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- W4387442954 abstract "Purpose The study aims to identify trends, assess research status by country and SCOR standard processes, and provide machine learning-based classification methods in the research area. Methods Qualitative analysis is conducted on a total 493 literature data. And nine machine learning models to classify the SCM process are performed Results Artificial intelligence in Supply Chain research has been consistent since 1998, with a noteworthy concentration from 2017 to 2023. High-performing models (XGBoost, LightGBM, CatBoost) achieve F1 values exceeding 70% and AUC values over 80%, effectively classifying literature data into SCOR's 6 processes. Conclusion In Supply Chain research, focus is on 'Enable' for decision support and 'Plan' for predictive optimization,. This addresses global AI-driven efficiency demands for strengthening domestic supply chains. Korea's 1% contribution requires policy-driven activation. Employing ML-based classification enhances future analysis, allowing ongoing research and easy referencing for national and corporate endeavors." @default.
- W4387442954 created "2023-10-10" @default.
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- W4387442954 date "2023-09-30" @default.
- W4387442954 modified "2023-10-11" @default.
- W4387442954 title "A Study on the Application of Artificial Intelligence in Supply Chain :A Machine Approach" @default.
- W4387442954 doi "https://doi.org/10.35373/kmes.28.3.9" @default.
- W4387442954 hasPublicationYear "2023" @default.
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