"I need you to produce more innovation per unit time." This is a real quote from an industry CEO to his engineers, and a direct statement of the business problem. We need to go to market faster. Given a fixed quantity of cells that we can build and test, we can learn more with an optimal Design of Experiments than with several smaller A/B tests. We can make discoveries faster and go to market sooner with optimal DOE.
The Challenge of Implementing Optimal DOE
There are a few challenges to implementing optimal DOE in battery R&D. Optimal DOE requires that we test several variables simultaneously. However, it isn't always obvious how to fit the cell design factors of interest into discrete control variables. It can also be cumbersome to run the statistical analysis needed to understand the results. In A/B tests, the results analysis is simpler, and so this is often the first choice for stressed engineers.
The Micantis Solution
The Micantis platform integrates optimal design of experiments, cell builds specification, test data tracking, and results analysis. The seamless integration makes optimal DOE just as simple and straightforward as A/B testing. It also allows engineers to quickly analyze progress and results while tests are underway, so no time is wasted waiting for a protocol to complete if a test outcome is already clear.
Key Benefits of Optimal DOE
Faster Innovation Cycles
By testing multiple variables simultaneously, optimal DOE enables engineers to extract maximum information from each experimental round. This accelerated learning process directly addresses the CEO's mandate to "produce more innovation per unit time."Resource Optimization
With optimal DOE, you can achieve the same level of understanding with fewer test cells compared to sequential A/B testing. This is particularly valuable in battery R&D where cell fabrication and testing resources are often constrained.Statistical Rigor
Optimal DOE provides statistically valid results that support confident decision-making in product development. The robust statistical framework ensures that conclusions are based on solid evidence rather than intuition.Real-Time Analysis
The Micantis platform's integration allows engineers to monitor experimental progress and make informed decisions about continuing, modifying, or concluding tests based on emerging results.Conclusion
Optimal Design of Experiments represents a paradigm shift from traditional A/B testing approaches. By embracing this methodology through the Micantis platform, battery engineers can accelerate innovation cycles, optimize resource utilization, and deliver products to market faster. The integration of DOE design, cell specification, data tracking, and analysis removes the traditional barriers to implementing optimal DOE, making it as accessible as simple A/B testing.
In today's competitive landscape, the ability to "produce more innovation per unit time" isn't just an advantage—it's a necessity. Optimal DOE provides the pathway to achieve this goal while maintaining scientific rigor and statistical validity.