## 4 Results
### Bibliometric Analysis
#### 4.1.1 Q1.
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Title** & **Authors** & **Year** & **Citations** & **Ref.** \\ \hline \multirow{3}{*}{Integration of Industry 4.0 technologies into Total Productive Maintenance practices} & [PERSON]; [PERSON]; & \multirow{3}{*}{2021} & \multirow{3}{*}{193} & \multirow{3}{*}{[6]} \\  & [PERSON]; [PERSON]; & & & \\  & [PERSON] & & & \\ \hline Predictive Maintenance 4.0 as next evolution step in industrial maintenance development & [PERSON]; [PERSON]; [PERSON] & 2019 & 114 & [41] \\ \hline Intelligent machine learning based total productive maintenance approach for achieving zero downtime in industrial machinery & [PERSON]; [PERSON]; & \multirow{3}{*}{2021} & \multirow{3}{*}{106} & \multirow{3}{*}{[42]} \\  & [PERSON]; [PERSON]; & & & \\  & [PERSON] & & & \\ \hline Fault class prediction in unsupervised learning using model-based clustering approach & [PERSON]; [PERSON] & 2018 & 88 & [43] \\ \hline Innovative airport 4.0 condition-based maintenance system for baggage handling DCV systems & [PERSON]; [PERSON]; [PERSON] & 2019 & 86 & [44] \\ \hline Innovative Approach to Preventive Maintenance of Production Equipment Based on a Modified TPM Methodology for Industry 4.0 & [PERSON]; [PERSON]; [PERSON]; & \multirow{3}{*}{2021} & \multirow{3}{*}{82} & \multirow{3}{*}{[45]} \\ \hline An Agile System to Enhance Productivity through a Modified Value Stream Mapping Approach in Industry 4.0: A Novel Approach & [PERSON]; [PERSON]; & \multirow{3}{*}{2021} & \multirow{3}{*}{58} & \multirow{3}{*}{[46]} \\ \hline Cybersecurity Concerns for Total Productive Maintenance in Smart Manufacturing Systems & [PERSON]; [PERSON]; [PERSON]; [PERSON]; & \multirow{3}{*}{2019} & \multirow{3}{*}{57} & \multirow{3}{*}{[47]} \\ \hline Digitization of processes in manufacturing SMEs—value stream mapping and OEE analysis & [PERSON] [PERSON]; [PERSON] & \multirow{3}{*}{2022} & \multirow{3}{*}{53} & \multirow{3}{*}{[48]} \\ \hline The impact of Industry 4.0 on the relationship between TPM and maintenance performance & [PERSON]; [PERSON]; [PERSON]; & \multirow{3}{*}{[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] & \multirow{3}{*}{2022} & \multirow{3}{*}{45} & \multirow{3}{*}{[49]} \\ \cline{1-1}  & [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Most cited publications. Figure 3: Top 10 countries contributing to publications. Starting first with publications, the most cited article is [PERSON] et al. [6], which has received 193 citations and created the foundation for combining digital technologies with traditional TPM methods. Other key contributions include the study of [PERSON] et al. [41] on predictive maintenance as a major step in modern maintenance, with 114 citations, and another by [PERSON] et al. [42] on machine learning for zero downtime in industrial equipment, with 106 citations. These works have helped define the main directions of research, especially in areas like AI, predictive analytics, and intelligent systems. In addition, some papers have also explored cybersecurity [47] and digitalization in small businesses [48], showing that the field is expanding into diverse topics. At this stage, and after presenting the most cited publications, it is equally important to examine the most dominant publication sources in this research field. The Figure 4 below highlights the most frequent publication sources, offering insights into the journals and conferences that serve as key platforms for advancing research in this domain. Research in this field is published in a mix of journals and conference proceedings. The International Conference on Industrial Engineering and Operations Management stands out as the most common source, with 10 papers. It serves as a platform for applied studies in industrial maintenance. Several journals also appear frequently, including _Sustainability, Procedia Manufacturing, Computers and Industrial Engineering, Processes, Electronics_, and _Applied Sciences_.