AI-RPA Integration in Cloud-Based MES for Smart Production

Abstract The integration of AI and RPA in cloud-based MES opens up new possibilities for smart production in industries. Therefore, AI and RPA must interoperate within cloud based MES to achieve smart production but the real-world integration still remains fragmented. Literature on subject is still strewn and there isn’t one clear framework that brings it all together. This study formulates and empirically validates a requirement of holistic model that integrates AI analytics, RPA task execution MES orchestration with-in the cloud infrastructure. Explains how the technological fit, cloud trust and human factors shape the operational performance. Grounded in TTF, TOE, RBV, and STS theories, the current study employs a quantitative, explanatory, cross-sectional survey (n=210) analyzed via Partial Least Squares Structural Equation Modelling (PLS-SEM), with standard screening procedures including duplicate removal, missing data treatment, outlier detection, VIF checks, and CMB assessment. The results of study analysis, leads to the cloud trust substantially that shows improved MES performance (R² = 0.57, p < .05). Compatibility (β = .32, p < .01) and cloud trust (β = .27, p < .05) significant. Furthermore, study show that the Integration challenges have an indirect effect through habitual barriers, highlighting the socio-technical aspect of digital transformation. A theory integrated framework (TTF-TOE-RBV-STS) that clarifies fit, thus this study validates theoretical contribution by connecting environment, resources and human adaption guidance for readiness/barriers, and practical contribution i.e. helping manufacturers to use AI  RPA  MES feedback blueprint that improve efficiency, adaptability and the operational resilience and decision to action latency reduction. _____________________________________________________________________ Keywords: AI-RPA Integration, Cloud-Based MES, Smart Production, Industry 4.0, Task-Technology Fit, Predictive Maintenance, Manufacturing Execution System, Automation, Closed-Loop Control, Digital Transformation.