Beauty and Supply Chain

the new challenges for the cosmetics industry

Today, the cosmetics industry is one of the most dynamic in the world, with constant challenges for development, continuous innovation and ensuring high product quality at all times.

 

For this reason, manufacturers in the cosmetics industry face several challenges in their daily work: the market is fast and demanding, driven by a constant search for innovation and changing demand. And it is precisely for this last reason that working with unique formulations and ingredients to meet the needs and desires of customers in a fast-paced environment is becoming increasingly challenging.

Introducing a new product? How AI supports this complex challenge

In an ever-changing cosmetics industry characterized by constantly changing beauty trends and consumer preferences, the demand for continuous innovation requires rapid product development. For this reason, manufacturers will have to develop, launch and adapt products rapidly to be competitive in the market.

 

The cosmetics industry’s greatest challenges in this respect stem from the relatively short product life cycles and rapidly changing trends. These factors place significant demands on companies in terms of resources and timing. Therefore, manufacturers must quickly adapt their production processes to meet these changing trends, which often involve complex formulations and small batch sizes.

 

To support companies in this challenge, sedApta offers a solution that manages the phase-in phase-out of products comprehensively, starting with demand, continuing with stock management, and ending with the production planning and scheduling process.

In particular, in demand planning, the Artificial Intelligence, in addition to the traditional support to the world of marketing, sales and pricing, can help companies in the forecasting of new products, for which there would be no historical basis available to consult.

 

sedApta has developed supervised and unsupervised algorithms to produce a quantitative ranking of the most similar new products. This ranking is consulted by the user to analytically validate the choice he would have made without the support of the virtual manager and/or identify similar products he had not thought of.

 

Therefore, the solution proposed by sedApta makes it possible to identify more reliable introduction curves, manage the most correct production batches and respond quickly to what will be the market feedback as soon as sales begin.

An integrated solution between planning and transport processes: the benefits of predictive AI

Manufacturers in the cosmetics industry are faced with a double challenge: while they have to source high quality ingredients, they also have to navigate a complex regulatory landscape, especially in Europe, to which an additional layer of complexity is added due to transport management.

 

As already mentioned, cosmetics companies are asked to be responsive and having a supply chain that responds quickly. It can become a differentiator for a company’s business.

 

We are not only talking about having a demand-driven supply chain with agile systems, but also about innovative technologies that allow companies to plan production and have complete visibility, from the raw material supplier through to distribution to customers (or up to the shop, for those running a direct retail chain).

 

Therefore, integrating real-time organization and tracking of transports into the planning processes also enables full control and visibility over the flow of goods and up-to-date estimated delivery dates, both incoming for raw materials and outgoing for finished product distribution. This will be possible through innovative TMS systems offered by sedApta which, by integrating predictive AI, allow for increasingly reliable estimated arrival dates. What’s more, this will enable companies to react quickly in the event of possible delays and make efficiency gains by optimizing the number of transports, reducing costs and CO2 impact.

 

sedApta has developed supervised and unsupervised algorithms to produce a quantitative ranking of the most similar new products. This ranking is consulted by the user to analytically validate the choice he would have made without the support of the virtual manager and/or identify similar products he had not thought of.

 

Therefore, the solution proposed by sedApta makes it possible to identify more reliable introduction curves, manage the most correct production batches and respond quickly to what will be the market feedback as soon as sales begin.

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