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A Machine Learning-Based Risk Classification Model for Hot Rolled Coil (HRC) Import Delays Using Supply Chain Visibility Indicators

  • Amadea Syahbani Rachela Putri1*
  • Wiwik Handayani2
1,2 Universitas Pembangunan Nasional “Veteran”
* Corresponding Author: Amadea Syahbani Rachela Putri

Abstract

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.

Keywords

Supply Chain Visibility Risk Scoring Machine Learning Import Delay Supply Chain Risk Management

How to Cite

Citation Format
Rachela Putri, A. S., & Handayani, W. (2026). A Machine Learning-Based Risk Classification Model for Hot Rolled Coil (HRC) Import Delays Using Supply Chain Visibility Indicators. International Journal of Management Science and Information Technology, 6(2), 1415-1423. https://doi.org/10.35870/ijmsit.v6i2.7849

References

22 References
  • Alfaqiyah, E., Alzubi, A., Aljuhmani, H. Y., & Öz, T. (2025). How Industry 4.0 Technologies Enhance Supply Chain Resilience: The Interplay of Agility, Adaptability, And Customer Integration in Manufacturing Firms. Sustainability (Switzerland), 17(17). Https://Doi.Org/10.3390/Su17177922
  • Alvarenga, M. Z., Paulo, M., & De Oliveira, V. (2023). The Impact of Using Digital Technologies on Supply Chain Resilience and Robustness: The Role of Memory Under the Covid-19 Outbreak. (28(5)), 825–842. Https://Doi.Org/10.1108/SCM-06-2022
  • Creswell, J. W., & Creswell, J. D. (2018). Research Design: Qualitative, Quantitative, And Mixed Methods Approaches (5th Ed.). SAGE Publications.
  • Culot, G., Podrecca, M., & Nassimbeni, G. (2024). Artificial Intelligence in Supply Chain Management: A Systematic Literature Review of Empirical Studies and Research Directions. In Computers in Industry (Vol. 162). Elsevier B.V. Https://Doi.Org/10.1016/J.Compind.2024.104132
  • Dubey, R., Bryde, D. J., Dwivedi, Y. K., Graham, G., Foropon, C., & Papadopoulos, T. (2023). Dynamic Digital Capabilities and Supply Chain Resilience: The Role of Government Effectiveness. International Journal of Production Economics, 258. Https://Doi.Org/10.1016/J.Ijpe.2023.108790
  • Guo, X., Chen, Y., Xie, J., Wang, H., & Lei, X. (2025). Research On Supply Chain Resilience Mechanism Of AI-Enabled Manufacturing Enterprises -Based on Organizational Change Perspective. Scientific Reports, 15(1). Https://Doi.Org/10.1038/S41598-025-17138-3
  • Handayani, W., & Adam Yusuf, M. (2022). Analisis Dan Mitigasi Resiko Rantai Pasok Dengan Metode AHP Dan FMEA. REVITALISASI: Jurnal Ilmu Manajemen, 11(1), 43-53. doi:10.32503/revitalisasi.v11i1.2501
  • Heydarbakian, S., & Spehri, M. (2022). Interpretable Machine Learning to Improve Supply Chain Resilience, An Industry 4.0 Recipe. IFAC-Papersonline, 55(10), 2834–2839. Https://Doi.Org/10.1016/J.Ifacol.2022.10.160
  • Ivanov, D. (2021). Supply Chain Viability and the COVID-19 Pandemic: A Conceptual and Formal Generalisation of Four Major Adaptation Strategies. International Journal of Production Research, 59(12), 3535–3552. Https://Doi.Org/10.1080/00207543.2021.1890852
  • Kosasih, E. E., Papadakis, E., Baryannis, G., & Brintrup, A. (2024). A Review of Explainable Artificial Intelligence in Supply Chain Management Using Neurosymbolic Approaches. In International Journal of Production Research (Vol. 62, Number 4, Pp. 1510–1540). Taylor And Francis Ltd. Https://Doi.Org/10.1080/00207543.2023.2281663
