Integration of Digital Twins and IoT Sensors to Support Monitoring Accuracy and Maintenance Effectiveness in the Manufacturing Industry in the Cikarang Region - Farida Arinie Soelistianto; Salwa Aulia Noviasari; Rully Fildansyah (West Science Interdisciplinary Studies, 2026)
This study investigates the integration of digital twin technology and Internet of Things (IoT) sensors in improving monitoring accuracy and maintenance effectiveness within manufacturing companies in the Cikarang industrial region. A quantitative research design was employed using data collected from 125 respondents representing operational, maintenance, engineering, and managerial roles. The measurement instrument was developed using a Likert scale, and data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS 3). The results indicate that the integration of digital twins and IoT sensors has a significant positive effect on monitoring accuracy, demonstrating that real-time data synchronization and simulation capabilities enhance system visibility and anomaly detection. Monitoring accuracy is also found to significantly influence maintenance effectiveness, highlighting the importance of accurate and timely information in supporting predictive maintenance practices. Furthermore, digital integration directly affects maintenance effectiveness, indicating that simulation-based insights and real-time data contribute to improved maintenance planning and reduced downtime. The model explains 61.2% of the variance in maintenance effectiveness (R² = 0.612), suggesting strong explanatory power. This study contributes to the literature on Industry 4.0 by providing empirical evidence on the role of integrated digital technologies in enhancing operational performance in a developing country context. Practically, the findings suggest that manufacturing firms should prioritize the integration of digital twins and IoT sensors to achieve higher monitoring accuracy and more effective maintenance strategies.