Mulyata, JohnAlbandari, Alenize2026-03-092026Alenize, A. (2025). The role of artificial intelligence in optimising demand forecasting and inventory management within pharmaceutical supply chain management: A case of Saudi Arabia (Master’s thesis). Swansea University.https://hdl.handle.net/20.500.14154/78397The study examined the application of Artificial Intelligence (AI) in optimising demand forecasting and inventory management within the pharmaceutical supply chain in Saudi Arabia. The literature review indicates that AI has significant potential to improve forecasting accuracy and inventory efficiency; however, its adoption is still hindered by technical, organisational, and regulatory challenges. The existing literature highlights important gaps; however, most studies have focused on developed markets, while research on emerging economies, such as Saudi Arabia, remains limited. As the nation undergoes a remarkable digital transformation with a set of Vision 2030 initiatives, issues with AI technologies are emerging in response to deeper systemic inefficiencies in healthcare logistics. The study is based on secondary data, employing a qualitative research design that involves a thematic analysis of literature, industry reports, and related case studies. The inquiry was guided by four research questions: what leads to the adoption of AI, how much AI can affect demand forecasting, whether it can be effective in enhancing inventory management, and what are the obstacles to successful implementation. The study revealed that AI improves the precision of demand forecasting by 30% and reduces the forecasting errors by 20-50%. In inventory management, AI is expected to lead to a 20% reduction in waste and a 25-35% decrease in inventory expenses. Challenges persist, including poor data, outdated systems, organisational resistance, cultural norms, and regulatory ambiguity. It is concluded that technological and strategic preparedness is high; nevertheless, to achieve real success, it is essential to address the organisational and regulatory barriers that run deep. Some of the recommendations include investing in data infrastructure, enhancing AI literacy, developing more transparent regulatory frameworks, and promoting intersectoral cooperation.64enArtificial IntelligencePharmaceutical ScienceSupply Chain ManagementThesis