In science and engineering, complex problems often require more than just expert judgment or simple spreadsheet analysis to solve. To ensure product quality, controlling and optimizing critical quality attributes (CQAs) – the outputs of your process – is essential. When faced with common cause variation, adjusting the controllable critical process parameters (CPPs) based on their impact on process quality outputs becomes crucial.
Process factors may include variables such as time and energy consumption, significantly influencing the total cost and sustainability of the solution. Striving for quality, cost reduction, and robustness is paramount.
Statistical modeling helps you to gain a deeper understanding of the process when confronted with multiple variables.
In this webinar, you learn how to:
- Visualize data: Utilize exploratory data analysis for initial insights.
- Build interactive models: Identify critical process parameters and assess their impact on quality attributes.
- Optimize solutions: Leverage models to find optimal combinations of controllable factors and simulate various what-if scenarios to enhance quality, reduce costs, and improve sustainability.
This webinar is designed for scientists and engineers seeking to make informed decisions through model building – no coding expertise required.
