Research on the Implementation of Artificial Intelligence in the Supply Chain and Logistics Optimization

Abstract

Modern supply chains face high uncertainty and demand for speed. AI enables a shift from reactive to proactive management, evolving from expert systems and basic ML to deep learning, reinforcement learning, and hybrid models. Key applications include demand forecasting, transport logistics optimization, and predictive inventory management. AI with blockchain ensures transparency and smart contracts. Strengths are accuracy and adaptability; weaknesses include high complexity, data dependence, and poor interpretability. Barriers are poor data quality, low digital maturity, staff resistance, skill shortages, and integration issues. Solutions involve unified data spaces, cloud platforms, and phased pilots. Trends include digital twins, autonomous systems, and Green AI. Recommendations aim to boost sustainability and cost-efficiency.