explained a substantial proportion of variance in fleet performance outcomes. The study concluded that integrated AI frameworks provided measurable operational advantages and strengthened decision-making capabilities in logistics environments. Organizations adopting comprehensive AI-based fleet management systems achieved higher efficiency, improved service quality, and greater competitive advantage. The research contributed to the growing body of knowledge on smart logistics and offered practical implications for managers seeking to optimize fleet operations through advanced analytics and automation technologies.