The Role of AI-Driven Predictive Maintenance in Enhancing U.S. Pharmaceutical Manufacturing Operations

Abstract

Pharmaceutical manufacturing is essential for the production and sale of pharmaceutical and biomedical products required for curing diseases. In recent years, pharmaceutical manufacturing improvements have gained increasing focus due to the COVID pandemic, compelling the industry to become more agile and flexible to ensure the drug supply chain's efficiency and robustness. Advanced systems, valves, sensors, or other equipment are required in building facilities. These items are considered critical assets as they are directly involved in the manufacturing of sterile drug products, thus affecting drug quality, safety, and efficacy [1]. Additionally, if equipment is not available during the process flow, productivity and material throughput would be lost. Such interruptions heavily impact production, as data suggests a strong dependency on equipment scaling on the manufacturing process duration. Therefore, equipment condition monitoring approaches are required to ensure the health of manufacturing-critical assets and to initiate action if necessary. Suitable maintenance actions could be the difference between smooth production and severe product supply problems, with substantial financial repercussions for the companies involved. For instance, a product shortage could exceed the estimated annual revenue of $1.8B for a typical vaccine production facility. In addition, costly penalty fees for contract defaults could arise. A terrestrial approach based on whether assets are degrading or no longer suitable for use should be considered when developing maintenance concepts.