AI and Fashion

from the design of new collections to market trends

In the past, fashion shows used to set market trends: real performances where buyers and forecasters could only note down the fashion trends that designers had chosen for the mass market for the coming year. Today, forecasts move much faster thanks to new technological innovations, which have actually changed the way fashion operates in society.

An example of new technology is artificial intelligence, which is changing incredibly fast and incisively not only the way fashion taste and consumption is tracked, but also the creative process itself, increasingly a direct emanation of fashion forecasts. Once the result of time-consuming research by trend hunters around the world, now data extractions from excel tables that can be done with the click of a button.

Machine learning and artificial intelligence algorithms, put themselves at the service of fashion styling in a very “productive” way, supporting companies in the industry to predict and create new collections based on demand forecasting analysis.

AI algorithms: the future of fashion

We should not think of AI only as Artificial Intelligence but in terms of “increased” intelligence in that it can extend human thinking and creative capacity and automate, for example, the automatic recognition of fashions, styles and worldly trends through the analysis of posts and articles. The more appropriate question then becomes not whether AI can take the place of new designers but in what aspects it can support them to be more creative through its own analysis and observation capabilities.

AI and demand forecasting

In addition to traditional support related to marketing, sales, and pricing, AI can help fashion companies to leverage data to predict new trends through Machine Learning solutions that can influence the design and development of new products based on consumer preferences.

One of the most critical aspects of demand forecasting is calculating the forecast for new products, for which there is no historical basis available to use.

This problem tends to be overcome through manual selection by the user of an existing product that he or she deems similar to the new product. In this area, sedApta has trained supervised and unsupervised algorithms to produce a quantitative ranking of the products most similar to the new one. This ranking is consulted by the user to analytically validate the selection they would have made without the support of the virtual assistant and/or identify similar products they may not have considered.

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