Generate relevant information by building and interpreting experimental designs
You want to produce the maximum amount of information in the least amount of experiments?
- How to limit the number of experiments to be carried out if the analysis is very long and costly?
- Which samples to measure for building a robust calibration model?
- How to determine the experiments to be carried out to obtain relevant information?
- How to formulate a product as quickly as possible using a mixing design?
- How can I identify the influence parameters of my process?
- How can I optimize my process to limit the cost of production and increase the quality of the product?
- I cannot vary some of my parameters, how can I manage my experiences?
- Some factors in my study are categories, how can I include them in an experimental design?
You wish to :
Designs of Experiments adapted to your problematics
your experimental designs in order to better understand your processes
your analyzes and industiral processes
Design of Experiments (DoE) are adapted to a large number of fields of study, whether for research work or at an industrial scale.
However, implementing these methods requires following specific mathematical and statistical methodologies for which Ondalys can help you.
Implementation of designs of experiments (DoE)
Any scientific process, from the simplest chemical analysis to the industrial process of large-scale production, is made up of experiments. While it is relatively “simple” to generate experiences, it can be more complicated to interpret them efficiently in order to understand and optimize the processes.
The implementation of DoE will make it possible to select the experiments to be carried out in the most rationalized way possible. Thus, with a minimum number of trials, it is possible to extract a maximum of information and knowledge.
The Ondalys team can help you implement your experimental designs! We are available to train you, support you, or carry out the design of your experiences and the processing of associated data
DoE can be used in many applications:
- Screening designs: they allow to identify influent factors on studied product or process
- Factorial designs: these designs are very widespread and allow to study the effect of continuous or discrete variables and their interactions on a product or a process
- Optimization designs: once the influencing factors have been identified, these designs allow you to choose the best possible modalities to optimize your process
- Mixing designs: they allow a product formulation to be developed very quickly
Our expertise at the service of data analysis
With over 15 years of experience in data analysis and design of experiments, the experts of our teams support you at every stage of your projects.
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