OVERVIEW

Quality by Design (QbD) - Product & Process Optimization using Design of Experiments is organized by The Center for Professional Innovation & Education (CfPIE), Inc. and will be held from Oct 26 - 29, 2026.

Description:
Quality by Design (QbD) means that, starting from the very first development step, products and processes are designed in a way to ensure a high level of quality and reliability. One of the main QbD tools is statistical Design of Experiments (DoE), which enables to perform the necessary experiments in an efficient and structured way. This constitutes the most effective manner to identify the Critical Process Parameters (CPP’s) and Material Attributes (CMA’s) that, together, influence most the quality characteristics of highest concern, the so-called Critical Quality Attributes (CQA’s). Of course, the methodology can also be applied to optimize existing products or processes.

The training gives a comprehensive introduction to statistical Design of Experiments (DoE): on the one hand, the statistical background is explained, on the other hand, the methods are illustrated with examples from the pharmaceutical and chemical industry, and their application is trained with many exercises based on real-world case studies using a DoE software tool. A part of the last afternoon is reserved for discussing the participants’ own applications. The practical aspects are addressed at regular intervals throughout the whole course duration, so that the course overall proposes a balanced combination of methodological knowledge and practical aspect.

Topic areas to be discussed include, but are not limited to:

The importance of Quality by Design (QbD) as part of an efficient QA strategy
DoE vs. one-factor-at-a-time
The sequential approach of DoE: screening, modelling and optimization – which design in which context?
Definition of a practical problem as the first step of the application of DoE
Factor screening and modelling: how to identify the Critical Process Parameters (CPP’s) and Material Attributes (CMA’s) as well as their interactions
Optimization of a response variable with response surface models
Graphical visualization and interpretation of the results
DoE for formulations
Defining the Design Space
Robustness issues
Accounting for real-world challenges: complex restrictions, unsuccessful experiments

Learning Objectives:
After completing the course, the participants will have a thorough understanding of the concepts of statistical Design of Experiments (DoE). They will have gained confidence thanks to the many practical exercises, so that they can solve practical problems easily and efficiently in their daily work, when developing or optimizing products and processes in the QbD context. Among the advanced topics, the participants will in particular learn how to deal with typical complex practical situations such as the simultaneous optimization of several potentially contradictory Critical Quality Attributes (CQA’s), the optimization of formulations or the consideration of technical difficulties like an imprecise setting of the process parameters.