Statistical design of experiments (DoE) for process and product optimization
Targeted planning of experiments for more efficient data acquisition
Illustration: Statistical design of experiments
For the development and optimization of (industrial) products or processes, experiments are often necessary. Data obtained through trial and error or observation of running processes is not optimally suited for applying statistical analyses and models. This is where the methods of statistical Design of Experiments (DoE) can help.
What are the benefits of DoE?
DoE provides a better understanding of the problem through a structured approach. In the case of complex issues and processes, analysing and describing correlations is much easier. Important effects and interactions can be identified and quantified. Thanks to transparent test scenarios, the data obtained provides an objective basis for decision-making and meaningful interpretable results. DoE leads to the best possible value for money, the available resources are used optimally and the technological risks are minimized.
Our offer:
- Problem structuring and process analysis to determine possible influencing factors
- Creation of statistical test plans for screening, modelling and optimization
- Statistical analysis of the data collected from the experiments
- Development of a data-based predictive model for simulation and optimization of processes
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