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Process and Method Variability Modeling to Achieve QbD Targets.

Mark Alasandro1, Thomas A Little2

  • 1, Mission Viejo, California, 92692, USA. malasandro@aol.com.

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Summary
This summary is machine-generated.

This statistical modeling tool visualizes how process variability impacts product acceptance. It aids in setting specifications, monitoring manufacturing runs, and justifying process improvements for continuous optimization.

Keywords:
AtPmethod variabilitymodelingprocess variabilitystability

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Area of Science:

  • Statistical modeling
  • Process analytical technology
  • Quality by design

Background:

  • Manufacturing processes involve inherent variability and bias.
  • Monitoring and controlling these factors are crucial for product acceptance.
  • Existing methods may lack real-time feedback on variability impact.

Purpose of the Study:

  • To present a statistical modeling tool for real-time impact assessment of variability.
  • To enable data-driven setting and justification of product specifications.
  • To support continuous process improvement and change justification.

Main Methods:

  • Development of a statistical model for real-time variability analysis.
  • Integration of method, process, and stability data.
  • Capability to incorporate additional variability sources for enhanced prediction.

Main Results:

  • The tool provides real-time visualization of variability/bias effects on acceptance rates.
  • It facilitates the assessment of manufacturing runs for process control.
  • Aberrant results can be traced to specific sources of variability/bias.

Conclusions:

  • The statistical modeling tool enhances process understanding and control.
  • It provides a robust framework for setting and justifying specifications.
  • The tool supports informed decision-making for process optimization and continuous improvement.