ABSTRACT
This study aims to explore the integration of Artificial Intelligence (AI) into pharmaceutical companies' supply chain strategies and its impact on key performance indicators (KPIs) such as cost efficiency, inventory turnover, delivery fulfilment, and supply chain robustness. The principal objective is to assess how AI adoption enhances operational performance while addressing challenges in implementation. A mixed-methods approach was employed, combining quantitative data gathered from surveys with qualitative insights to analyze AI’s role in optimizing supply chains. Statistical tools were used to evaluate the relationship between AI integration and various performance metrics. The results indicate that AI adoption significantly improves cost efficiency, inventory management, and on-time delivery, demonstrating a positive correlation between AI use and enhanced supply chain performance. However, challenges related to data quality, governance, and ethical considerations persist, particularly in delivery fulfilment and supply chain resilience. It concludes that while AI offers substantial benefits in streamlining supply chain operations, pharmaceutical companies must address these challenges to fully leverage AI’s potential. Stronger integration into organizational culture, leadership support, and enhanced data governance practices are recommended to optimize AI-driven improvements in supply chain performance, ultimately contributing to a more efficient and resilient pharmaceutical sector.
Keywords: Supply Chain Strategies, Artificial Intelligence, Key Performance Indicators, Market Dynamics, Technological Advancement, Environmental Sustainability
INTRODUCTION
The pharmaceutical industry, as a critical pillar of global healthcare, faces immense pressure to ensure the availability, affordability, and accessibility of life-saving medications. However, the industry is increasingly challenged by the complexities of its global supply chains, characterized by stringent regulatory requirements, fluctuating demand, and product perishability. Traditional supply chain management strategies often struggle to cope with these complexities, resulting in inefficiencies that directly impact product availability, cost management, and overall operational performance.
As the pharmaceutical industry grows in both scope and in scope and scale, adapting supply chain strategies becomes critical in meeting shifting market demands. However, many pharmaceutical companies find their conventional approaches outdated and insufficient for addressing modern challenges. This problem is further compounded by the pressure to adopt advanced technologies such as artificial intelligence (AI), which promises enhanced operational efficiency and risk mitigation. Despite the potential benefits of AI integration, pharmaceutical companies face significant hurdles, including organizational readiness, data governance, and the need for compliance with stringent industry regulations.
This research addresses the pressing need to optimize supply chain strategies in the pharmaceutical sector by integrating AI technologies to enhance key performance indicators (KPIs). The study explores how AI can transform traditional supply chain practices by improving demand forecasting, inventory management, and regulatory compliance. By doing so, it seeks to offer actionable insights into overcoming the barriers of AI adoption, contributing to sustainable growth and competitiveness in the pharmaceutical sector.
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