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Brands in the fashion industry use AI to help plan and develop strategies by predicting future trends. They are also leveraging AI capabilities to implement smarter inventory management strategies to build upon the technology used to forecast demand and perform demand research.

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An example of AI forecasting fashion trends is the company Heuritech. Heuritech is a fashion trend forecasting agency that specializes in the use of AI and their own deep neural network to predict coming fashion trends up to one year in advance. Among their clients are Louis Vuitton, Wrangler, Paco Rabanne, Adidas, Dior, Jimmy Choo and Lee. According to Poncelin (2020), Heuritch utilizes an image recognition technology to analyze fashion images that come from social media, in particular from Instagram and Weibo. Heuritech begins by defining the audience by looking for social media accounts that have interest in fashion and then collect random samples to create panels of users. Images are then collected and categorized from those accounts. After that, products with key trend components are detected. Algorithms predict the behavior of a trend by tracking early signals and rising influencers then unifying all the forecasts on fashion items that have been traced. Clients utilize the finalized data to develop their communication and design strategies.

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Regarding the forecasting of demand, AI helps merchandisers and buyers predict future demand with increasing accuracy, which ultimately allows for the teams to improve their final margin and reduce waste. Therefore, the use of AI and machine learning algorithms help to propel the industry down a more sustainable path in an environment where consumers are increasingly aware of sustainability. More specifically, brands apply machine learning technologies to expedite logistics and make the supply chain more efficient. Brands use AI to manage and optimize supply chains as well as reduce shipping costs and transit time, which also eliminates last minute purchases to meet unexpected spikes in demand. For example, Stylumia, a trend forecasting company, uses Demand Sensing machine learning algorithms augmented with consumer demand signals to localize distribution. Overall, buyers and merchandisers can analyze the available data and make high-level decisions. Merchandising is there to tell a story. It's a balance of art and science.

 

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