<![CDATA[This study aims to evaluate the economic engineering aspects of predictive maintenance implementation in the manufacturing industry using a systematic literature review (SLR) approach. Literature searches were conducted through five databases, namely Google Scholar, ScienceDirect, IEEE Xplore, SpringerLink, and Emerald, using keywords related to predictive maintenance, manufacturing industry, engineering economy, cost-benefit analysis, return on investment, payback period, and life cycle cost. The article selection process followed the PRISMA 2020 framework and resulted in 25 final articles from 250 articles identified in the initial search. The findings show that predictive maintenance in manufacturing is commonly implemented through machine learning, IoT sensor monitoring, vibration analysis, digital twin, and condition-based monitoring. From an economic engineering perspective, these implementations contribute to downtime reduction, lower emergency maintenance costs, improved asset reliability, and better investment efficiency. However, most studies remain dominated by technical performance evaluation, while comprehensive economic indicators such as ROI, NPV, IRR, payback period, life cycle cost, and cost-benefit analysis are still limited. This study highlights the need to integrate technical performance and economic feasibility analysis in predictive maintenance decision-making.]]>
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