Digital Twin Predictive Maintenance for Manufacturing SMEs

Abstract

Digital twins can connect equipment condition, operational context, models, and maintenance decisions, but many manufacturing small and medium enterprises lack the data, integration capacity, and specialist staff required by high complexity implementations. This technical review develops a minimum viable digital twin framework for predictive maintenance. It distinguishes a digital model, digital shadow, and bidirectionally connected twin, then identifies the capabilities that produce maintenance value: asset identity, synchronized condition data, contextual history, state estimation, anomaly detection, diagnosis, prognosis, decision support, and feedback from completed work. The proposed architecture follows condition monitoring standards and separates acquisition, data manipulation, state detection, health assessment, prognostic assessment, advisory generation, and human interaction. A readiness matrix helps organizations choose between threshold monitoring, data driven models, physics based models, and hybrid approaches. The implementation roadmap begins with one consequential failure mode and a measurable maintenance decision rather than a broad platform purchase. It uses staged gates for data quality, baseline detection, shadow recommendations, workflow integration, and scaling. Unlike prior digital twin reviews that catalogue enabling technologies and definitions across manufacturing broadly, this review’s contribution is a minimum-viable capability sequence and cost-aware readiness matrix scoped specifically to the resource constraints of manufacturing SMEs, where a full high-fidelity twin implementation is often not viable. The review emphasizes lifecycle governance, model uncertainty, configuration management, and the cost of maintaining the twin itself. The main conclusion is that a useful manufacturing digital twin is not defined by visual realism or data volume. It is defined by a maintained correspondence between physical asset state and a decision model that improves a documented maintenance outcome while preserving traceability and human accountability.