Extracting Practical Information from Quality-by-Design Models
See how to:
- Understand the value of QbD
- Developmental studies lead to enhanced knowledge of a process (design space)
- Higher levels of quality, faster development and approval of new products, and reduced cost, regardless of industry
- Understand prerequisites
- Assure risk analyses with cause and effect diagrams is completed
- Solicit knowledge of first principals and prior experience to identify process inputs that could likely affect results
- Explore design space
- Reduce models to include significant factors
- Explore models dynamically using Prediction Profiler
- Use Profiler to optimize responses
- ·Create control space within design space
- Create Contour Plot grid to visualize design space
- Define control space for a pair of inputs with acceptable operation ranges
- Identify realistic control space that is well within design space
- ·Simulate using Prediction Profiler using Process Variability
- Visualize variability of actual process input measures
- Summarize behavior of process inputs using Distributions
- Include input variability with simulation to estimate likely defect rate
- ·Estimate robustness of control space
- Create simulated data for each corner of control space
- Combine into one JMP data table
- Run capability study to determine worst-case robustness of control space
Resources
QbD: Robustness of Control SpaceQbD: Robustness of Control Space
Design experiments that incorporate quality into your processesDesign experiments that incorporate quality into your processes
Quality and Process Engineering
