Design of Experiments
Gain more knowledge from experiments and make better-informed technical decisions.
Why traditional experimentation falls short
Across many industries, there is a growing need to better understand and optimize processes. Whether improving a production line, developing a new food product, or refining an electronic component, experiments are often needed to understand what works and what does not.
However, experimentation takes time, capacity, and resources. Which combinations should you test, and which should you leave out? How do you avoid missing crucial insights or drawing the wrong conclusions?
Without a structured approach, you risk unnecessary costs, delays, and limited learning. That is why it is important to design experiments in a way that maximizes the information they provide.
Fewer experiments
Gain more insight with less testing.
Faster development
Shorten development cycles through more targeted experimentation.
Reliable conclusions
Make decisions based on statistically sound insights.
Efficient experiments that deliver the right insights
The challenge is not just to conduct experiments, but to design them in a way that provides maximum insight with minimum effort.
Well-designed experiments reveal which factors actually affect performance, quality, or costs. This leads to a better understanding of the process or system you want to improve.
With the right approach, you avoid wasting time and resources on experiments that provide little new knowledge.
What decisions do we support?
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Designing experiments
Which experiments provide the most valuable insights?
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Identifying critical factors
Which variables actually affect performance or quality?
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Quantifying effects
How significant is the impact of changes to a product or process?
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Optimizing processes
Which settings deliver the best results?
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Reducing development costs
How can you avoid unnecessary experiments and waste?
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Supporting technical decisions
When is there sufficient evidence to make a decision?
Smarter experimentation with Design of Experiments
Design of Experiments (DoE) is a statistical method that helps you get the most out of a limited number of experiments. By combining variables strategically, you can identify which factors actually affect performance, quality, or costs without having to test every possible combination.
DoE helps uncover cause-and-effect relationships, quantify effects, and improve your understanding of processes. This allows you to run experiments more efficiently and make better-informed decisions in product development, process improvement, and innovation.
By carefully considering the experimental design in advance, you gain faster insight into what really matters. This reduces unnecessary testing, shortens development cycles, and increases the reliability of your conclusions.
Whether you are improving an existing process or developing a new product, Design of Experiments helps you gain more knowledge from every test and increases the likelihood of making successful decisions.
Get more value from your experiments?
Want to know how Design of Experiments can help your organization reach reliable insights faster? We’d be happy to explore the possibilities with you.
Contact Jan to discuss your needs.
