APPLICATION OF ARTIFICIAL INTELLIGENCE IN PRODUCTION LOGISTICS: MODERN PRACTICES AND DEVELOPMENT PROSPECTS

Abstract

The rapid advancement of artificial intelligence (AI) technologies is transforming the landscape of industrial logistics by enabling more adaptive, efficient, and intelligent supply chain systems. This article investigates the strategic role of AI in reshaping production logistics in the context of Industry 4.0. The study emphasizes how AI tools such as machine learning, computer vision, digital twins, generative models, and predictive analytics are revolutionizing logistics functions including demand forecasting, inventory control, warehouse automation, route optimization, and quality assurance. A comprehensive review of current academic and industrial research is conducted, drawing on international case studies from countries such as Germany, the United States, and Japan. The article discusses successful implementations, such as Amazon’s autonomous warehouse systems and predictive maintenance applications in the automotive sector. The research outlines key technological and organizational barriers to AI adoption, including data quality issues, lack of skilled personnel, high implementation costs, resistance to change, and limited digital maturity of enterprises. Special attention is given to the role of digital twins as a simulation tool for stress testing logistics networks under conditions of uncertainty and external shocks. The findings suggest that AI is not merely an automation instrument but a foundational technology enabling real-time decision-making, system adaptability, and resilience in production logistics. The Ukrainian context is explored in detail, noting both the potential and structural constraints of AI deployment in the national industrial sector. The article concludes with specific recommendations for further research and policy development, including integrating AI into logistics curricula, creating national standards, and encouraging public-private partnerships and pilot projects. This study contributes to the theoretical and practical understanding of how AI-driven logistics can enhance productivity, sustainability, responsiveness, and long-term competitiveness in modern manufacturing ecosystems.