## 4 Results
### Systematic Analysis
#### 4.2.3 Q6  
What Are the Challenges and Opportunities Associated with the Implementation of Digital TPM?  
The digitalization of TPM in the era of I4.0 offers undeniable transformative potential, both in terms of equipment performance and organizational agility [61]. However, integrating this new generation of TPM depends on technical, human, and systemic conditions that generate significant opportunities but pose substantial challenges. A cross-analysis of the 33 studies highlights this dual reality, where performance gains are counterbalanced by implementation constraints.  
Among the most compelling opportunities, operational performance improvements stand out. Numerous studies report significant gains in OEE, increased equipment availability, and reduced downtime, largely enabled by real-time monitoring and predictive capabilities [53; 55; 58; 64; 65; 67]. Furthermore, intelligent analytics and decision-support systems based on machine learning enhance diagnostic precision, fault prediction, and maintenance planning [58; 62; 64; 67; 71].  
Digital TPM also contributes to sustainability and quality through reduced emissions, improved product consistency, fewer defects, and better energy and material efficiency [12; 69; 60]. From a workforce perspective, the use of AR, mobile HMIs, and IoT interfaces enhances operator autonomy and accuracy, supporting faster onboarding and reducing training errors [69]. Economically, digital TPM enables better resource efficiency by minimizing unnecessary interventions, extending asset lifespan, and lowering maintenance costs [55; 62].  
Lastly, integration and traceability are greatly improved through the adoption of CMMS, MES, and cloud platforms that connect maintenance and production data, providing end-to-end visibility and supporting standardized workflows [45; 51; 55].  
Yet, despite these benefits, digital TPM faces several persistent challenges [50]. Technically, data-related issues are widespread, ranging from poor data quality and heterogeneity to difficulties in threshold setting and system initialization [51; 58; 62]. Sensor management and integration with legacy systems also remain complex and resource-intensive [66]. Moreover, technological maturity and scalability remain problematic. Many digital TPM models suffer from limited generalizability or rely on configurations that are not easily transferable across different industrial environments.  
On the human and skills front, the lack of technical expertise, insufficient operator training, and resistance to change hinder adoption [68; 72], especially in small and medium-sized enterprises (SMEs), where digital literacy gaps are more pronounced [48; 53; 63].  
Organizationally, challenges include cultural resistance, weak data governance, and siloed departments [12; 50]. These factors limit the alignment of TPM with broader operational strategies and often result in isolated, short-lived initiatives. And finally, the cost of implementation, especially for advanced techniques, represents a major barrier, particularly in developing contexts or budget-sensitive industries [57].  
Looking beyond these findings, it becomes clear that while the digitalization of TPM offers clear and measurable advantages across performance, sustainability, and workforce efficiency, its success depends on the ability to overcome significant technical, human, and organizational barriers. The path forward requires investment not only in technologies but also in skills, culture, and cross-functional coordination. Therefore, bridging these gaps is essential to unlock the full potential of a truly integrated and resilient TPM 4.0 framework.