## 4 Results
### Systematic Analysis
#### 4.2.2 Q5  
How Does Industry 4.0 Transform the Traditional Foundations of TPM? Industry 4.0 technologies have profoundly transformed the core pillars of TPM, embedding digital intelligence into processes that were traditionally manual, reactive, and document-driven. The analysis of the 33 articles reveals that this transformation extends beyond technology itself, it reshapes practices, decision-making timelines, and operator roles. In the Autonomous Maintenance pillar, connected sensors, SCADA systems, and Cyber-Physical architectures now automate routine inspections and continuously monitor equipment conditions [45; 60]. Instead of executing predefined manual tasks, operators interact with intelligent interfaces, receiving real-time alerts and decision support. For example, [PERSON] et al. [51] present a four-layer system, from data acquisition to real-time visualization, that allows operators to prioritize interventions through smart HMIs rather than merely report anomalies. Moreover, AR, delivered via smart glasses or tablets, further enhances autonomy by guiding users through procedures hands-free, improving both inspection accuracy and responsiveness [69]. From another perspective, the Planned Maintenance pillar is shifting from static scheduling to dynamic, predictive planning [58]. AI-powered models, including LSTM and ensemble forecasting, leverage real-time machine data to anticipate failures and trigger maintenance only when needed [58; 62]. This transition is exemplified by the iSMART model proposed by [PERSON] and [PERSON] [64], which uses reinforcement learning in a semi-Markovian framework to optimize maintenance policies. Supporting this, cloud-based CMMS platforms such as Mpoint enable the automation of task planning, tracking, and the full traceability of maintenance activities [65]. In the Focused Improvement pillar, digitalization has turned reactive problem-solving into a continuous, data-driven loop [51]. The IoT and SCADA systems feed live operational data into machine learning algorithms that detect inefficiencies and support rapid root cause analysis [58]. [PERSON] and [PERSON] [59] further illustrate this with a digital twin that simulates production scenarios and identifies improvement opportunities before losses  
Figure 7: Digital TPM technologies. materialize. FP-Growth algorithms, as shown by [PERSON] et al. [51], reveal hidden correlations between events and unplanned downtime, refining continuous improvement strategies. Real-time dashboards also enhance cross-functional collaboration and allow for a measurable impact on KPIs (key performance indicators) such as OEE [48; 51; 55]. The Training and Education pillar has evolved into an immersive learning environment [69]. AR provides real-time procedural guidance, reducing onboarding time and human error [69]. Operators are not passive recipients of training anymore, but active participants interacting directly with machine data via mobile dashboards and cloud-based platforms, reinforcing their skills through real-world feedback [53; 55]. This shift also makes the traditional lines between training, quality, and safety less clear, encouraging a more unified performance culture [51]. In the Quality Maintenance pillar, sensors and analytics tools detect deviations before defects occur [51]. Predictive models analyze patterns in operational data, enabling early interventions, while MES systems ensure traceability throughout the production chain [51; 57]. Root cause analysis is increasingly supported by algorithmic insights rather than relying solely on human experience. The Safety, Health, and the Environment pillar also benefits from digital integration [49]. Embedded sensors identify hazards such as overheating or abnormal vibrations and automatically trigger alerts or shutdowns [66]. In parallel, AR guides operators through high-risk tasks while keeping their hands free, improving safety without compromising productivity [69].