Why Loving Your Problem Is the Key to Smarter Pharma Process Optimization

Watch Part 1 of this interview on how smart sensors, connectivity, and advanced analytics are enabling predictive, insight-driven pharmaceutical manufacturing.

In Part 2 of a two-part interview at INTERPHEX 2026, Jason Pennington and Bethany Silva of Endress+Hauser and Mel Radford of Rockwell Automation discuss the barriers to smart instrumentation adoption and how manufacturers can measure meaningful progress in process optimization.

The panel opens by framing data trust as foundational. Manufacturers need data not only for reporting but to engineer new processes and procedures, and that data must be consistent, traceable, and audit-ready to satisfy regulators. The speakers emphasize that regulators are increasingly supportive of innovation and transparency, but companies must be able to clearly explain the "why" behind their data and decisions. Bringing offline or asynchronous analytical measurements into alignment with time-synchronized process data within a unified namespace is essential to achieving that standard.

On barriers to adoption, the trio identifies several. Financial constraints prevent some facilities from fully re-instrumenting and revalidating. Cybersecurity is a growing concern, as new field devices increasingly come with built-in IP addresses, requiring deliberate network segmentation and cyber resilience strategies as IT and OT environments converge. People and organizational priorities present perhaps the most persistent challenge, as internal quality teams can be more demanding than regulators and companies can struggle to prioritize among competing initiatives. The panel's advice is to engage quality stakeholders early, not at the end of a project.

For measuring success, the speakers point to throughput gains and manufacturing footprint reduction as the clearest KPIs. A real-world digital twin deployment across an entire drug product line serves as a compelling example, with one customer now rolling out the approach across all production lines after seeing measurable yield improvements. The panel closes with a reframe: rather than chasing solutions, start by deeply understanding the problem and defining how success will be measured.