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Results show significant growth after 2021, with dominant themes including AI, ML, supply chain, and supply chain resilience. Emerging topics include predictive analytics, digital transformation, digital twins, big data, blockchain, and federated learning, indicating a shift toward predictive, adaptive, and data-driven approaches. 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MANAGEMENT  \nDaniel Bunga Paillin*  \nIndustrial Engineering Department, Pattimura University, Ambon, Indonesia  \nJacobus Bunga Paillin  \nFaculty of Fisheries and Marine Sciences,, Pattimura University, Ambon, Indonesia  \n*[E-mail korespondensi:](E-mail korespondensi: dani.ti.fatek@gmail.com)[ ](E-mail korespondensi: dani.ti.fatek@gmail.com)[dani.ti.fatek@gmail.com](E-mail korespondensi: dani.ti.fatek@gmail.com)  \nABSTRAK  \nStudi ini bertujuan untuk memetakan tren perkembangan, struktur pengetahuan, dan arah penelitian masa depan untuk AI dan pembelajaran mesin dalam manajemen risiko rantai pasokan. Studi ini menggunakan pendekatan bibliometrik dengan data yang diambil dari Scopusuntuk periode 2016–Maret 2026. Analisis dilakukan melalui ko-okurensi kata kunci, visualisasi jaringan, visualisasi overlay, dan visualisasi kepadatan menggunakan VOSviewer. Hasil menunjukkan bahwa publikasi tentang kecerdasan buatan dan pembelajaran mesin dalam manajemen risiko rantai pasokan telah meningkat secara signifikan, terutama setelah tahun 2021. Tema inti yang paling dominan meliputi kecerdasan buatan, pembelajaran mesin, rantai pasokan, dan ketahanan rantai pasokan. Sebaliknya, tema-tema baru yang muncul meliputianalitik prediktif, transformasi digital, kembaran digital, big data, blockchain, dan pembelajaran federasi. Temuan ini menunjukkan pergeseran fokus penelitian dari pendekatan analitis ke pendekatan yang lebih prediktif, adaptif, dan berbasis data. Studi ini menegaskan bahwaintegrasi kecerdasan buatan dan pembelajaran mesin dalam manajemen risiko rantai pasokan masih memiliki ruang yang signifikan untuk perbaikan, khususnya dalam implementasi end-toend, penjelasan, dan integrasi teknologi digital.  \nABSTRACT  \nThis 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.  \nKeywords: Artificial intelligence; machine learning; supply chain risk management; supply chain resilience; bibliometric analysis  \n1. INTRODUCTION  \nGlobal supply chains are currently facing greater complexity due to digitalization, crossborder integration, and the growing frequency of disruptions driven by demand volatility, logistics issues, and geopolitical uncertainty. These conditions require organizations to have end-to-end visibility and more adaptive response capabilities to survive in a dynamic business environment. In this context, Artificial Intelligence (AI) and Machine Learning (ML) are gaining increasing attention because they can improve predictive capacity, accelerate large-scale data processing, and support more responsive, data-driven decision-making (Baryannis et al., 2019; Culot et al., 2024) . Similarly, the development of concepts such as the digital supply chain twin also shows great po","cbCaibUylTGOdtdW","https://ap.wps.com/l/cbCaibUylTGOdtdW","pdf",604314,13,"English","# Introduction\n## Global supply chain complexity and digitalization\n## Challenges in traditional supply chain risk management\n## Role of AI and ML for predictive, adaptive decision-making","[{\"question\":\"What method does the study use to analyze AI and ML research in supply chain risk management?\",\"answer\":\"It uses a bibliometric approach with Scopus data and performs keyword co-occurrence, network visualization, overlay visualization, and density visualization using VOSviewer.\"},{\"question\":\"What major themes dominate the research findings after 2021?\",\"answer\":\"The most dominant core themes are artificial intelligence, machine learning, supply chain, and supply chain resilience.\"},{\"question\":\"Which emerging topics indicate a shift in future research focus?\",\"answer\":\"Emerging themes include predictive analytics, digital transformation, digital twins, big data, blockchain, and federated learning.\"}]","TRENDS AND FUTURE DIRECTIONS OF ARTIFICIAL IINTELEGENCE AND MACHINE LEARNING IN SUPPLY CHAIN RISK MANAGEMENT | PDF",33]