Emerging Applications of Generative AI and Deep Neural Networks in Modern Pharmaceutical Supply Chains: A Focus on Automated Insights and Decision-Making

Abstract

This essay investigates the nascent and innovative applications of generative AI and deep neural networks within modern pharmaceutical supply chains, with a primary focus on the potential of AI-based solutions to automate the generation of actionable insights and improve decision-making processes in pharmaceutical logistics. The objective is to illustrate the transformative potential of AI-based technologies in the context of pharmaceutical logistics, as they have the potential to enhance efficiency and effectiveness. A state-of-the-art review of the most recent AI-based approaches in pharmaceutical logistics is presented, including automatic insight generation and accurate decision-making mechanism, before discussing various challenges and offering suggestions for future research. Broad overviews are coupled with the focus on real-world examples and detailed use case analyses within the context of modern pharmaceutical supply chains.
It is shown that enormous advances have been made in machine learning-based applications within vast data-rich logistics industries. These advances range from supervised learning, unsupervised learning, and reinforcement learning, to the most recent generative modeling of vascular architectures and locomotion models. Nonetheless, it is contended that the deep learning community is only on the brink of hypothetical exploration of actions that could entirely replace or heavily support human thought processes. Rather, the goal of this piece is to consider the directions where deep learning has not yet been applied and envision how such applications may disrupt the broader pharmaceutical industry. Examples and discussion will be provided with an emphasis on the potential transformation of pharmaceutical supply chains, from API intermediates to the distribution and retail sale of medicines. It is suggested that, in a broader view, there lies a large potential space for successful investment into AI-related solutions in the pharmaceutical industry and its interconnected transportation and retail segments.