I’m often asked where we’ll first see the real impact of AI/ML in GMP. I don’t think I’ve hidden my skepticism on the topic in the past, but people keep asking, so here’s one of the first places I think it will really impact our field.
In-Process Monitoring
AI algorithms, coupled with advanced sensing technology, can detect and respond to minute changes in critical parameters. I can, today, easily imagine a system that not only detects abnormal temperatures but also automatically adjusts pressure and pH levels to maintain optimal conditions to a level of responsiveness not possible in today’s automation system, with continuous monitoring of every aspect of the production process in real-time. This will drive huge gains in predictive maintenance and data-driven decision making for improved product quality through early defect detection, especially in continuous manufacturing processes.
AI and machine learning algorithms will more and more empower manufacturers to analyze complex data sets, revealing hidden patterns and trends that were previously undetectable. This deep analysis will allow for more informed decision-making and process optimization, leading to significant improvements in manufacturing efficiency. Including:
- Enhancing Equipment Efficiency
- Reduce downtime
- Predict and prevent breakdowns
- Optimize maintenance schedules
- Process Parameter Optimization
- Analyze historical and real-time data to determine optimal process parameters
- Predict product quality and process efficiency
- Adapt through iterative learning
- Suggest proactive adjustments to production parameters
There is a lot of hype in this area, I personally do not see us as close as some would say, but we are seeing real implementations in this area, and I think we are on the cusp of some very interesting capabilities.
Investigations of a Dog 