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Material attribution is an integral part of product life cycle management. In the apparel fashion industry, material attribution activities are error prone because of their manual and monotonic nature. As a part of intelligent process automation for material attribution, we are proposing a model that uses deep neural networks to automate the classification of apparels based on attributes such as gender, category, subcategory, and color, when an image of an apparel is passed to the model. Our model assures process improvement by accurately extracting all the attributes in one go by using a computationally efficient algorithm that also minimizes the carbon footprint.
If you are a fashion product life cycle manager and want to leverage AI/ML for automating material attribution, then this whitepaper will help you understand the art of the possible, to realize value quickly in our computationally efficient algorithm that is scalable. The whitepaper describes how PLM has undergone a paradigmatic shift, how ESG regulations add pressures on sustainability compliance, and how an efficient AI/ML algorithm can help automate material attribution to improve cost and performance efficiencies. This paper may be of interest to other stakeholders in the fashion retail supply chain like the merchandiser, designer, developer, and sourcing manager to name a few.
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