Received 01.08.2024, Revised 17.10.2024, Accepted 15.11.2024
This article examines the role of artificial intelligence (AI) in global supply chains, focusing on its impact on optimizing logistics processes, inventory management, demand forecasting, and risk management. AI in modern supply chains enables cost reduction, automation of routine tasks, shorter delivery times, and improved customer service accuracy. The study is based on an analysis of scientific literature, applied research in the field of artificial intelligence and supply chain management, as well as practical cases of leading companies. The methods used include data analysis, comparative analysis of the effectiveness of process automation using AI, and studying the impact of AI on key indicators of supply chain management. Best practices for implementing artificial intelligence at different stages of the supply chain are considered to identify promising areas of technology use. Machine learning algorithms, which are at the core of AI, can analyze vast amounts of data and forecast demand based on various factors such as seasonal fluctuations, weather conditions, and economic trends. This approach to supply chain management provides businesses with the flexibility and adaptability needed to respond to changing market conditions and maintain competitiveness in a global environment. The article also discusses automated inventory management systems that can forecast and make decisions with minimal human intervention, reducing risks and increasing logistics efficiency. Furthermore, AI offers new opportunities for dynamic pricing and personalized marketing strategies, allowing businesses to better meet customer needs. The conclusion emphasizes the need for further research on AI in supply chains to explore risks, ethical considerations, and the potential for enhancing the resilience and efficiency of business processes over the long term
artificial intelligence; supply chains; automation; demand forecasting; machine learning; logistics optimization; risk management; digital transformation; dynamic pricing; marketing personalization
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