The impact of artificial intelligence-based technologies on the financial and economic efficiency of logistics systems

Abstract

The article analyzes the impact of artificial intelligence-based technologies on the financial and economic efficiency of logistics systems, taking into account modern trends in digital transformation. In the context of increasing global competition and the rapid development of information technologies, logistics companies are facing new challenges related to optimizing operational processes, reducing costs and increasing profitability. The study examines four key technologies that have the greatest impact on the performance of logistics systems: predictive analytics, robotic platforms and automated systems, pattern recognition systems with smart sensors and intelligent transport solutions. It is demonstrated that their integration helps minimize the human factor, increase the speed of order processing, reduce the number of errors and losses, and improve flexibility in difficult or crisis conditions. Four essential characteristics of such technologies are highlighted: adaptability, data transparency, decision-making automation and scalability, which ensure high dynamism and compliance with modern requirements of the logistics environment. It was found that the introduction of artificial intelligence into logistics allows for the prompt processing of large amounts of information, making informed management decisions and responding to uncertain market conditions in real time. This results in the optimization of all links in the logistics chain - from transportation planning to warehouse inventory management - which directly affects cost savings and increased profitability. The results of the study prove that the use of technologies based on artificial intelligence can not only increase the efficiency of logistics systems, but also lay the foundation for the further innovative development of enterprises. It was established that the formation of a comprehensive strategy for the implementation of such solutions requires a thorough analysis of available resources, possible risks and scaling prospects. The conclusions obtained may be useful for scientists, logistics managers and company leaders seeking to strengthen their positions in the market.