DataStories for Cosmetic Formulation Industry

Case Study Concept - DataStories for Cosmetic Formulation Industry

Creating new and improved cosmetic formulations

About the concept

Reformulating cosmetic products to improve gross margins while maintaining quality

Industry

Cosmetics - Personal Care - Skin Care

Project Duration and Effort

1 - 2 weeks

Industry Involvement

Moderate: data processing and discussing results

Technology Used
  • DataStories AI Platform for modelling
  • Python for data processing

The challenge

There are many different goals to achieve in cosmetic formulation. Whether it is to lower overall product cost, decrease time-to-market or improve the manufacturing process. These are all good reasons to reformulate current products and stay on top of the competition.

The main goal for this case is to have or keep the product, a beauty cream, stable while improving the overall composition and lowering the production cost.

Product stability is measured in days. The product is assumed to be stable if it's consistent after 50 days. As a second priority we want to maximize the homogeneity of the product which is measured by experts in the lab.

Beauty creams consist of many ingredients and some of them are quite expensive but absolutely necessary for the cream to function as expected.

This raises the following business question:

How do we define how much of these expensive ingredients we can use to have the best trade-off between quality and cost while also keeping in mind our other goals: keeping the cream stable and homogeneous?

The solution

DataStories A.I. Platform enables the industry to use their existing product data to inspect thousands of products to generate new and improved cosmetic formulations that have a favorable impact on both the stability and homogeneity of the product.

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We started out with a clean data set that contained data of many different products (or formulations). It contains the percentage or presence of certain components in the mix as well as data of our two KPIs: stability & homogeneity. We also have information about the price of these components, which allows us to factor in the cost or benefit of adding or removing certain components to any new formulations.

If it is available, other data entries can also be factored in such as temperatures and conditions under which production took place.

DataStories helps to identify the main drivers or in this case the most important components in the formulation. After they are defined we can visualize, through useful 'what-if analysis' what will happen if we change any of the variables, thus changing the formulation.

These models allow the personal care industry to determine their optimal formulation while maintaining product quality and keeping in check with compliance regulations.

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The impact we created

In a short time span, cosmetics professionals are able to obtain a competitive edge by:

  • Identifying key ingredients to extract or add to a product mixture
  • Identifying key formulas that drastically reduce overall cost, maintaining quality
  • Improving overall product quality
DataStories A.I. Platform enables the industry to use their existing product data to inspect thousands of products to generate new and improved cosmetic formulations that have a favorable impact on both the stability and homogeneity of the product.

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Our core expertise is in business-driven applications of predictive analytics and data science to solve complex business challenges which directly impact the bottom line.