Per Vase

Managing Consultant, Novo Nordisk Engineering

Within pharmaceutical manufacturing, few priorities outrank compliance. At the heart of that scrutiny is validation and the robustness of the statistical methods used in drug development and manufacturing that are crucial for getting the green light from auditors. But for many teams, the challenge is structural, with chemists and engineers not necessarily having the advanced statistical training needed to construct dependable, validated workflows from the ground up. The result can be a gap between scientific ambition and regulatory expectation, one that auditors are quick to flag. It is in that space that consultants at Novo Nordisk Engineering (NNE) step in. By building robust, repeatable statistical frameworks, NNE helps companies turn fragile processes into systems that auditors can trust – and ultimately, approve.

Per Vase, Managing Consultant at NNE, has spent more than decade supporting statistical frameworks in the pharmaceutical industry. While reproducibility and validation are core values of a good statistical workflow, both from the perspective of the manufacturer and auditor, the problem, Vase argues, lies in the tools. In environments such as Excel, scientists and engineers can end up doing a lot of programming themselves, creating bespoke solutions that may work in practice but fall short under regulatory scrutiny. For auditors, it raises a red flag: processes that are difficult to validate are, by definition, difficult to trust.

In contrast, JMP offers a different route. Tools such as the Workflow Builder uses predeveloped, validated scripts embedded at each stage of a protocol, removing the need for manual coding. The result is not just efficiency, but transparency and something auditors can interrogate with confidence: “It’s very easy for the auditor to feel comfortable because they can see exactly what you have done,” Vase says.

But compliance is only part of the story. Increasingly, pressures also come from driving down costs. Pharmaceutical manufacturers that fail to properly optimize their processes risk poor yields or unnecessarily long run times when running them at scale. The consequence is not just technical inefficiency but financial drag. As Vase puts it, “there’s a lot of focus on cost today that wasn’t there ten years ago.” For companies looking to extract real value from their R&D investment, it often means circling back to revisit and reoptimize production steps that should have been right the first time.

In Vase’s view, though, the real gains come from acting earlier: “Ideally, we should prevent bad things from happening and do preventative actions,” he says, though concedes that such work is “much more difficult to sell” to management given the lag time between investment and visible return. Therefore, Vase says, it’s key to focus initial efforts on what he calls “burning platforms,” the areas of production with established need for an efficiency boost. “This way of working creates value,” Vase continues, “and then at the next step, you can go for more long-term preventative things.” Tools such as design of experiments (DOE) in JMP make that shift possible by enabling quick, targeted improvements that help to build a case for a more systematic, preventative approach. And crucially, help to win the confidence of those holding the budget.

Once that preventative mindset takes hold, the focus shifts again, this time to measurement. For Vase, robust measurement systems are as critical as reliable production methods. “If you cannot measure, then you cannot see where you are,” he says. Without that visibility, production can become guesswork. Teams that optimize their measurement processes before tackling production stand a far better chance of catching deviations early – rather than reacting after quality has already begun to drift, as it inevitably does in any pharmaceutical manufacturing process. The payoff is both practical and persuasive: fewer surprises, less wasted time, and a stronger case for investment.