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NNE uses JMP to replace ad hoc, hard‑to-validate statistical processes with standardized, transparent workflows that auditors can trust. By combining validated automation with DOE-driven optimization, they help pharmaceutical manufacturers improve compliance while reducing cost and inefficiency.
As regulatory expectations tighten and cost pressures rise, the ability to demonstrate not just compliance, but control, is becoming a defining advantage. NNE’s work shows that with JMP, statistical rigor is no longer a bottleneck – it’s a foundation for faster approvals, smarter processes, and more resilient pharmaceutical manufacturing.
In a new customer success video, consultants at Novo Nordisk Engineering (NNE) explain how JMP helps them build validated, transparent workflows that stand up to inspection while unlocking measurable efficiency gains.
At the center of NNE’s approach is a shift away from fragile, bespoke Excel-based solutions towards standardized, repeatable frameworks. Using JMP’s Workflow Builder and design of experiments (DOE), NNE enables teams to embed validated methods directly into everyday workflows, reducing manual coding, improving traceability, and giving auditors full visibility into how results are produced.
Key takeaways
- Compliance starts with robust statistics: Validation is not just documentation; it depends on reproducible, auditable statistical workflows that regulators can interrogate.
- Excel creates hidden risk: Bespoke coding and manual processes may work operationally, but often fail under audit due to poor traceability and validation.
- JMP enables built-in validation and transparency: Workflow Builder embeds pre-validated scripts, reducing human error and making it easy for auditors to see exactly what was done.
Returns on Investment
- Faster audit readiness: Transparent, standardized workflows reduce the time and effort needed to demonstrate compliance.
- Reduced risk of audit findings: Validated processes eliminate red flags associated with manual coding and non-reproducible analyses.
- Improved process efficiency and yield: DOE-driven optimization helps avoid suboptimal operating conditions and long run times.
- Lower operational costs: Eliminating inefficiencies and rework directly reduces manufacturing costs at scale.
- Stronger internal confidence and decision making: Reliable measurement and early detection of deviations prevent downstream quality issues.
- Accelerated shift to preventative optimization: Demonstrating quick wins builds momentum for broader, long-term process improvements.
This is excellent. Love this summary: At the center of NNE’s approach is a shift away from fragile, bespoke Excel-based solutions towards standardized, repeatable frameworks.
