Artificial Intelligence Is Being Used In The Pharmaceutical Industry
How pharmaceutical companies are utilizing artificial intelligence to upgrade performance management and proactive maintenance to avoid batch loss while lowering repair and maintenance costs.
Multivariate analytics, a type of artificial intelligence-enabled analysis, can assist in identifying and troubleshooting process and quality issues, increasing yields and reducing off-spec products.
Learn how artificial intelligence-driven software software-assisted company in eliminating the need to replace a robotic seal in its filament mill every eight combinations to prevent batch loss.
Learn how another pharmaceutical company was able to avoid unplanned downtime by receiving more than a month’s notice of an impending failure in its purified water system equipment.
Most users of advanced manufacturing automation technologies will not come into direct contact with artificial intelligence. Instead, it is used within different systems to process information at a scale, speed, level of detail, and precision that humans cannot match.
As a result, the use of artificial intelligence is beginning to have a massive effect on robotic technology used throughout the industry, most notably machine vision and analytics. And some of the most significant applications of AI are taking place in drug companies.
And, given that single batch values for some drugs can exceed three million dollars, it’s not surprising that the pharmaceutical industry is looking to optimize production with artificial intelligence.
Pharmaceutical companies are focusing on two areas of artificial intelligence applications: asset performance management and analytic tools to create manufacturing efficiencies… and planned maintenance systems to analyze failure trends and provide anomaly alerts and early warnings of incipient equipment failures
According to Richard Porter, worldwide director of pharmaceuticals at AspenTech, a provider of industrial software technologies, opportunities to reduce production costs exist throughout the product lifecycle. And information systems can uncover these opportunities, allowing businesses to make informed decisions to save money. These techniques provide pharmaceutical companies with a competitive advantage, whether they are used to identify process deterioration and its impact on quality…or to predict final product quality to decrease lab testing lag times.
Porter also mentioned that multivariable logistic analytics software can be applied to data sources in pharmacological manufacturing facilities, rather than just batches in process, to analyze and continuously monitor how differences in material characteristics, variations in processes, and process anomalies such as sensor drift and changes in environmental conditions affect the final product.
According to him, these tools can assist in identifying and troubleshooting process and quality issues, increasing yields and reducing off-spec goods.
Primary hardware such as air and rotational condensers, boilers, pumps, and water purification systems are effectively protected by ai – powered proactive maintenance systems in the pharma industry. Autoclaves, bead mills, filtration, chillers, and conveyors are types of secondary production and packaging hardware that can benefit from artificial intelligence.