Digital transformation can be tricky for any enterprise, but those in the life-sciences space face particularly high hurdles due to strict regulations and elongated times to market. During this Automation Fair panel discussion, hosted by Gagan Naeger, global vice president of enterprise accounts, life sciences for Rockwell Automation, a team of experts pondered the future of digitalization in the life-sciences field and discussed the best paths to get there.
What is encouraging, noted Naeger, is that life-sciences manufacturers have shown a unique ability to adapt to new ways of working. Front-runners—those properly aligning people with digital processes and technologies—are recognizing that data is a byproduct of automation programs and can be turned into insights that unleash additional levels of productivity.
“Yes. There are many standards to follow. There are a lot of tools at play here. But relying on a common information model will help,” said panelist David Hinkler, director at Thermo Fisher Scientific. “The key is to leverage as much external knowledge as you can from all of your partners and solution providers.”
Here’s a roadmap, prepare for potholes
The panel agreed that digital transformation is more a journey than a destination, particularly in the turbulent world of life sciences. And on that journey’s road, warned panelist Sachin Misra, global pharmaceuticals and biotech industry lead and co-lead for life sciences practice at Kalypso, a Rockwell Automation Company, travelers should expect potholes. “Yes, we must work toward a plan. We must have a vision in place. But we must be prepared to adjust along the way. Sticking too rigidly to a plan can get in the way of progress.”
What, exactly, are those potholes? prompted Naeger. IT/OT convergence, of course. Impatience for digital programs to mature to the point of delivering results. Failure to align digitalization campaigns to larger business goals. Partnership relationships that don’t evolve throughout the transformation. Overly complex digital strategies.
In a risk-averse industry, the client must be presented risk-mitigating plans to move forward, summed Misra. And it doesn’t hurt if the plan prioritizes simplifying processes. Naeger referenced recent digitalization programs at Pfizer that reduced drug-manufacturing processes down to a fifth of what they were previously.
Perspectives from OEMs
Joining the panel remotely was Kevin Seaver, Cytiva executive GM of bioprocess automation & digital and the executive GM of bioprocess program management, who provided the OEM’s perspective. The most prominent concern among that camp, he noted, is cyber-attacks that halt production in the life-sciences space, costing millions or billions of dollars to remedy. He stressed that a cyber-secure mindset should inform all these discussions, as should a consideration of the cost and time required to bring a medication to market.
“Clinical tests can take 10 years,” he noted. “And then if they are going into production, a company must buy land and build facilities. From the OEM perspective, we want to reduce the risks at those stages. Our assets should be ready to go out of the box—like a microwave is—so the customer can wait as long as they can in the process before buying it. That lowers their risk.”
Risk, speed & results
Risk was central to this discussion. As was speed and the digital tools and techniques that can quicken responsiveness among life-sciences enterprises. Virtual factory-assessment tests (FAT) prompted a lively discussion—enabling customers to tour and test out facilities and production lines without actually being inside them. “This enables an OEM to prove the equipment does what we say it does,” explained Seaver.
Sachin explained how on the discrete-manufacturing side of life sciences (making, say, surgical gloves), a PLM, or product life cycle management system, might be central to speed production. While on the process-manufacturing side of life sciences (think pharma) longer timelines can be made more efficient with, say, natural language processing or artificial intelligence to hasten the processing of records and discern trends that might have previously remained hidden.
The panel agreed that the vast majority of industrial data in life-sciences facilities is wasted; Seaver estimated that just 10% is used. They concurred that manufacturing-execution systems currently at play must evolve to remedy this. They discussed moving some reporting from the edge and adopting a hybrid approach to take advantage of data-rich environments but also bring that information to the cloud for richer analytics.
Intertwined with all of the topics discussed during this life-sciences panel—just as we see with most discussions about digital transformation, regardless of the vertical in which it is undertaken—is the human component to the success of projects; the decision-makers and workforces charged with implementing these digital approaches to makes assets and processes run better.
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Hinkler championed employees in life-sciences enterprises who have evolved into digital-project managers as their campaigns have matured. Sachin stressed how critical it is to consider the people who must turn newly unearthed data into insights that optimize larger efforts.
“It’s the operator, the connected worker, the people who will make this happen,” said Naeger.
“And it’s all about increasing their capabilities,” added Seaver. “These younger workforces are so much smarter than we were. Automation has allowed people to get to the next level faster. And as we drive up that ladder and get plants up to even higher levels, that allows us to meet bigger and better challenges.”
