Machine learning for clothing manufacture as a mean to respond quicker and better to the demands of clothing brands: a Greek case study
| Autor: | Evridiki Papachristou, Nikolaos Bilalis, Antonios C. Chrysopoulos |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Decision support system Computer science 02 engineering and technology Machine learning computer.software_genre Industrial and Manufacturing Engineering 020901 industrial engineering & automation Procurement Fast fashion and AI Manufacturing Reinforcement learning Intelligent decision support system Dimension (data warehouse) Product design business.industry Mechanical Engineering Clothing Clothing manufacture Computer Science Applications Control and Systems Engineering Artificial intelligence Industrial and production engineering business computer Software |
| Zdroj: | The International Journal of Advanced Manufacturing Technology. 115:691-702 |
| ISSN: | 1433-3015 0268-3768 |
| DOI: | 10.1007/s00170-020-06157-1 |
| Popis: | Summarization: In the clothing industry, design, development and procurement teams have been affected more than any other industry and are constantly being under pressure to present more products with fewer resources in a shorter time. The diversity of garment designs created as new products is not found in any other industry and is almost independent of the size of the business. The proposed research is being applied to a Greek clothing manufacturing company with operations in two different countries and a portfolio of diverse brands and moves in two dimensions: The first dimension concerns the perfect transformation of the product design field into a field of action planning that can be supported by artificial intelligence, providing timely and valid information to the designer drawing information from a wider range of sources than today’s method. The second dimension of the research concerns the design and implementation of an intelligent and semi-autonomous decision support system for everyone involved in the sample room. This system utilizes various machine learning techniques in order to become a versatile, robust and useful “assistant”: multiple clustering and classification models are utilized for grouping and combining similar/relevant products, Computer Vision state-of-the-art algorithms are extracting meaningful attributes from images and, finally, a reinforcement learning system is used to evolve the existing models based on user’s preferences. Παρουσιάστηκε στο: The International Journal of Advanced Manufacturing Technology |
| Databáze: | OpenAIRE |
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