The blueprint for digital manufacturing was set decades ago by International Society for Automation(ISA) standards S88 and S95, which cover batch processes and the interfaces between enterprise and control systems, respectively. These provide definitions and explain how to monitor manufacturing processes to generate data that can be used to automate control.
Like many industries, the biopharma sector embraced the standards. However, the complexity of drug production processes mean they generate a lot of data.
According to Laks Pernenkil, principal life sciences operations at Deloitte Consulting, a typical biopharma manufacturing environment has over 350 software systems and generates over one terabyte of data each day.
“As a result, information architectures ended up with a diverse set of manufacturing and quality systems that were separated by barriers of complexity and diversity that seemed to become more intractable with time,” he says, adding that advances in digital technology have helped industry to manage processes more effectively.
“With the advent of new digital tools and platforms—faster processing speeds, growth of cloud enabled infrastructure in regulated environments, a rapid uptick of other enablers like AI/ML, IoT, 5G, etc.—digitalization in biopharma is seeing a second frontier,” continues Pernenkil.
“These enablers have created a clear value proposition for bringing more predictability to the plant floor, designing higher quality into the manufacturing process, maximizing asset utilization, and optimizing human performance in manufacturing environments.”
Value judgment
This is important because demonstrating value is one of the biggest hurdles biopharmaceutical companies encounter when deciding on a digital manufacturing strategy, says Pernenkil.
“In our view the biggest challenge in digital adoption in manufacturing is far less technical than it is organizational,” he notes. “Many companies do not understand how to identify and communicate the value of digital investments in manufacturing and capture it over time.
“It amazes me how many biopharma companies have invested in the last 5–7 years in digital assets and enablers in the biopharma manufacturing, with limited line of sight to ability to scale and ability to capture clear value from those investments.”
Drug companies also need to understand that advances in digital technology are helping to change the regulatory landscape and adjust their thinking accordingly, Pernenkil says, particularly when it comes to process variability.
“The long history of the culture in biopharma manufacturing can be best summarized by a quote I used to hear from my professor in graduate school: ‘The best way to remove or reduce variability in manufacturing process is to not measure it,’” he tells GEN.
The prevailing industry view is that regulators expect all process and product variations to be explained and that failure to do so indicates a lack of understanding. But attitudes are changing.
“Global regulators continue to evolve their thinking on how to best encourage biopharma manufacturers to invest in driving additional process understanding—changing the culture to a Quality by Design mindset,” Pernenkil says. “But that change, while slowly coming, continues to challenge progress in digitalizing biopharma manufacturing.”
