1 research outputs found
Breaking Moravec's Paradox: Visual-Based Distribution in Smart Fashion Retail
In this paper, we report an industry-academia collaborative study on the
distribution method of fashion products using an artificial intelligence (AI)
technique combined with an optimization method. To meet the current fashion
trend of short product lifetimes and an increasing variety of styles, the
company produces limited volumes of a large variety of styles. However, due to
the limited volume of each style, some styles may not be distributed to some
off-line stores. As a result, this high-variety, low-volume strategy presents
another challenge to distribution managers. We collaborated with KOLON F/C, one
of the largest fashion business units in South Korea, to develop models and an
algorithm to optimally distribute the products to the stores based on the
visual images of the products. The team developed a deep learning model that
effectively represents the styles of clothes based on their visual image.
Moreover, the team created an optimization model that effectively determines
the product mix for each store based on the image representation of clothes. In
the past, computers were only considered to be useful for conducting logical
calculations, and visual perception and cognition were considered to be
difficult computational tasks. The proposed approach is significant in that it
uses both AI (perception and cognition) and mathematical optimization (logical
calculation) to address a practical supply chain problem, which is why the
study was called "Breaking Moravec's Paradox."Comment: 10 pages, 19 figures, The fifth international workshop on fashion and
KDD, KDD 202