Analisis Tren Penerapan Economic Evaluation pada Predictive Maintenance Berbasis Iot di Industri Manufaktur: Sebuah Systematic Literature Review - Rizky Hardi nata (Fakultas Keguruan dan Ilmu Pendidikan Universitas Sebelas Maret Surakarta, 2026)
Predictive Maintenance (PdM) based on the Internet of Things (IoT) has emerged as a strategic approach to improve equipment reliability, reduce unplanned downtime, and optimize maintenance costs in manufacturing industries. However, despite the rapid advancement of PdM technologies, studies focusing on their economic evaluation remain relatively limited. This study aims to identify the economic evaluation methods used in PdM-IoT literature, analyze the reported economic benefits, identify key implementation challenges, and propose future research directions. A Systematic Literature Review (SLR) was conducted following the PRISMA protocol. Literature was collected from Scopus, Google Scholar, and IEEE Xplore databases for the period 2019–2025. From an initial pool of 268 publications, 20 articles met the inclusion criteria and were analyzed in depth. The findings indicate that Return on Investment (ROI) and Net Present Value (NPV) are the most frequently used methods for evaluating the economic feasibility of PdM-IoT implementation. The reported benefits include reduced maintenance costs, minimized downtime, improved asset availability, and enhanced operational efficiency. Nevertheless, several challenges remain, including high initial investment costs, data integration complexity, sensor infrastructure requirements, and difficulties in quantifying indirect economic benefits. The study concludes that economic evaluation plays a crucial role in supporting PdM-IoT investment decisions and highlights the need for more comprehensive evaluation frameworks tailored to specific industrial contexts.