Are data blind spots hurting your pharma manufacturing?

Imagine a busy pharma factory in the middle of a production cycle.

Bioreactors, mixing vessels and filling lines work as instructed by their PLCs and DCS. Valves open and close, temperatures and pressures stay within the limits, and the line keeps moving. Operators watch local HMIs, react to alerts and fill out documentation.

Every machine produces data like tags, cycle counts, alarms and rejects, but most of it stays inside the equipment or in OEM-specific sub-systems. If that data is needed elsewhere, someone must physically walk to the equipment and transfer it manually.   

– Over the years, variety in manufacturing equipment and systems has created a lack of consistency and context in data structures at the edge. This lack of visibility creates blind spots where pharma companies end up missing vital information in value streams, says John Downey, Technical Solutions Director at NNIT.   

Often, the information is there, but isolated in individual machines or sub-systems.

The impact of poor data visibility

Lack of shared data standards and poor connectivity prevents production data from being utilized by the systems that run production, manage quality, drive performance improvement, prevent downtime, and align supply with reality.

The effects are noticeable throughout the organization:

• Deviance investigations are slow and disrupt production, because QA must manually gather data fragments and ask technicians to export machine data or send screenshots via email.   

• Maintenance relies on planned service checks or reacts to critical events, which may cause downtime in the middle of production, putting an entire batch at risk.

• Efficiency analysts struggle to connect outcomes like yield variation and slower changeovers to specific events and process signals.  

• Process drift and equipment issues stay hidden until they are caught in end-of-batch reviews.

• Planning and product release is delayed, because production confirmations and material consumption reports arrive late and must be reconciled manually  

– Data blind spots cause increased risk and missed efficiency gains. The consequence is that costs and downtime stay higher than necessary. The issue of isolated data becomes even more relevant as AI increasingly shapes life sciences manufacturing, because AI is only as good as the data it has access to, says John Downey.

Connect the shop floor with higher-level systems

To maximize OT system integration and data availability, you must ensure that the data is mapped, standardized and contextualized. In practice, this involved tying the data to specific processes and uses.

For example, how to auto-populate batch records in MES with values like time, setpoints and critical parameters. Or which specific alarms or trends from the production layer should trigger a deviation investigation in the QMS.

– Success starts with clear business needs and trusted data. Then we can select the right tools to solve them.
Based on our experience with optimizing manufacturing for numerous life sciences customers, NNIT can help standardize and contextualize the trapped data, so it flows cleanly into higher-level systems and supports better decisions, John Downey explains.

Establishing a data flow that is connected end-to-end enables pharma companies to see trends, catch deviations earlier, and support predictive maintenance.

– When you connect shop-floor equipment with digital systems, information like batch records, OEE and process data becomes reliable, traceable, and ready for continuous improvement. This reduces the cycle time for process steps and produces audit-ready reporting, says John Downey.