## 4 Results
### Systematic Analysis
#### 4.2.1 Q4  
What Are the Main Technological Enablers of Digital TPM? The transformation of TPM in the Industry 4.0 context is driven by several key digital technologies. These technologies combine to make TPM more connected, predictive, and data-driven [49; 50]. From the analysis of 33 articles, nine major categories of technologies stand out: IoT, AI, cloud computing, Big Data Analytics, Industrial Information Systems, CPSs, human-machine interfaces, augmented reality, and digital twins. The IoT is at the forefront of digital TPM. It is the main technology that allows machines to automatically and in real time collect data such as temperature, vibration, pressure, and lubrication. This data helps with condition-based and predictive maintenance strategies [51; 52]. Moreover, smart sensors, connected via lightweight infrastructures like BLE (Bluetooth Low Energy) and LIN (Local Interconnect Network) to cloud platforms such as ThingSpeak [53], SCADA (Supervision Control and Data Acquisition) [54], or Firebase [55], allow for the real-time visualization of machine status on mobile devices and HMIs (Human-Machine Interfaces) [53]. This connectivity enables key functionalities such as automated part counting, fault classification, and proactive alert generation, which have contributed to measurable performance improvements, including increased availability,  
Figure 6: Keyword co-occurrence network. reduced MTTR (Mean Time To Repair), and enhanced OEE [52, 56, 57, 58]. Furthermore, the IoT is frequently integrated with AI models, cloud services, and digital twins to anticipate failures and facilitate data-driven decision-making [43, 58, 59]. Additionally, it supports circular traceability in sustainable manufacturing practices [60] and reinforces interoperability with legacy systems [61]. However, its success is largely dependent on operator training and successful organizational integration; without these, the anticipated benefits might be minimal or even detrimental [49, 50]. At the same time, AI is considered complementary to the IoT by enabling smart, adaptive maintenance. Random Forest, k-NN (K-Nearest Neighbors), and Naive Bayes are examples of supervised learning methods that have been able to predict failures with up to 96% accuracy [62]. Also, rule-based approaches like Association Rule Mining (ARM) combined with FMEA (Failure Modes and Effects Analysis) help prioritize failures according to their criticality, supporting TPM strategies and increasing OEE by up to 10% [63]. Meanwhile, even in the absence of labeled datasets, deep learning architectures (such as LSTM--Long Short-Term Memory) and unsupervised techniques (such as GMM (Gaussian Mixture Model) and k-means) enable precise anomaly detection and RUL (Remaining Useful Life) prediction [43, 58]. Notably, reinforcement learning techniques, such as iSMART and Relative Actor-Critic, have enabled real-time, self-optimizing maintenance policies without relying on probabilistic models [64]. Moreover, Big Data and cloud computing together form the digital core of modern TPM systems [50, 51]. The real-time aggregation of data is enabled through cloud computing platforms such as ThingSpeak and Firebase [55], while Cloud-native CMMS (Computerized Maintenance Management System) services like Mpoint support the planning, execution, and traceability of maintenance activities [65]. At the same time, Big Data analytics enables the large-scale processing and analysis of equipment data [50], with practical applications including supervised ensemble models to forecast reliability [62], unsupervised clustering techniques to analyze sensor data [43], and FFT-based vibration analysis on MEMS (Micro Electro-Mechanical Systems) sensors for early fault detection [66]. Another key enabler consists of Industrial Information Systems (IISs), including CMMS, MES (Manufacturing Execution System), ERP (Enterprise Resource Planning), and SCADA, essential in the integration and control of TPM data and processes [45, 51, 65].