Implementation of Internet of Things for Predictive Maintenance in Manufacturing Industry - Pratama, Rizky Adi; Collins, James Andrew (RESWARA: Jurnal Riset Ilmu Teknik, 2025)
The rapid advancement of Industry 4.0 has accelerated the adoption of Internet of Things (IoT) technologies in manufacturing systems, particularly in predictive maintenance applications. Traditional maintenance strategies, such as corrective and preventive maintenance, often result in unplanned downtime, increased operational costs, and inefficient resource utilization. This study aims to analyze and synthesize recent scientific literature on the implementation of IoT-based predictive maintenance in the manufacturing industry, focusing on system architecture, data acquisition, analytics techniques, and operational impacts. A qualitative systematic literature review method was employed, analyzing peer-reviewed journal articles, conference proceedings, and book chapters published between 2020 and 2025. The findings indicate that IoT-enabled predictive maintenance significantly improves equipment reliability, reduces downtime by up to 50%, lowers maintenance costs, and enhances production efficiency. The integration of machine learning, edge computing, and digital twin technologies further strengthens real-time decision-making and failure prediction accuracy. This study contributes by providing a comprehensive and structured understanding of IoT-driven predictive maintenance implementations and identifying research gaps related to scalability, data interoperability, and cybersecurity. The results serve as a reference for both researchers and practitioners seeking to adopt predictive maintenance solutions in smart manufacturing environments.