How To Unlock Manufacturing Data With AI
By Matt Lowe, chief strategy officer for MasterControl.
getty
In life sciences manufacturing, some of the most valuable data remains trapped in spreadsheets, scattered across different systems or simply uncollected. This fragmentation creates blind spots around how equipment performs, what drives maintenance costs and where inefficiencies lurk in production processes.
However, a new approach is emerging that can then be enhanced with new advances in AI: the convergence of asset management, batch-level manufacturing and quality system data.
Asset data offers a unique opportunity in manufacturing because it's the only place where organizations can access equipment performance data down to the serial number level. This granular insight reveals not just how well equipment is performing, but also its operational costs and maintenance challenges. Surprisingly, even equipment suppliers themselves often lack this detailed performance data about their own machines in actual production environments.
The current manufacturing landscape typically features disconnected systems. Asset Management Systems (AMS), Manufacturing Execution Systems (MES) and Quality Management Systems (QMS) operate in isolation, while critical equipment performance data often resides in basic spreadsheets that make trend analysis difficult. This separation makes it challenging to understand the true cost and efficiency of producing drugs, devices or therapeutics.
The integration of these data streams enables manufacturers to optimize their operations in ways that were previously impossible. By combining asset management data with batch-level manufacturing information and quality system metrics, organizations can make more informed decisions about their production processes. For instance, manufacturers can track how specific pieces of equipment perform across different manufacturing sites, understand their impact on product quality and optimize maintenance schedules.
Consider a piece of manufacturing equipment that must perform within specific parameters, such as a bioreactor. Traditional maintenance might occur every three months regardless of necessity. However, with integrated data analysis, if the analytics shows no performance drift over three years, the system might recommend extending the maintenance interval to eight months. If performance remains stable, the interval could be further adjusted, optimizing both maintenance costs and equipment uptime while ensuring quality standards are maintained.
Machine learning and AI applications play a crucial part in this optimization. When combined with IoT sensors, these systems can revolutionize traditional manufacturing approaches. Instead of relying on periodic analysis, manufacturers can implement real-time reviews of processes, usage and performance patterns.
For example, customer complaints may be stored in a QMS, but when integrated with other systems, AI can significantly speed up the identification of the core issue. By linking this data with asset management and MES, the company can detect which specific piece of equipment or which of all the batches is the culprit behind the issue and suggest proactive resolutions to problems faster.
The business case for data integration and AI adoption extends beyond operational efficiency. When manufacturers can see the complete picture of their production process—from equipment performance to quality outcomes—AI can suggest better decisions about equipment purchases, maintenance scheduling and process optimization. This integrated approach minimizes costs while enhancing product quality, ensuring safety and maximizing production uptime.
For life sciences manufacturers, the convergence of manufacturing data isn't just about efficiency—it's about creating a more intelligent, responsive and cost-effective production environment. As the industry continues to evolve, those who can effectively integrate and analyze their manufacturing data will be better positioned to optimize their operations and reduce production costs while maintaining the high-quality standards required in life sciences manufacturing.
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