## 4 Results
### Systematic Analysis
#### 4.2.4 Q7. How Can Enterprises Successfully Integrate Digital TPM? After defining the main technologies driving the digitalization of TPM, and examining how the traditional pillars of TPM have evolved through this digital shift, along with the opportunities and challenges it brings, it is time to look at how companies can successfully navigate this digitalization process. In order to accomplish this successfully, organizations require a coherent and systematic approach, combining technological readiness, human resource development, and strong organizational strategies. To begin with, companies must establish a clear digital maintenance strategy aligned with their overall business goals [73]. Digital TPM should not be treated as an isolated initiative, but rather as an integral part of a broader I4.0 roadmap [41]. Defining key performance indicators such as OEE, MTBF (Mean Time Between Failures), and maintenance cost reduction targets allows companies to measure progress and demonstrate the value of their initiatives [51; 55]. Once the strategic vision is defined, attention must turn to assessing the company's technological infrastructure. This infrastructure rests on four key components. First, the IoT serves as the technical foundation and backbone of digital TPM [12]. The deployment of smart sensors capable of collecting real-time data, such as temperature, vibration, and pressure, is essential [61; 66]. These sensors must be connected to cloud platforms like ThingSpeak, Firebase, or SCADA via lightweight communication networks such as BLE or LIN [53; 55; 58]. This configuration enables the continuous monitoring of machines, and it triggers automated alerts for proactive maintenance [53]. To support a smooth and progressive transition, companies must also ensure interoperability with legacy systems by establishing communication bridges through appropriate gateways [61]. Second, AI forms the analytical core of the system. AI transforms raw sensor data into actionable insights through a range of algorithms. Supervised methods such as Random Forest and k-NN [62]; unsupervised techniques like k-means and GMM [43]; and deep learning models such as LSTM [58] enable accurate anomaly detection, failure prediction, and estimation of RUL for equipment. In addition, cloud computing and Big Data technologies provide the scalable analytical infrastructure needed for digital TPM. Cloud platforms offer a flexible and adaptable architecture [74], while Big Data Analytics enables the detection of hidden patterns and trends within vast volumes of equipment data, enhancing both decision-making and predictive capabilities [51]. Furthermore, Industrial Information Systems represent the organizational structure that supports TPM operations. Systems such as CMMS, MES, and ERP are critical for organizing, automating, and tracking maintenance processes [51, 65]. These tools allow for the automatic generation of work orders, synchronization of operational and maintenance data, and integration of decision-making rules within production workflows [45]. However, the success of digital TPM is dependent not entirely on technology, but also, critically, on human factors and effective leadership [41, 61]. Employees must be equipped with more than just basic digital skills; they need to understand predictive maintenance models, interpret complex data, and confidently use digital tools such as mobile maintenance applications and AR systems [41, 51, 53, 69]. This shift calls for continuous investment in workforce development through e-learning platforms, practical workshops, and long-term training programs. These initiatives help reduce resistance to change and support smoother adoption across all levels of the organization. Equally vital is the role of leadership in driving and sustaining the transformation. Top management must actively champion digital TPM by allocating sufficient resources, setting clear priorities, and consistently communicating the strategic importance of modernizing maintenance [53, 75].