Life science manufacturers are trying to become more agile and use information more effectively to drive their businesses. Yet the pressures in life science manufacturing are unique. Products have a significant impact on human life, tolerances are tight, regulatory requirements are increasing, and there is a high cost of failure.
These pressures make implementing new technology more challenging than general industrial verticals. There are constraints from existing processes, tools, and IT systems that must be considered when deploying new systems, including:
These constraints create an agility paradox, as processes need to be closely controlled, while analytics requires a real-time, iterative approach. These constraints can also lead to rising and unpredictable cloud ingestion and processing costs and inefficient use of human resources. As such, many life science manufacturers are still struggling to scale their Pharma 4.0 use cases beyond pilot.
Yet there are life science manufacturers who are seeing success and leading the way. A panel discussion hosted by ABI Research—featuring digitalization leaders from Alcon and Catalent—provides some very useful guidance. Here’s what they had to say.
Like many manufacturers, Alcon was struggling to scale their Industry 4.0 use cases beyond pilot. Across 17 manufacturing lines, they had thousands of pneumatic cylinders and motors, all sending vast quantities of data directly to a cloud data lake. They initially planned to manually standardize this data in the cloud, but quickly realized the personnel requirements to do so would be prohibitive.
To scale these use cases across the enterprise, they would need:
In their search to address these needs, Alcon discovered HighByte Intelligence Hub. Using the Intelligence Hub, they were able to templatize their 17 manufacturing lines, handling months of manual standardization work in a fraction of the time.
This approach also allowed them to first process their data on-premises to reduce cloud ingestion costs and contextualize the data closer to the data source and domain expert. With their newly standardized data, Alcon was able to launch a predictive maintenance program that they could easily adapt to work on additional sites in weeks rather than years.
Proving ROI to decision-makers has not been difficult for the Alcon team. To justify these investments, John Patanian, Data Analytics Manager at Alcon, provided the following advice:
This approach has enabled Alcon to better develop future funding requests for new use cases aimed at increasing efficiency and lowering costs.
Catalent faced a multifaceted challenge with their bioreactor data. They wanted to model data from their bioreactors to make it accessible in their Unified Namespace (UNS), but the data lacked the context and format necessary to be usable by data consumers. To give their data context, they relied on a manual process in which data scientists spent time labeling data. There were two key problems with this approach:
While looking for ways to automate data contextualization, Catalent decided to implement HighByte Intelligence Hub. Catalent leveraged the Intelligence Hub to build a replicable model that added the context needed to make their data fit the ISA-95 format of the UNS, freeing their data scientists from almost all manual labeling.
In one case, Catalent used HighByte Intelligence hub to model data from a set of 48 bioreactors, each generating 100 tags. Manual entry routinely took hours to accomplish, but with the Intelligence Hub, Catalent built a data model that they could replicate for each bioreactor. As a result, they were able to model 24 of the 48 reactors in less than 1 hour, ensuring that data could be ingested by the UNS without manual effort. The model can now be altered and applied across the enterprise for any similar bioreactors.
Increased productivity and value generation can be difficult to quantify, but Chris Demers, Global Lead for Plant Data and Analytics at Catalent, clearly sees the value. Chris advises that manufacturers measure ROI in digital infrastructure investments by calculating the time savings achieved by eliminating manual data entry. This time savings frees up highly skilled personnel to focus on critical thinking and problem-solving and ultimately do more with less.
Alcon and Catalent have both implemented HighByte Intelligence Hub as a core element of their architecture. While they have taken different approaches, both companies have both improved data access, built a resilient data architecture, and ensured scalability.
Based on learnings from these companies and other life science manufacturers, I recommend the following:
To learn more about HighByte or Alcon and Catalent’s approaches to building a scalable data architecture, watch the panel discussion or download this ABI Insight report from ABI Research.