<![CDATA[This study investigates the impact of Digital Twin and Edge AI technologies on optimizing industrial machine maintenance in Karawang, Indonesia. Using a quantitative research approach, data were collected from 80 respondents via a Likert-scale questionnaire and analyzed using SPSS version 25. The findings reveal significant positive relationships between both technologies and maintenance optimization. Digital Twin technology enhances predictive maintenance by enabling real-time simulations, while Edge AI improves decision-making through decentralized data processing. Together, they explain 58% of the variance in maintenance optimization. These results emphasize the synergistic effects of these technologies in reducing downtime, improving operational efficiency, and achieving cost savings. This research contributes to the understanding of advanced technological adoption in industrial maintenance and provides practical implications for enhancing productivity in industrial settings.]]>
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