Predictive AI Models for Manufacturing Failure Detection in Multi-Site Pharmaceutical Facilities

The role of Predictive Artificial Intelligence (AI) models is continuously emerging as critical in the pharmaceutical manufacturing industry particularly in failure detection in multiple sites. These models use artificial intelligence, evolving ML techniques, and large datasets to forecast, monitor and resolve rigorous system failures. This paper aims to analyze how research has embraced the development of effective predictive AI frameworks that are relevant to multi-site pharmaceutical facilities, given the many limitations and challenges associated with such settings. A deeper elucidation of different forms of ML, such as supervised and unsupervised learning, is provided. There is special emphasis on the usage of fleet and vehicle domain knowledge and regulatory compliance knowledge and, in general, field operational data feeds to the prediction model into the system. Our approach is fully based on the mixed physics and data approach, which provides high accuracy of results and well interpretable quantitatively. This study supports the generalized understanding that predictive AI models are capable of reducing downtime, increasing product quality, and optimizing operations. Real-world experience in a multi-site pharmaceutical firm establishes more than 95% efficacy of failure prediction along with significant cost-effectiveness and time compression for products. Finally, the paper points to the implications for Industry 4.0 in the context of the pharmaceutical sector and presents additional research avenues. Keywords: Predictive AI, Machine Learning, Pharmaceutical Manufacturing, Failure Detection.