Sistem Informasi Prediktif Menggunakan Machine Learning dalam Manajemen Rantai Pasok
Keywords:
sistem informasi, Machine Learning, Rantai Pasok, ManajemenAbstract
Penelitian ini bertujuan untuk menganalisis peran sistem informasi prediktif berbasis machine learning dalam meningkatkan kinerja manajemen rantai pasok. Fokus penelitian diarahkan pada kemampuan sistem prediktif dalam mendukung peramalan permintaan, pengelolaan inventori, optimasi logistik, dan peningkatan keandalan pemasok. Pendekatan penelitian menggunakan metode kuantitatif dengan desain empiris, yang mengintegrasikan analisis data historis rantai pasok dan penerapan beberapa algoritma machine learning untuk menghasilkan informasi prediktif yang akurat dan relevan bagi pengambilan keputusan. Hasil penelitian menunjukkan bahwa sistem informasi prediktif mampu meningkatkan akurasi peramalan permintaan secara signifikan, mengurangi tingkat kelebihan dan kekurangan persediaan, serta meningkatkan efisiensi distribusi dan koordinasi antar aktor rantai pasok. Temuan ini juga menunjukkan bahwa integrasi machine learning ke dalam sistem informasi manajemen memberikan nilai strategis melalui penyediaan informasi berbasis data yang real time dan terintegrasi lintas fungsi. Selain dampak operasional, sistem ini berkontribusi pada peningkatan resiliensi dan keberlanjutan rantai pasok dengan mendukung pengambilan keputusan yang lebih adaptif terhadap ketidakpastian pasar. Namun, penelitian ini mengidentifikasi bahwa kualitas data, kesiapan infrastruktur teknologi, dan kompetensi sumber daya manusia menjadi faktor penentu keberhasilan implementasi. Oleh karena itu, penelitian ini menegaskan pentingnya pendekatan holistik yang menggabungkan aspek teknologi, proses bisnis, dan tata kelola organisasi dalam pengembangan sistem informasi prediktif berbasis machine learning untuk manajemen rantai pasok
References
Ahmad, S. (2025). AI-driven predictive analytics for proactive supply chain planning. Open Science Framework. https://doi.org/10.31219/osf.io/fr6h5
Ahamed, S., Kanimozhi, P., Kumar, T. A., et al. (2025). AI-driven food supply chain management with machine learning for demand forecasting. Proceedings of IEEE Conference. https://doi.org/10.1109/incet64471.2025.11140424
Ahmed, S. R., Hussein, A., Fadel, H. A., et al. (2024). Machine learning applications in supply chain management: Predictive power unleashed. Proceedings of IEEE Conference. https://doi.org/10.1109/hora61326.2024.10550647
Anumula, S. K. (2025). Optimizing supply chain management with AI-powered predictive analytics. Journal of International Commercial Law and Technology, 44(4). https://doi.org/10.52783/tjjpt.v44.i4.1644
Achatbi, I. (2024). Leveraging machine learning techniques to forecast and enhance supplier reliability in supply chain management. Transactions on Machine Learning and Artificial Intelligence, 12(6). https://doi.org/10.14738/tmlai.1206.18078
Batra, G., & Batra, A. (2025). Optimizing supply chain performance using AI and machine learning: A predictive analytics approach. Journal Article. https://doi.org/10.71143/n5d6fr98
Bhuvaneswari, L. (2025). Artificial intelligence in supply chain management: A strategic tool for efficiency. European Economics Letters, 15(2). https://doi.org/10.52783/eel.v15i2.3350
Chowdhury, W. A. (2025). Optimizing supply chain logistics through AI & ML: Lessons from NYX. International Journal of Data Science and Machine Learning, 5(1). https://doi.org/10.55640/ijdsml-05-01-10
Devega, M., Kusrini, K., Utami, E., et al. (2025). Transformative impact of data diversity and machine learning on supply chain management. IEEE Conference Proceedings. https://doi.org/10.1109/iccit65724.2025.11167560
Duttyal, S. (2025a). Autonomous inventory intelligence: ML-driven predictive and prescriptive analytics for supply chain optimization. World Journal of Advanced Engineering Technology and Sciences, 15(3). https://doi.org/10.30574/wjaets.2025.15.3.1009
