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AI is revolutionizing supply chain management by processing vast data, extracting valuable insights, and making intelligent decisions. It optimizes operations, enhances efficiency, and responds quickly to market dynamics. This research analyzes the impact of artificial intelligence (AI) techniques on supply chain network design. It provides insights into their application, performance, and implications. The study uses machine learning, optimization, and expert systems to evaluate their performance. The use of artificial intelligence (AI) is transforming societies by altering attitudes towards money, value judgements, and business ramifications. While artificial intelligence can address global issues such as the environment, food safety, and health care, it also poses significant risks and ethical dilemmas. AI can streamline manufacturing processes, reduce risks, mistakes, and waste by automating processes, optimizing output, and aiding in predictive maintenance, reducing replacement costs and availability. However, successful AI use necessitates precise forecasting and understanding of its repercussions. The purpose of this thesis is offering insights into how AI might improve SCM operations through the use of qualitative and grounded theory. Real-world case studies demonstrate their practical implementation. The findings guide decision-makers in selecting appropriate AI techniques, enhancing operational efficiency and customer satisfaction. Future research directions include hybrid approaches and big data integration.
Mishra et al. (Sat,) studied this question.