Breaking Moravec's Paradox: Visual-Based Distribution in Smart Fashion Retail (2007.09102v1)

Abstract: In this paper, we report an industry-academia collaborative study on the distribution method of fashion products using an 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."

• The paper introduces a deep learning model, Kolon-Smart-Net, that uses a convolutional autoencoder to extract visual features for optimizing retail distribution.
• It employs a maximum dispersion framework with the MaxMean measure to ensure a diverse product mix, achieving a 13% improvement in style variety.
• The research demonstrates practical integration of AI-driven visual assessments with business optimization, paving the way for smarter supply chain strategies in fashion retail.

Breaking Moravec's Paradox: Visual-Based Distribution in Smart Fashion Retail

The focal point of the research conducted by the collaborative team from the Korea Advanced Institute of Science and Technology (KAIST) and KOLON F/C revolves around the application of artificial intelligence to address practical supply chain challenges within the fashion retail industry. Recognized as a substantial advancement, the paper explores the integration of deep learning (DL) with optimization methodologies to tackle a pressing distribution problem faced by apparel retailers, especially those adopting high-variety, low-volume production strategies.

Problem Characteristics and Methodological Approach

The paper identifies a crucial problem in fashion retail: the necessity to distribute a heterogeneous style mix among various offline stores. Due to rapid changes in fashion trends, KOLON F/C adopts a strategy that involves the introduction of numerous styles in limited quantities, posing a challenge to distribution managers who must ensure a diverse variety of styles across stores. The research specifically targets the complexity of managing and optimizing the product mix without a coherent system that effectively uses the visual characteristics of the product styles.

The researchers introduce the concept of leveraging deep learning to perform visual similarity assessment of fashion items, a task traditionally considered challenging due to its subjective nature. They devised a model named Kolon-Smart-Net (KSN), a convolutional autoencoder, which successfully extracts meaningful feature vectors from the visual data of fashion products. The paper adopts an unsupervised learning approach which avoids the issue of requiring predefined labels or categories—often a limiting factor in visually oriented domains like fashion.

Optimization and Results

Once the visual features are quantified, they are used to measure the dissimilarity or distances between styles, forming a basis for optimizing the distribution. The team employs a maximum dispersion problem framework to ensure a distributed variety. By utilizing MaxMean as the variety measure, they effectively uphold the properties of monotonicity and linearity—key aspects for ensuring that adding a style to a store's inventory will not inadvertently reduce the perceived variety.

The results, when compared to KOLON F/C's existing strategies, indicate an enhancement of approximately 13% in product style diversity. This improvement demonstrates not only a theoretical advancement but also significant practical implications, particularly in enhancing customer satisfaction and reducing unsold inventory.

Implications and Future Directions

This industry-academia paper illustrates the robust potential for DL methods to be employed beyond conventional applications, intersecting with business optimization techniques to solve real-life logistical issues in the retail sector. The novel combination of perceptual AI and logic-driven optimization promises advancements in smart retail operations, effectively aligning business goals with AI capabilities.

While the paper provides a compelling case focused on a relatively small-scale experiment, the research anticipates scaling the approach to more extensive real-world applications. Future work is expected to involve refining heuristic algorithms to handle increased complexity—more styles, stores, and associated constraints—reflecting a global retail setting.

In conclusion, this research exemplifies a significant step toward breaking traditional computational boundaries as articulated in Moravec's Paradox, setting the stage for future innovations where AI systems collaborate seamlessly with business operations to optimize and enhance supply chain efficiencies in the fashion industry.