<![CDATA[Delays in importing Hot Rolled Coil (HRC) raw material can disrupt production continuity and generate operational losses for manufacturing companies that depend on imported supply. This study aims to develop a risk classification model for HRC raw material import delays based on Supply Chain Visibility indicators, using a risk scoring approach combined with machine learning at PT Berjaya Mandiri Indonesia. A quantitative descriptive approach was applied to 200 historical HRC import records from 2020 to 2025. Six risk indicators were weighted through a risk scoring method to produce Low, Medium, and High-Risk labels, which subsequently served as the learning target for three classification algorithms Naïve Bayes, Decision Tree, and Random Forest evaluated using stratified 5-fold cross-validation. Decision Tree and Random Forest achieved the highest average accuracy at 97.0%, with Decision Tree marginally outperforming Random Forest on macro-F1 (0.965 versus 0.962) and consequently selected as the final model on grounds of computational efficiency and interpretability. A confirmation interview with four company practitioners indicated that the model possesses good face validity. The integration of risk scoring and machine learning produced a risk classification model that consistently automates the manual risk-scoring rules; however, the high accuracy obtained reflects consistency in replicating a deterministic label rather than predictive capability over an independent outcome, so further predictive validation is still required before the model is applied operationally.]]>
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