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Thanks for a great 2025 Online DOE Summit!

Stat-Ease hosts the premier annual conference on practical applications of industrial design of experiments (DOE). Check back in 2026 for information about the next DOE conference.

Missed it?

All our presenters' abstracts are listed below, along with links to their bios & slides, and the recording on our YouTube channel. Or, click the link below to go straight to the playlist on YouTube!

2025 Online DOE Summit Playlist on our channel: Statistics Made Easy by Stat-Ease

Andrew Macpherson headshot: middle-aged white male with close-cropped hair and beard

DOE: Developing Optimal Espressos

Andrew Macpherson, Prism Training & Consultancy
Watch the recording | View the slides

Making a cup of coffee is easy… isn’t it? On the face of it, yes – but you’d be surprised at how many parallels this seemingly simple process has to real-world DoE applications! When viewed through this Prism, brewing the perfect espresso transforms into a complex multi-stage operation, allowing us to explore and solve troublesome issues familiar to all experimenters.

This highly caffeinated presentation will offer practical solutions to real-world challenges faced by scientists, engineers and formulators from every industry. We will demonstrate DoE techniques essential to constructing practicable designs, optimising processes and products, and reaching otherwise impossible levels of process understanding!

Hank Anderson headshot: adult white male with short brown hair and glasses

Diving into Detail on New Stat-Ease Features

Hank Anderson, Stat-Ease
Watch the recording | View the slides

Our VP of Software Development provides a demonstration of new features for Stat-Ease software released in June 2025: perceptually uniform color maps, split-split-plot designs, and a Python script for Weibull analysis. Useful for anyone who's interested in how to make the most of their software!

Frank Westad headshot: older white male, mostly bald

Using Stat-Ease 360® for simulation and optimization of first-principle and simulation models (metamodeling)

Frank Westad, Idletechs AS & the Norwegian University of Science and Technology
Watch the recording | View the slides

DoE is, as we know, the best strategy for planning and performing experiments for process optimization and product development. After acquiring the empirical data, ANOVA is typically applied for analysis and visualization. Nevertheless, DoE has also a huge potential also as a tool for understanding and optimizing first-principle (physical, mechanistic) models and simulation systems, so-called metamodeling. Some of the objectives of metamodeling are the following:

  • Perturb the parameters in the physical/simulation model
  • Estimate the sensitivity of the parameters (including interaction and square terms)
  • Establish an empirical model between input and output
  • Find the optimal settings given constraints
  • Predict output while circumventing the physical model

Firstly, the concept of metamodeling will be described, followed by considerations regarding what kind of experimental designs that might be appropriate in various situations. Results from selected cases studies involving metamodeling will be presented.

Martin Bezener headshot: young adult white male with close-cropped brown hair

State of Stat-Ease Software: A Look Back & The Path Forward

Martin Bezener, Stat-Ease
Watch the recording | View the slides

As head of software & business development, Martin will go over the features released in the last couple of years for Design-Expert and Stat-Ease 360 software. Then, he'll preview what's in the works for the next versions of these world-class design of experiments programs.


Morten B Nielsen headshot: young adult white male with close-cropped light brown hair and glasses

The one that got away – Experiments avoided through DOE

Morten Bormann Nielsen, Danish Technological Institute
Watch the recording | View the slides

One of the central value-drivers in Design of Experiments is avoiding unnecessary work, but the process of how this is done in practice is rarely paraded in front of an audience. In this talk I will share examples where DOE was used to find the minimum (but still adequate) amount of work required for the given task and show what my decision process looks like. As a counterexample, we will also dive into a horror story where DOE was not used to plan a large experiment to see what the poor souls involved missed out on.


Niels Dekker headshot: older white male, bald

Definitive screening designs in the creation of robotic coating application for vehicle refinishes business

Niels Dekker, AkzoNobel
Watch the recording | View the slides

In the vehicle refinishes business it is crucial that the color of the repair paint matches the vehicle that is up for repair in the bodyshop. AkzoNobel has several processes for the bodyshops to ensure this is achieved independent of the car that enters the repair process. One of the aspects of this process is the ability to internally apply coatings in a fast and efficient way using robots. Before this can be done, the robot application needs to match the manual application as executed in the bodyshop. In this talk, I will discuss and show how definitive screening designs add value to this process.

Oliver Thunich headshot: young adult white male with close-cropped brown hair, smiling

ImproClean – Intelligent and resource-efficient monitoring system of process quality of disinfection and cleaning along the healthcare supply chain

Oliver Thunich, STATCON GmbH
Watch the recording | View the slides

During the government funded research project ImproClean, Statcon performed multiple DoEs with the goal of predicting hygiene parameters along the healthcare supply chain. DoE strategies used for investigating laundry and surface disinfection processes such as optimal designs and design augmentation and their implementation with the easy to use features of Statease 360 are presented. Additionally, the application of the models generated in a data streaming and prediction platform, utilizing data from innovative sensor systems provided by the project partners will be discussed.

Eric Coppens headshot: young adult white male with short reddish-brown hair

Addressing a multivariate and multilevel challenge

Erik Coppens, ONDRAF/NIRAS
Watch the recording | View the slides

In this presentation, we will explore the successful application of Design of Experiments (DoE) in optimizing the development of a conditioning process for liquid radioactive waste. By leveraging Generalized Least Squares (GLS), we systematically analyzed the effects of key production settings while accounting for batch-to-batch variability in the liquid waste and other raw materials.

This data-driven approach enabled us to identify optimal process conditions that ensure compliance with product specifications while unlocking a potential cost reduction—potentially saving multiple millions. This work demonstrates the power of DoE in addressing not only multivariate problems but also multi-level challenges involving multiple sources of variability.