Suffering from Data Overload? There’s a Cure

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.

Others have deployed individual point solutions from multiple vendors. These niche solutions are designed to address specific issues – such as reducing energy costs or predicting substrate moisture content – but are not designed to work well together. Typically, the result is islands of automation, which are difficult to integrate and maintain.    

There’s a Better Way: Scalable Analytics

A better approach is a unified, scalable analytics platform that can address manufacturing challenges today. And extend capabilities, performance gains – and ROI – as needs expand.

For example, spray drying is becoming more prevalent in pharmaceutical applications due to improved efficiency, reduced cost and better quality control. Spray drying can be a continuous process – and is very suitable for automation. Process modeling and analytics (from pilot through scale-up) are critical enabling technologies for any continuous manufacturing strategy. 

From an operations perspective, an analytics platform can alert operators via dashboard when the process begins to approach quality constraints so appropriate actions can be taken.

And because this solution is holistic, the same scalable platform can be used across a broad range of applications – from facilitating predictive sensor calibration to optimizing energy management. 

Simply put, scalable analytics will enable our industry to accelerate innovation in product manufacturing and quality assurance.

Co-authored by Jim Miller
Business Director, Advanced Analytics, Rockwell Automation