1. Screening Design

Screening design is usually the first type of design to be employed when there is no historical data (or only very shallow historical data). The screening design aims to “screen” the input variables and determine which ones significantly impact the target properties and which have low or no impact. It will help you extend your dataset most efficiently (i.e., with the smallest, well-distributed, statistically optimal dataset).

Importantly, no previous experience with machine learning, data science, or statistics is required. Any chemist or scientist can input their formulating objectives and constraints and be guided most efficiently to achieve their goals.

In our alpha and beta testing, we have seen this functionality significantly shorten testing cycles by as much as 80% and streamline broader research and product development by as much as 60%. 

Please discuss how to add this to your system with your CSM or Salesperson.

2. Analyze Screening Design

After performing the screening design, the effects of each input variable are assessed -  it is statistically determined if an ingredient has a significant, moderate, or no effect on a specific performance characteristic. Based on this information, users should be able to reduce the number of input variables they want to vary, reducing the problem space and the required number of experiments for a more in-depth understanding of the underlying chemistry. 

3. Optimal Design

Optimal design is a type of design that is performed to gain a deeper understanding of a problem space. Because it requires a larger number of experiments to be performed, it is normally done if the problem space is small enough initially or if the problem space has been reduced by executing a screening design.

Importantly, no previous experience with machine learning, data science, or statistics is required. Any chemist or scientist can input their formulating objectives and constraints and be guided most efficiently to achieve their goals.

In our alpha and beta testing, we have seen this functionality significantly shorten testing cycles by as much as 80% and streamline broader research and product development by as much as 60%. 

Please discuss how to add this to your system with your CSM or Salesperson.

4. Lock/Unlock Materials in the Formulation Table

When scaling up a formulation, it is essential to lock some materials to maintain consistency, quality, and safety. Locking specific materials ensures that certain critical aspects of the formulation remain constant. In contrast, others may be adjusted to accommodate a specific target.

In practice, deciding which materials to lock and which ones to adjust depends on the scaling process's specific formulation, industry, and goals. It often involves carefully evaluating the critical factors that must remain constant to achieve the desired product quality, safety, and consistency.

5. Available Samples

The list of available samples for each material refers to the existence of physical samples or specimens of a particular substance that are accessible for various purposes, such as research, testing, analysis, or distribution. The availability of samples can be crucial in scientific research, quality control, product development, and many other fields.