Review on the Applications of AI in Laboratory Experiments in Supply Chain Management Education

This paper comprehensively reviews the profound impact of Artificial Intelligence (AI) on laboratory sessions within supply chain management education. The primary objective is to elucidate the diverse applications of AI in educational settings and assess how AI has reshaped traditional laboratory sessions. This paper uses a systematic review methodology to analyse literature sources, identifying key trends, methodologies, and outcomes. The analysis categorises and scrutinises supply chain education laboratory sessions, including inventory management, demand forecasting, logistics optimisation, and procurement strategies. The paper delves into how AI technologies, such as machine learning algorithms, optimisation models, and simulation tools, have revolutionised these laboratory sessions. Findings reveal that AI integration has substantially improved the quality and effectiveness of laboratory experiments. AI-powered sessions empower students with enhanced problem-solving skills and a deeper understanding of real-world supply chain challenges. Moreover, AI-driven experiments create dynamic and adaptive learning environments, fostering student engagement and critical thinking. The implications extend beyond the classroom, as AI in laboratory sessions enhances supply chain management education and prepares future professionals for an AI-driven industry. This paper underscores the need for continued research and innovation in AI applications for supply chain management education and emphasises the importance of educators staying updated with evolving AI technologies.