Workshop: Design of Experiments
Ghent, Belgium
Developing products and optimizing processes often involve countless variables - far more than traditional trial-and-error experimentation allows. This approach is slow, costly, and rarely guarantees the best outcome. Design of Experiments (DoE) offers a structured, statistical method to extract maximum insight from minimal experiments. DoE enables to cut on trial costs and more efficient use of test material and equipment. DoE helps to design the right experiments, collect high-quality data, and make informed decisions faster.
Goal
In this workshop, you will discover the practical value of DoE without being overwhelmed with complex mathematics. You will learn why varying multiple factors simultaneously is more efficient than testing one-by-one and how to implement DoE easily without complex software. By the end, you will have a clear, actionable strategy to apply DoE in your own work.
Target Audience
The target audience for this workshop includes professionals who need to plan, conduct, and analyse experiments to optimize processes, improve product quality, etc., such as:
- R&D Scientists and Engineers – accelerate innovation and product/process development.
- Process Engineers & Manufacturing Engineers - optimize production processes, reduce variability, and improve efficiency.
- Quality Assurance & Quality Control Specialists - drive root cause analysis and continuous improvement.
- Data Analysts & Statisticians - support experimental design and interpret results.
No specific background is required.
Programme
The workshop includes two interactive sessions:
- Session 1: Understand the intuition behind DoE and optimize a real-world case hands-on
- Session 2: Apply DoE using software—either on your own challenge or a provided case study
LECTURER: Bart De Ketelaere is Innovation Manager at the MeBioS division of the Department of Biosystems. He combines a Master in Bioscience Engineering (KU Leuven) and a Master in Statistics (UHasselt). His main interest is in combining novel sensor technologies and data analytical tools for product and process control. His AI-related interest relates to building and maintaining models in case of limited data, as well as to the analysis of (hyperspectral-) images and spectra. Besides, he is co-founder of two spin-off companies, both active in the field of data science.
Dates and location
Friday 22 May and 12 June 2026 from 9h00 until 12h30, including coffee break
KU Leuven, Gebroeders De Smetstraat 1, 9000 Gent