Data infrastructure excellence changing the shape of biopharma manufacturing

Every step of the biopharmaceutical process generates data – from development to manufacture to distribution. Capturing and centralizing that data can help the industry create and deliver medicines and treatments more quickly and efficiently. Here’s how.

In biopharma manufacturing, there is a fine line between being data rich and information poor. Sites are filled with disparate systems, increasing the potential for siloed data. But since data is so key, biopharma companies must find better ways to mine that data and use it to improve manufacturing processes and achieve data infrastructure excellence. Which side of the line a company lands on largely comes down to data infrastructure excellence – mastering how data is collected, stored and leveraged to drive meaningful change. Many are looking at data historian technology to deliver multiple-system and cross-site data collection, storage and analysis to drive business innovation and efficiencies.  

“Life sciences companies are harnessing data historians and positioning them at the center of their manufacturing 4.0 architecture, delivering profound benefits,” says David Fitzgerald, Associate Director of Engineering, Life Sciences Manufacturing at Cognizant. “We are seeing our clients realize data capture of shop-floor equipment and then streaming that data in real-time to an enterprise-level democratized platform.”

Data, data everywhere

Biopharma companies capture data across a variety of different fronts, including key manufacturing equipment, remote mobile assets and IoT-connected devices, sensors and gateways. Performance monitoring alone captures machine pressures and temperatures, downtime, batch data, energy usage, ambient temperature and application data. This data must be managed effectively in order to unlock business efficiencies and drive improvements.

Historically, for many biopharma manufacturers, data was collected manually and sat in small data silos which were limited by location or machine. This restricted biopharma companies to more limited or disparate analytics, with no efficient way to connect data from different batches or compare batches from different sites.

Data availability has also been an issue, affecting both line workers and the information technology experts charged with finding ways to store and analyze the data. “Data consumers would spend time collecting the data from disparate sites, cleaning it and trying to figure out ways to extract the most information possible,” Fitzgerald explains.  “This slowed down the analysis of data and therefore the speed at which solutions could be implemented and utilized.”

The ability to collect, store and analyze data across sites can change everything, creating huge opportunities to improve patient health. Establishing a solid data historian program and integrated data strategy is a foundational component for the successful deployment of medicines to market and can help overcome the issues that too much data brings.

Seeing the whole picture

For one leading biopharma client in particular, their data strategy provides a good example of what happens when you can collect data from an entire site or manufacturing line and leverage it to compare processes and outcomes across other sites. During a new product launch, there was a requirement to develop a strong, consistent data strategy across multiple sites and locations. This included standardized data integration and consumption across key sites and creating an end-to-end manufacturing 4.0 data architecture in line with this strategy, inclusive of a global historian program. Combined, these elements enabled the client to analyze manufacturing data to make better-informed decisions and increase speed to market.

Allowing multiple stakeholders, from machine operators to executives, access to shop-floor data in the historian is critically important, however at any given point a historian could be working with real-time streaming data, analyzing hundreds of thousands of data points every second. “Having standardized data structures and templates in place allows companies to increase the availability of data for various users. This allows subject matter experts (SMEs) and intelligent solutions to consistently enable stronger data analytics solutions and cross-site data analysis,” Fitzgerald explains.

Historians have the ability to take data from shop-floor systems, edge devices and execution systems, whilst having the ability to contextualize the data and provide it to different IT/OT applications. By comparing data across multiple sites, companies can uncover best practices, speed up the development, manufacturing and delivery of the life-changing vaccines to the patients who need it. The ultimate goal is to have a global historian that delivers manufacturing data availability hundred percent of the time, as well as adding data contextualization and democratization across sites.

True data visibility gains

Many biopharma companies using data historian solutions are seeing the benefits of true data visibility. “By unlocking previously unavailable shop-floor data to a wider data consumer base, a site can make better, more strategic decisions,” Ajit Yeole, Senior Director of Life Sciences Manufacturing at Cognizant says. Developing a strong and robust data strategy allows companies to optimize a manufacturing site so that it runs more efficiently, and they can get the most from their data.

Process reliability is also improved since the data historian system can be constantly looking for elements that would impact process control and deviations. A data historian could also help enhance decision-making by providing data access to process analytical technologies (PAT) and predict batch and equipment failures.

“Manufacturers are getting away from siloed data stacks on individual sites and they’re instead making data available to other sites within their network via a global historian solution and the creation of data lakes or data hubs,” Yeole says. “They’re able to better share ideas, processes and best practices that could drastically improve yield, shorten process cycle times, reduce waste and lower the cost of production. It is a vital step in creating a robust and successful data strategy for any company.”

Find out how Cognizant Data Infrastructure and Intelligence solutions can help you get more value from your data here: Life Sciences Data Infrastructure & Intelligence Solutions | Cognizant