Our DoE course offers a clear, hands-on introduction to planning and analyzing experiments using Minitab. The course features four case studies presented in a completely click-through manner. All examples are set in a pharmaceutical context.
DoE theory is covered at a practical level to ensure participants understand the concepts and can accurately interpret software outputs, ultimately leading to correct conclusions.
• Day 1: Focuses on the planning stage of DoE
• Day 2: Focuses on the analysis of DoE results
The course covers a broad range of classical and modern DoE designs, including full and fractional factorial, Plackett-Burman, central composite (RSM), D-optimal and mixture designs.
Background
Design of Experiments (DoE) is a powerful statistical method used in Quality by Design (QbD) to gain insights into the characteristics of processes and products. It provides a scientific basis for selecting process parameter ranges in regulatory submissions and helps identify key parameters affecting product quality, while minimizing the number of experiments and associated costs. In the planning step, a minimal number of experiments is selected to representatively cover the experimental space. In the analysis step, a multivariate empirical model is fitted to the measurements to describe the relationship between process parameters and quality attributes of interest. This model is then used to predict product quality with the desired level of certainty and to establish operational ranges that ensure compliance with specification limits. The importance of the DoE methodology in the pharmaceutical development is further emphasized by various regulatory guidelines, including the Quality ICH Guidelines (Q8, Q11, and the newer Q14) and, more recently, the European Pharmacopoeia (Supplement 11.7).
Target Group
This course is ideal for employees in development, quality control labs, process engineering, and quality assurance departments who are currently using DoE or plan to use it in the future. It is also suitable for GMP auditors, inspectors, and validation personnel involved in DoE review or other submission processes. Requirements: Basic math skills/understanding and strong intrinsic Motivation.
Date & Venue
Date Tuesday, 23 September 2025, 09.00 – 17.30 h (Registration and coffee 08.30 – 09.00 h) Wednesday, 24 September 2025, 08.30 – 17.00 h
Venue HYPERION Hotel Berlin Prager Straße 12 10779 Berlin Germany
Programme
Day 1 Welcome and Setting Up the Scene
A short introduction round
Objectives of the training
Basics of DoE Planning: Part A
Why DoE?
One-Factor-at-a Time (OFAT) vs. DoE
The DoE workflow
Experimental goals
Main, interaction and quadratic effects
First Optimization Example
Get familiar with DoE in Minitab
Plan and evaluate a factorial experiment in Minitab
Calculate main and interaction effects in Excel
Basics of DoE Planning: Part B
Randomization, blocking and replication
Power and sample size
Orthogonality
Fractional factorial designs: resolution III, IV and V
Plackett-Burman and central composite designs
Special designs: D-optimal and mixture designs
A Pharmaceutical Optimization Problem: Planning
Plan a full-factorial design in Minitab
Assess signal to noise, correlation and effect-sizes
Employ Minitab’s DoE planning tool
Exercise: Hands-On DoE Planning
Plan a DoE on your own
Case Study 1: Optimization of a granulation process
Case Study 2: HPLC robustness assessment
Day 2 Statistical Modeling and Prediction
Effect selection
Analysis of variance (ANOVA)
Model building
F-test, t-test, p-value
Confidence, prediction and tolerance intervals
Data and model visualizations with Minitab
Interpreting Model Diagnostics
Goodness of fit and prediction
Curvature
Predicted values and residuals
Transformations (Box-Cox)
A Pharmaceutical Optimization Problem: Analysis
Effect selection for model building
Interpretation of model diagnostics
Derivation and Assessment of Sweet Spot and Proven Acceptable Ranges (PARs)
Supplementary Material: Design Augmentation
Questions with answers (elaborated sample solutions)
Simultaneous optimization of 3 responses with 2 factors
Design augmentation to incorporate quadratic effects
Circumscribed central composite designs in Minitab
Design Space and Sweet Spot derivation using overlay plots
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