🔬 The framework focuses on transforming raw data into actionable, useful information for decision-making.
🧪 Three stages of the approach include framing the information needed, assessing data hazards, and ensuring controls.
📊 The framework aims to generate trustworthy information that can be trusted by technicians and engineers.
💡 It aligns with international guidance on quality risk management and has potential for use with innovative process control technologies.
📚 The next step is exploring how this framework can drive improvements in digital skills and shape data governance policy.
Introduction:
Raw data in drug manufacturing needs to be analyzed effectively to be useful. The FoReSight framework, developed by researchers at the University of Limerick, focuses on turning data into actionable and trustworthy information. This framework emphasizes the importance of context and intended use of information in the pharmaceutical manufacturing process.
- The distinction between data and information is crucial for effective process analytics in drug manufacturing.
- The FoReSight framework emphasizes the need to frame the information needed, assess hazards around data access and transformation, and ensure appropriate controls for information use.
- The framework aims to generate trusted information at each stage of the drug manufacturing process.
- The framework can align with international guidance on quality risk management and FDA guidelines on Computer Software Assurance.
- Potential applications of the framework include automated reporting, process optimization, and support for process investigations.
Conclusion:
The FoReSight framework provides a methodology for turning raw data into actionable and trustworthy information in drug manufacturing. Its implementation can enhance decision-making, improve digital skills and maturity levels, and contribute to training the next generation of bioprocess engineers. The framework aligns with international guidelines and offers potential applications for process optimization and automation in pharmaceutical manufacturing.
