Building Quality Into Every Stage of Pharmaceutical Manufacturing

Production is getting faster, while the tolerance for quality problems remains as low as ever. In pharmaceutical manufacturing, teams are required to increase output, introduce new systems, and respond to changing demand without weakening the controls that protect product quality and patient safety.

Traditional quality reviews still have an essential role, including required batch review and release activities. The growing opportunity is to shorten the time between a problem occurring and the organization knowing about it.

Some of that improvement comes from experienced specialists building quality into systems as they’re designed, implemented, and validated. Increasingly, AI models can add another layer by learning from trusted operational data and identifying patterns that suggest a problem may be developing.

Neither approach reduces the need for careful engineering or established quality controls, but both can help manufacturers see important signals sooner.

Quality Control in Pharmaceutical Manufacturing Is Moving Closer to Production 

Quality has always depended on reliable processes, but faster production environments put more pressure on how quickly teams can recognize variation and understand its impact.

FDA process validation guidance takes a lifecycle view, encouraging manufacturers to maintain a state of control through ongoing process understanding and monitoring rather than viewing validation as a single event. ICH Q10 takes a similar approach, linking pharmaceutical quality systems with knowledge management, quality risk management, process monitoring, and continual improvement.

• In practice, earlier visibility depends on several connected elements:
• Systems that capture accurate production data as work happens
• Validation that establishes confidence in how those systems behave
• Traceability that makes data and decisions easier to reconstruct
• Monitoring that can identify patterns before they become larger quality events

Modern manufacturing and operations environments increasingly bring these elements together through connected production systems, analytics, automation, and quality management.

The starting point, however, remains the reliability of the underlying system and its data 

A Validated Pharmaceutical MES Creates a Trusted Foundation

One GMP-regulated manufacturer was implementing a new Manufacturing Execution System while active project timelines continued around it. The organization needed to validate a system that was critical to manufacturing operations without turning validation into a separate activity that slowed implementation.

Hands-on computer system validation support was embedded throughout the rollout. Validation activities followed the pharmaceutical MES from system design through release, while plans, protocols, and summary reports were developed and executed alongside the wider implementation.

The work also included onsite support during critical testing and implementation periods, combined with remote execution where appropriate. Validation activities remained aligned with project milestones, internal quality systems, GxP expectations, 21 CFR Part 11 requirements, and data integrity needs.

The result was a successfully implemented and validated pharmaceutical MES with stronger data integrity, traceability, and manufacturing process control from the outset.

Those outcomes matter beyond compliance. A system can only support faster operational decisions when the information coming from it is trustworthy. FDA guidance emphasizes that data integrity depends on complete, consistent, and accurate data throughout the CGMP data lifecycle. 

In that sense, verification and validation provide groundwork for continuous visibility. When systems, processes, and data have been validated properly, manufacturers have a stronger basis for identifying changes as production unfolds instead of reconstructing them later.

AI Digital Twins Can Extend Visibility into What Happens Next

Once reliable operational data is available, manufacturers can begin asking a more forward-looking question. Instead of only understanding what has happened, can the system provide an earlier indication of what may happen next?

The PRIA AI digital twin initiative was originally developed around complex environments where the cost of identifying risk too late can be high. PRIA creates a digital representation of a real-world environment and uses AI to assess how conditions are evolving so potential adverse events can be anticipated.

Research associated with PRIA has explored predictive models that use a synchronized digital twin to identify patterns that may precede adverse events.

The same principle has a clear application in production. A manufacturing digital twin can represent equipment, processes, operating conditions, and their relationships, while predictive models assess patterns across those signals.

For quality control in pharmaceutical manufacturing, the value lies in timing. Instead of waiting until a completed batch or scheduled review reveals an issue, predictive models may help teams recognize that conditions associated with a deviation are forming while production is still underway.

The model does not make the underlying manufacturing process trustworthy. It depends on trusted data and sound engineering to provide a reliable picture of what is happening.

Validated Systems Give AI Digital Twins Something Reliable to Learn From

The pharmaceutical MES example and the predictive work behind PRIA sit at different points in the same quality chain. 

Validation establishes confidence that a production system operates as intended and that its data can be relied upon. AI models can then use reliable information to identify relationships and patterns that would be difficult to see through periodic review alone.

• The connection can be understood simply:
• Expertise defines and validates the system, including its controls, data, and expected behavior.
• Production systems create the operational record, providing continuous information about what is actually happening.
• AI learns from reliable patterns, helping identify conditions that may deserve attention sooner.
• Quality teams apply judgment, determining what the signal means and what action is appropriate.

Connected life sciences and healthcare environments already bring together manufacturing, quality operations, automation, and predictive technologies. The value comes from keeping those capabilities connected to the same underlying process knowledge rather than treating AI as a separate layer of intelligence.

Earlier Detection Supports Quality as Production Scales

As manufacturers increase production speed and complexity, the gap between an event occurring and its detection becomes increasingly important. Required reviews and controls remain in place, while validated digital systems can give teams better visibility between those formal checkpoints. 

Organizations must move toward quality systems that can observe more of the production process as it happens and provide earlier warning when conditions begin moving outside expected patterns. Broader work in smart manufacturing and logistics is already connecting production equipment, analytics, predictive maintenance, and quality data around that goal. 

Our teams support both sides of that picture. Through Raland Compliance Partners and its broader validation capabilities, our teams help life sciences manufacturers establish compliant, trustworthy production systems. Through Akkodis Intelligence initiatives such as PRIA, our global research teams are also exploring how AI digital twins can use trusted data to anticipate risk earlier.

For more information about how connected expertise, validated systems, and predictive technologies can strengthen quality across your manufacturing operations, contact our team today.