Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Journal of Applied Engineering and Technological Science (JAETS) ARIKA Archipelago Engineering
Daniel Bunga Paillin
Program Studi Teknik Industri Universitas Pattimura
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering Transportation

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 21 Documents
Search

 1   2   3 
Trends and Future Directions of Artificial Intelligence and Machine Learning in Supply Chain Risk Management Daniel Bunga Paillin; Jacobus Bunga Paillin
ARIKA Vol 20 No 1 (2026): ARIKA
Publisher : Industrial Engineering Study Program, Pattimura University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/arika.2026.20.1.35

Abstract

This study aims to map development trends, knowledge structures, and future research directions for AI and machine learning in supply chain risk management. This study uses a bibliometric approach with data taken from Scopus for the period 2016–March 2026. Analysis was conducted through keyword co-occurrence, network visualization, overlay visualization, and density visualization using VOSviewer. The results show that publications on artificial intelligence and machine learning in supply chain risk management have increased significantly, especially after 2021. The most dominant core themes include artificial intelligence, machine learning, supply chain, and supply chain resilience.In contrast, more recent emerging themes include predictive analytics, digital transformation, digital twins, big data, blockchain, and federated learning. These findings indicate a shift in research focus from an analytical approach to a more predictive, adaptive, and data-driven approach. This study confirms that the integration of artificial intelligence and machine learning in supply chain risk management still has significant room for improvement, particularly in end-to-end implementation, explainability, and digital technology integration.
Co-Authors A. Rahawarin Abdulla Pelu Adha B. Dharma Amos Kilay Aprilia Indah B. J. Camerling Billy Jhones Camerling D. Tumanan Deivy Tutarima Erlon Wattimena Farid Abdullah Fhony M. Kaihatu Galang Pradipta Goldsteyn M. O. Masela Hartrisari Hardjomidjojo J. M. Tupan Jacobus Bunga Paillin Jerry Rieuwpassa Johan Marcus Tupan Kevin M. Runteh Machfud Machfud Meiske D. Sopacua Meisly Tamaela Muhammad Romli dan Suprihatin Andes Ismayana N. Suruali Nil Edwin Maitimu Pius Nusmesse Robert Hutagalung Suprayogi Suprayogi Victor Oryon Lawalata Wardis Girsang