Motivi: the brand that uses AI to bring out employee skills and fine-tune market supply
Motivi is an Italian womenswear brand that periodically updates its offerings to adapt to customer needs. Established in 1993 as the first fast fashion company in Italy, the company now produces two new collections per month and has 280 own brand stores in 13 countries. The brand decided to adopt artificial intelligence to optimize price management and warehouse assortment using an algorithm based on Google TensorFlow.
What made you opt for AI?
“Managing a fast and diversified flow of products like ours is really complicated: it takes careful and versatile planning. We needed to improve our company business results by managing the warehouse and assortments in retail outlets more effectively. These were our initial needs when we chose to opt for artificial intelligence. Our AI model, which was developed in team with Evo-Pricing, has helped us to optimize our operations when the sales are on since we can see how fast a product reacts to variations in discounts, and most importantly to improve sell-through variance among the various stores so as to optimize their supplies”.
How does the technology work?
“We developed an algorithm using Google TensorFlow which can make accurate predictive analyses for each store using historical data. The software assesses the potential of a single product for each retail outlet and decides where to allocate the various supplies. The system has evolved over time and, after an initial analysis of the quantities sold, we then moved on to a “personalized” model that could analyze individual store behavior. It should be noted that our AI model is based on algorithms but human intervention and guidance are fundamental to properly manage and interpret the information we collect”.
What were the results?
"The technology enables us to always offer customers the product they want, increase sales and reduce the differences in sales between one store and another. In terms of stocks, artificial intelligence has allowed us to reduce sell-through variance across the stores by 50%.
But this is not just about numbers: it made our people grow culturally and professionally. In our case, machines and people work together and this mix led to the growth of new professionals, improved time-work management and empowered the staff. In practice, store managers develop new skills and are more like small entrepreneurs who are able to read and interpret the data that is collected analytically, and then support the company in managing and putting forward solutions”.
Would you recommend this to other entrepreneurs?
“Yes, I would recommend our model to anyone who needs to manage companies that are working with large quantities. What we need to remember is that in order to be effective artificial intelligence must have specialized staff who can read, interpret and run daily operations based on numbers and distribution. In order to do this, you need to be able to take a cultural stride in the direction of AI, knowing that assimilating and metabolizing the procedures takes time and investments, even with employees. In the long run, what you get is a new way of working where people are empowered through data knowledge and can reach even more challenging goals, by using their time and skills in the best way”.