Project 1: Faster Results

For the data scientists, it’s all about the perfect formulation. And because they aren’t poets formulating verse, for them this means finding a formulation for a product that will ultimately delight the customer. In this context, the customer can be from MPP, PLA, LAB, or another BU. “We’re the right ones to get in touch with if you’re looking to improve the features of a product,” says Lynn Ferres from the Data Science & AI team, or DSAI for short, which is part of the IT Group Function.

The DSAI team has developed an approach that makes it possible to generate promising suggestions for optimized product variants faster using machine learning support. They have successfully applied this approach for various business units. For example, the PLA BU had already developed an excellent flame retardant. Now they wanted to test this with different ingredients in different quantities in order to find an optimized version of a plastic. With the help of an AI model, they analyzed the complex multivariable inputs and outputs. This involved a continuous, iterative process, known as sequential learning, which supplied various formulations. “Our flame retardant experts He Qingliang and Christopher Simpson then decided which of these suggestions should be tested further,” explains Ferres. Thanks to their expertise, they were able to easily evaluate the AI suggestions. “This resulted in two candidates that exhibited excellent characteristics in all of the target variables,” says Ferres. The “chosen ones” then got to leave the microcosm of the laboratory and were produced on the scale of a few kilograms. The material is now being tested for its tensile and bending strength and elongation, as well as its flame retardant properties. But even if the candidates perform extremely well in the tests, the customer may still reject them.  “When they are too good, they are often also too expensive. Our job is to match the product with the customer’s wishes and applications,” Ferres explains matter-of-factly. After all, the company wants to generate revenue with its innovations – that is the primary goal. 

Hanna Kahlfeld, also a data scientist on the DSAI team, recently completed a project with the MPP BU. “Colleagues come to us when they can’t make any further progress in the lab. The cases are always tricky,” she says. With the help of the sequential learning approach, however, they quickly found a formulation that fulfilled the specified requirements. “This was further confirmation for us that our approach works,” she says. Others were similarly convinced, and so AI-supported formulation development was made a fixed part of Innovation Excellence. This is because it has ultimately shown that with this approach, LANXESS is capable of developing new and improved products at a faster rate. Kahlfeld now invites everyone to contact her and her team if they are facing similar challenges.