Why design of experiments is a game changer for scientists and engineers
Design of experiments (DOE) is an essential tool for scientists and engineers developing data-driven solutions. By carefully planning and analyzing experiments, DOE helps professionals find key insights in complex data and understand how different factors affect their processes. This approach boosts efficiency, cuts costs, and improves decision making, enabling teams to refine their work, pinpoint important variables, and drive innovation across various fields.
“Statistical design of experiments is an essential tool that is often underused by scientists and engineers,” says Douglas C. Montgomery, a noted DOE expert. “It allows us to conduct efficient experiments that maximize the information gained while minimizing the resources spent.”
Phil Kay , a DOE advocate and Head of the Global Technical Enablement Team at JMP, adds, “Every scientist and engineer in the world should be empowered to use design of experiments.” This powerful endorsement reflects DOE’s potential to transform how researchers approach experimentation, bringing precision, reliability, and resource efficiency to the forefront.
Understanding design of experiments
Design of experiments is a systematic, data-driven method for planning and conducting experiments in a way that reveals the relationships between multiple inputs (factors) and outputs (responses). According to Victor Guiller , Scientific Expertise Engineer at L’Oréal, DOE represents “a complete end-to-end framework for statistical exploration, analysis, and optimization.” The approach goes beyond traditional one-factor-at-a-time (OFAT) testing by allowing researchers to study multiple variables at once, making it versatile enough to apply in science, engineering, business, and even agriculture.
With DOE, experiments are crafted to deliver maximum insights with minimal testing, enabling researchers to achieve a deeper understanding of their systems while conserving time and resources. From biology to manufacturing, DOE is proving invaluable in modern research and development.
Medium article: Design of Experiment Introduction
DOE loves biology
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The rapidly evolving field of biology offers a unique stage for DOE’s capabilities. In the new podcast “The Next Experiment,” Phil Kay (JMP) and Markus Gershater (Synthace) discuss how DOE can help biologists design better experiments to manage the unpredictable nature of life sciences. The podcast’s first episode, airing on Nov. 6, explores why adopting DOE methods could change the way biological systems are studied and understood.
One of DOE’s main advantages in biology is its multidimensional experimentation. Traditional methods often test one factor at a time, limiting the depth of insights into complex biological interactions. Multidimensional DOE, however, allows researchers to explore multiple factors simultaneously, uncovering unique interactions that are essential to understanding biological systems. The hosts argue that although biology presents numerous challenges, using these advanced experimental designs can provide more meaningful, predictive insights.
Why a ‘failed’ DOE is still a success
Not all experiments are going according to plan. “The great thing about a failed experiment is what you can learn from it,” says Jonas Rinne, Systems Engineer at JMP. A failed design of experiments can still provide useful insights, especially about noise – random variation that can hide the true impact of tested factors. When results show wide confidence intervals or no clear effect of the factors, it usually means noise is affecting the findings. Checking for noise sources, like measurement errors or environmental factors, and trying to control them or expanding the range of tested factors can help reveal real effects. Even if the first experiment doesn’t go as planned, each DOE can improve understanding of the process, guiding future experiments and achieving reliable results. You can read more about conducting DOEs and the pitfalls to avoid in Jonas’ blog series .
Design of experiments is reshaping the way scientists and engineers conduct research by providing a structured, efficient method for extracting insights from complex systems. From precise biological studies to scalable engineering processes, DOE offers a robust framework that minimizes uncertainty and maximizes information. Whether you’re exploring DOE’s applications through podcasts or learning from the hands-on guidance of experts like Jonas Rinne, every experiment brings researchers one step closer to deeper understanding and innovative solutions.
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