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- W3000296873 abstract "Selection of supplier is the integral and most challenging part of supply chain management in fashion industry as its performance is largely dependent on effective supplier selection process. Multitude of information from the customers’ and market’s perspective has to be considered before making the decisions in regard to supplier selection. Data mining methods have found many applications in fashion supply chain management. However, decision making related to supplier selection lacks the application of data mining methods. It warrants an automated decision support system wherein machine learning models can be trained on historical customer order data to predict best suppliers or recommend new ones depending on the degree of matching between suppliers’ capabilities and the product order features. In this paper, our work revolves around the research question of how we can predict suppliers based on historical customer order data by using data mining methods. Our aim was to predict supplier by applying classification models on the historical customer order data. We applied four machine learning classification models and the research findings suggest that these models can be employed for the decision making concerning supplier prediction. This study can contribute to the development of automated decision support system which is reliable and efficient for the supplier prediction." @default.
- W3000296873 created "2020-01-23" @default.
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- W3000296873 date "2019-09-01" @default.
- W3000296873 modified "2023-09-24" @default.
- W3000296873 title "Supplier Prediction in Fashion Industry Using Data Mining Technology" @default.
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- W3000296873 doi "https://doi.org/10.1109/iesm45758.2019.8948109" @default.
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