## 6 Conclusions and Perspectives  
In an increasingly digitalized industrial landscape, maintenance has evolved from a reactive support function into a strategic lever for performance, flexibility, and competitiveness. This shift is particularly significant in the era of Industry 4.0, where the convergence of CPSs, AI, and real-time data analytics is fundamentally reshaping traditional production and maintenance paradigms. Within this changing environment, TPM must now be redefined to align with new technological and organizational imperatives.  
To address this issue, this article adopted a two-phase methodology: a bibliometric analysis to map the scientific landscape of digital TPM, followed by a systematic literature review to identify and synthesize key technological and conceptual contributions. The analysis of 58 relevant publications highlighted the main enablers of digital TPM, including the IoT, AI, digital twins, and augmented reality, and demonstrated how these technologies are transforming the foundational TPM pillars. In parallel, the study outlined both the opportunities offered by digital TPM (improved OEE, predictive maintenance, enhanced operator autonomy) and the persistent challenges (technological integration, training gaps, cultural resistance).  
Despite the growing interest in this topic, the current literature remains fragmented and primarily focused on isolated or sector-specific implementations. A clear theoretical and practical standard is still lacking, limiting the scalability and consistency of TPM 4.0 adoption--particularly among small and medium-sized enterprises. To bridge this gap, this study proposed a foundation for a standardized implementation framework, structured around four key dimensions: (i) modular technological integration; (ii) a phased deployment methodology; (iii) organizational readiness and cross-functional collaboration; and (iv) performance evaluation through standardized indicators such as OEE, MTBF, an MTTR.  
These dimensions together form the conceptual basis for a scalable and operational reference model that is both academically grounded and industrially applicable. They provide a roadmap for organizations seeking to align traditional TPM principles with the demands of smart manufacturing while also enhancing adaptability and resilience in the face of ongoing digital transformation.  
As a continuation of this research, future work will focus on the detailed development and validation of a generalized framework for digital TPM, grounded in the dimensions identified here and enriched through empirical case studies. This next phase will aim to fill the gap between theoretical modeling and practical deployment, contributing to both academic knowledge and industrial excellence in smart maintenance.  
Conceptualization, Z.M. and M.G.; methodology, Z.M.; validation, M.G., S.M., and A.S.; formal analysis, Z.M., M.G., and A.S.; investigation Z.M. and M.G.; resources, Z.M. and M.G.; data curation, Z.M.; writing--original draft preparation, Z.M.; writing--review and editing, Z.M., M.G., and M.D.N.; visualization, M.G., S.M., and A.S.; supervision, Z.M. and M.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.