Optimizing D-Optimal Designs in Cornerstone
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Beyond Design of Experiments: What’s Next in the Business?
Summary of the White Paper
This whitepaper presents an innovative approach to systematically collect DoE data from multiple sources in a unified database. By applying meta-analysis techniques, it becomes possible to uncover insights and knowledge that go beyond what isolated experiments can provide, helping engineers unlock the full potential of their experimental data
Key Insight
- Consolidating multiple DoE datasets in a unified database enables powerful meta-analyses to gain learnings beyond individual experiments.
- A flexible "Entity-Attribute-Value" database model allows to store and analyze heterogenous DoE data from different sources.
- Meta-analysis of DoEs opens up new possibilities including experiment planning based on aggregated learnings
