Plummer, DavidRasheed, Muhammad BabarZhang, ShujunAlQuwaie, Thamer Adeeb2024-12-022024-08APAhttps://hdl.handle.net/20.500.14154/73937This study investigates the application of artificial intelligence (AI) in the supply chain management of Toyota Motor Corporation. Through a review of existing literature and interviews with Toyota employees, the research highlights how AI technologies enhance critical functions such as logistics, demand forecasting, inventory management, and procurement. Key benefits identified include improved decision-making accuracy, enhanced operational efficiency, and significant cost reductions, driven by AI-powered predictive analytics and automation. However, the study also outlines challenges such as poor data quality, high initial costs, and resistance to change among staff. To address these barriers, the research recommends strategies like ongoing employee training, establishing robust data governance frameworks, and phased AI implementation. Emphasizing the human element, it underscores the importance of open communication and worker involvement to facilitate a smoother transition. The findings point to the need for strong leadership and collaboration across departments for successful AI integration. The study concludes by advocating for future research on cross-industry and cross-regional comparisons, longitudinal impact assessments, and the exploration of ethical and social implications. This analysis provides valuable insights and practical recommendations for organizations adopting AI in supply chain managementThis study examines AI deployment in Toyota Motor Corporation's supply chain management. By analysing the literature and interviewing key workers from Toyota, the research illustrates how AI technologies enhance logistics, demand forecasting, inventory management, and procurement. AI-driven predictive analytics and automation improved decision-making accuracy, operational efficiency, and cost savings. The research notes low data quality, expensive initial costs, and staff unwillingness to change as important impediments. The research suggests continual training, robust data management rules, and gradual AI deployment to solve these issues. The research also emphasises the importance of human factors in AI integration, including open communication and worker engagement for smooth adjustments. The research found that high management and departmental collaboration are needed to use AI technology successfully. Future research should include cross-sector and cross-regional comparisons, longitudinal studies to track impacts, and more work on social and ethical concerns. This research analyses Toyota's AI integration to provide supply chain AI users information and advice.101enArtificial IntelligenceSupply Chain ManagementToyota Motor CorporationOperational EfficiencyData ManagementThesis