  • Larsen, K. R., Lukyanenko, R., Mueller, R. M., Storey, V. C., Parsons, J., Vandermeer, D., & Hovorka, D. S. (2025). VALIDITY IN DESIGN SCIENCE. MIS Quarterly, 49(4), 1267–1294. Https://Doi.Org/10.25300/MISQ/2024/18064
  • Michelle Chibogu Nezianya, Ahmed Olanrewaju Adebayo, & Paschal Ezeliora. (2024). A Critical Review of Machine Learning Applications in Supply Chain Risk Management. World Journal of Advanced Research and Reviews, 23(3), 1554–1567. Https://Doi.Org/10.30574/Wjarr.2024.23.3.2760
  • Nwamekwe, C. O., & Igbokwe, N. C. (2024). Supply Chain Risk Management: Leveraging AI For Risk Identification, Mitigation, And Resilience Planning. International Journal of Industrial Engineering, Technology & Operations Management, 2(2), 41–51. Https://Doi.Org/10.62157/Ijietom.V2i2.38
  • Ordibazar, A. H., Hussain, O. K., Chakrabortty, R. K., Irannezhad, E., & Saberi, M. (2025). Artificial Intelligence Applications for Supply Chain Risk Management Considering Interconnectivity, External Events Exposures and Transparency: A Systematic Literature Review. Modern Supply Chain Research and Applications. Https://Doi.Org/10.1108/Mscra-10-2024-0041
  • Pettawali, A. F. L., & Dewita, H. (2024). Analisis Risiko Rantai Pasok Bahan Baku Dalam Memenuhi Permintaan Konsumen Pada Industri Pertambangan Andesit di Cilegon. JISI: Jurnal Integrasi Sistem Industri, 11(2), 191–212. Https://Doi.Org/10.24853/Jisi.11.2.191-212
  • Rezki, N., & Mansouri, M. (2024). MACHINE LEARNING FOR PROACTIVE SUPPLY CHAIN RISK MANAGEMENT: PREDICTING DELAYS AND ENHANCING OPERATIONAL EFFICIENCY. Management Systems in Production Engineering, 32(3), 345–356. Https://Doi.Org/10.2478/Mspe-2024-0033
  • Tarigan, Y., & Saniatul Mutmainah, S. (2023). 92-Jurnal Akuntansi. 11(1), 92.
  • Widayanti, R., Setiyowati, H., Yusup, M., & Rodriguez, M. (2026). Predicting Supply Chain Risks Using Machine Learning for Resilient Operations. ADI Journal on Recent Innovation (AJRI), 7(2), 137–148. Https://Doi.Org/10.34306/Ajri.V7i2.1376
  • Wieland, A., & Durach, C. F. (2021). Two Perspectives on Supply Chain Resilience. Journal Of Business Logistics, 42(3), 315–322. Https://Doi.Org/10.1111/Jbl.12271
  • Zhao, N., Hong, J., & Lau, K. H. (2023). Impact Of Supply Chain Digitalization on Supply Chain Resilience and Performance: A Multi-Mediation Model. International Journal of Production Economics, 259. Https://Doi.Org/10.1016/J.Ijpe.2023.108817
  • Zheng, G., & Brintrup, A. (2025). An Analytics-Driven Approach to Enhancing Supply Chain Visibility with Graph Neural Networks and Federated Learning. Http://Arxiv.Org/Abs/2503.07231
  • Zheng, G., & Brintrup, A. (2025). Enhancing Supply Chain Visibility with Generative AI: An Exploratory Case Study on Relationship Prediction in Knowledge Graphs. Http://Arxiv.Org/Abs/2412.03390

Author Biographies

Amadea Syahbani Rachela Putri, Universitas Pembangunan Nasional “Veteran”

Management Study Program, Faculty of Economics and Business, Universitas Pembangunan Nasional “Veteran”, Surabaya City, East Java Province, Indonesia

Wiwik Handayani, Universitas Pembangunan Nasional “Veteran”

Management Study Program, Faculty of Economics and Business, Universitas Pembangunan Nasional “Veteran”, Surabaya City, East Java Province, Indonesia

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Copyright (c) 2026 Amadea Syahbani Rachela Putri, Wiwik Handayani

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