Literature Review : Penerapan Machine Learning pada Supply Chain Management
DOI:
https://doi.org/10.55338/justikpen.v5i2.427Kata Kunci:
machine learning, supply chain management, resiliency, demand forecasting, stockoutAbstrak
Tinjauan literatur ini bertujuan untuk menganalisis kontribusi Machine Learning (ML) dalam mengatasi tantangan utama dalam supply chain management (SCM), khususnya pada peramalan permintaan, prediksi backorder dan stockout, serta penguatan ketahanan rantai pasok. Metode yang digunakan adalah systematic literature review (SLR) dengan menganalisis tujuh publikasi yang relevan dari database ScienceDirect selama periode 2020-2025, perkuat dengan analisis co-occurrence tematik menggunakan VOSviewer. Hasil tinjauan menunjukan dominasi penggunaan supervised learning, dimana algoritma ensemble secara konsisten menunjukkan akurasi (R2 hingga 0.99) dalam memprediksi peristiwa SCM. Secara tematik, ditemukan adanya pergeseran fokus riset menuju analitik preskriptif dan mitigasi risiko. Penelitian ini menunjukan bahwa ML tidak hanya berfungsi sebagai alat prediksi, tapi juga sebagai alat utama dalam mencapai sistem SCM yang efisien dan tangguh terhadap ketidakpastian.
Referensi
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S. Taghiyeh, D. C. Lengacher, A. H. Sadeghi, A. Sahebi-Fakhrabad, and R. B. Handfield, “A novel multi-phase hierarchical forecasting approach with machine learning in supply chain management,” Supply Chain Analytics, vol. 3, p. 100032, Sep. 2023, doi: 10.1016/j.sca.2023.100032.
M. Gabellini, F. Calabrese, F. G. Galizia, M. Ronchi, and A. Regattieri, “An information-sharing and cost-aware custom loss machine learning framework for 3PL supply chain forecasting,” Computers & Industrial Engineering, vol. 210, p. 111573, 2025, doi: 10.1016/j.cie.2025.111573.
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Hak Cipta (c) 2026 Muhammad Fauzani Akbar, Ita Damaris Br Munthe, Albert Julian Marito Silaban, Ahmad Dwi Abiyyu, Artha Putri Br Karo
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