A typical pharmaceutical plant generates a staggering amount of information. In fact, it’s not unusual for a single processing line or inspection system to produce multiple terabytes of data each day.
But while the pharmaceutical sector might top the list for the volume of information collected, it has lagged behind other industries in successfully mining that data to improve manufacturing processes.
There are good reasons for this, of course. Chief among them: the regulatory nature of the industry, which mandates validation and historically supported a more reactive approach to data analysis for quality verification purposes.
Moving Toward Continuous Manufacturing
For more than a decade, the FDA has encouraged the pharmaceutical industry to implement new smart technologies to improve drug quality and speed innovation.
Ultimately, the adoption of these technologies – supported by the process analytical technology (PAT) regulatory framework – could transform the industry from a batch-centric mindset to a more efficient continuous manufacturing approach. In fact, many industry leaders have implemented pilot projects, supported by FDA guidance, to help move the needle in that direction.
In the meantime, modern pharmaceutical plants continue to add more sophisticated sensors and instrumentation to their manufacturing lines, which generate increasing amounts of data. But they continue to struggle to pinpoint critical information that can have a bottom-line impact.
Cutting Through Data Clutter
With patents expiring, research costs rising and profit margins shrinking, today’s pharmaceutical companies are motivated to improve efficiency wherever possible. And although many have streamlined operations with modern MES and EBR systems, data analytics hold the key to manufacturing optimization on many levels.
Data analysis entails extracting and then modeling data to find meaningful correlations between variables that lead to insights and improvements. In a pharmaceutical plant, data analytics can be used to address many multivariable issues.
However, choosing an approach that can cut through data clutter and deliver both an immediate and long-term return on investment (ROI) can prove challenging.
What Are the Options?
To solve complex data riddles, pharmaceutical companies have historically taken one of two approaches. Some have hired data scientists. This costly and time-intensive approach involves educating the new hire on the process and challenge – and multiple rounds of implementation and testing that oftentimes result in standalone solutions.