Duttyal, S. (2025b). Machine learning-based supply chain technologies in the tech industry. European Modern Studies Journal, 9(4). https://doi.org/10.59573/emsj.9(4).2025.99
Eni, L. N., et al. (2023). From data to decisions: Leveraging machine learning in supply-chain management. Journal Article. https://doi.org/10.52783/tjjpt.v44.i4.1644
Gatla, T. R., & Mandati, S. R. (2020). Optimizing supply chain efficiency through machine learning-driven predictive analytics. International Journal of Emerging Industrial Research and Technology, 7(3), 67–80. https://doi.org/10.26662/ijiert.v7i3.pp67-80
Hasan, M. R. (2024). Predictive analytics and machine learning applications in the USA for sustainable supply chain operations and carbon footprint reduction. Journal of Environmental Sustainability, 5(1). https://doi.org/10.52783/jes.5138
Hasan, S. K., Islam, M. A., Asha, A. I., et al. (2024). The integration of AI and machine learning in supply chain optimization: Enhancing efficiency and reducing costs. International Journal for Multidisciplinary Research, 6(5). https://doi.org/10.36948/ijfmr.2024.v06i05.28075
Lin, H. F., Lin, J., & Wang, F. (2022). An innovative machine learning model for supply chain management. Journal of Innovation and Knowledge, 7(4), 100276. https://doi.org/10.1016/j.jik.2022.100276
Pham, X. V., Maag, A., Senthilananthan, S., et al. (2020). Predictive analysis of the supply chain management using machine learning approaches: Review and taxonomy. IEEE Proceedings. https://doi.org/10.1109/CITISIA50690.2020.9371842
Rahman, M. T. (2025). Predictive supply chain analytics: MIS-integrated AI models for U.S. manufacturing resilience. Preprints. https://doi.org/10.20944/preprints202508.2135.v1
Rane, N., Choudhary, S., & Rane, J. (2024). Artificial intelligence and machine learning for resilient and sustainable logistics and supply chain management. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4847087
Rane, N., Desai, P., Kaya, Ö., et al. (2024). Artificial intelligence, machine learning, and deep learning for sustainable and resilient supply chain and logistics management. Book Chapter. https://doi.org/10.70593/978-81-981367-4-9_5
Rattan, P. (2025). Reshaping supply chain management using artificial intelligence. Book Chapter. https://doi.org/10.1201/9781998511471-10
Selvaraj, K., & Lakshmanan, L. (2023). The machine learning for predictive maintenance in supply chain management. Journal of Artificial Intelligence and Machines, 1(1). https://doi.org/10.55124/jaim.v1i1.233
Sangeetha, G., Harshavardhan, S. V., Vaughanan, M. V. L., et al. (2024). Predictive analysis for supply chain management using extreme gradient boost classifier. International Journal of Advanced Research in Science, Communication and Technology. https://doi.org/10.48175/ijarsct-15987
Wang, Z. (2024). Data-driven supply chain performance optimization through predictive analytics and machine learning. Applied and Computational Engineering. https://doi.org/10.54254/2755-2721/2025.18487
Wilson, G., Johnson, O., & Brown, W. (2024). The role of machine learning in predictive analytics for supply chain management. Preprints. https://doi.org/10.20944/preprints202408.0343.v1
Zhang, J., Wang, Y., & Zidu, W. (2024). Enhancing supply chain forecasting with machine learning: A data-driven approach to demand prediction, risk management, and demand-supply optimization. Journal of Fintech and Business Analysis. https://doi.org/10.54254/3049-5768/2024.18321
Zaichenko, I. M., & Yakovleva, M. A. (2019). Predictive analytics in supply chain management. Journal of Economics and Management, 2, 18–22. https://doi.org/10.31775/2305-3100-2019-2-18-22
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Karmila Sari, Evi Endarti
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
