Integrating Artificial Intelligence With Supply Chain Operations: A Novel Methodology For Enhancing GMP And GDP Practices

Abstract

This paper provides a new approach to the utilization of AI and ML technologies inside the supply chain to enhance the GMP and GDP compliance. The paper investigates contemporary challenges facing supply chain management, especially in regulated industries such as pharmaceuticals, where compliance to GMP and GDP is a key prerequisite of product quality, safety, and regulatory reporting. Through the use of cutting-edge AI and ML, the proposed approach seeks to streamline crucial supply chain elements, including demand forecasting, inventory management, and logistics, while enabling real-time monitoring and tracking of products. Incorporating AI/ML, you can improve decision-making, better manage risks proactively and predict potential disruptions to avoid delays and drive efficiency. Moreover, it also highlights that the AI-driven predictive analytics can optimize monitoring of manufacturing and distribution to ensure that these functions remain in compliance with GMP and GDP regulations. The presented framework highlights the capability of AI/ML to transform the face of supply chain management by improving efficiency and compliance to standards, consequently leading to a more robust and adaptable supply chain system. This paper provides a detailed design of an implementation roadmap that is a generic model that can be adopted in different industrial settings to achieve the desired improvement on the supply chain performance.