Artificial Intelligence For Supply Chain Efficiency In Batam’s Manufacturing Sector
DOI:
https://doi.org/10.71305/ijemr.v3i1.1125Keywords:
Artificial Intelligence, Machine Learning, Internet of Things (IoT), Predictive Analytics, Resource-Based View (RBV);Abstract
This study investigates the role of Artificial Intelligence (AI) in enhancing supply chain efficiency in Batam’s manufacturing sector, focusing on firms in the electronics, shipbuilding, plastics, automotive, and related industries operating within the Free Trade Zone and Special Economic Zone. Drawing on the Resource-Based View (RBV), AI is conceptualized as a strategic capability comprising Machine Learning (ML), Robotics and Automation (R&A), Internet of Things (IoT), Natural Language Processing and Chatbots (NLP&C), and Computer Vision (COMV). Data were collected through a structured questionnaire administered to managers and supervisors from 320 purposively selected firms and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The study also models Supply Chain Automation Processes (SCAP)—including inventory, logistics, procurement, warehouse operations, and predictive maintenance—as a mediating mechanism between AI adoption and Supply Chain Efficiency (SCE). The results show that IoT and ML are the most influential AI technologies, significantly improving operational efficiency, accuracy, responsiveness, and customer satisfaction. Inventory management, warehouse automation, logistics optimization, and predictive maintenance emerge as critical automation domains translating AI capabilities into tangible performance gains. R&A and COMV exhibit weaker or context-dependent effects, reflecting capital intensity and integration challenges, particularly for small and medium-sized enterprises. Overall, AI and SCAP jointly explain a substantial proportion of the variance in supply chain efficiency, highlighting that AI yields the greatest benefits when embedded in end-to-end automation. The findings provide theoretical contributions by disaggregating AI into specific sub-technologies and practical guidance for firms and policymakers in Batam to prioritize IoT- and analytics-driven initiatives for scalable, resilient, and competitive supply chains.
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Copyright (c) 2025 Stephanie, Inda Sukati

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