An Empirical Decision-Support Framework Integrating Statistical Learning and Digital Twin for Predictive Maintenance Planning in Industry 4.0

Abstract

The integration of Digital Twin technology with advanced analytics has created new opportunities for predictive maintenance in Industry 4.0. This study develops and empirically validates a framework that examines how statistical learning capability and Digital Twin integration influence predictive maintenance performance. Data were collected through a quantitative cross-sectional survey of 210 industry professionals, and the proposed relationships were evaluated using Structural Equation Modelling (SEM) with bootstrapping. The findings show that statistical learning capability, when integrated, greatly improves predictive maintenance efficiency (β = 0.61, p < 0.001). Additionally, incorporating Digital Twin into predictive maintenance affects both predictive efficiency (β = 0.47, p < 0.001) and fault detection accuracy (β = 0.58, p < 0.001). Maintenance performance is largely mediated by the influence of predictive maintenance efficiency (β = 0.66, p < 0.001) on the link between statistical learning capability and performance (indirect effect β = 0.40, p < 0.001). The model explains well and adds to the body of literature providing an empirically proven relationship between statistical learning and Digital Twin technologies. The cross-sectional design of the study, however, may limit the generalizability of the results and studies based on real-time data and longitudinal data across sectors are recommended. In a practical sense, the results demonstrate the importance of having an integrated analytical and digital strategy for organizations to improve predictive capability, minimize downtime, and optimize maintenance planning.