## 4 Results and Discussion
### Impact on Generic Maintenance Program  
To summarize the results obtained from these case studies, Table 10 is provided where the technology(s), the mindset(s), and the maintenance concept(s) are traced for each case study. The results highlight that \"IoT and Cloud Services\", \"Machine Learning and AI\", and \"Simulation modeling\" are the most targeted technologies by several case companies and case studies. IoT, cloud services, machine learning, and AI are needed to enable any maintenance program to collect data and process them online. It is worth mentioning that generative AI was not observed in these case studies. Simulation modeling had two different purposes in most of the case studies: (1) to quantify the cost and benefit of applied technology or mindset, and (2) to generate the P-F curve that supports predictive and prescriptive maintenance analytics. Several emerging practices related to autonomous vehicles and remote operating vehicle technology (robots, mobots, drones) were developed with the aim of reducing the risk and expenses of manning. Some emerging practices were related to additive manufactured spare parts, VR, and AR for training and assisting maintenance technicians. Please note that most of the equipment considered in these seven case studies are critical from the safety, production, or maintenance expenses point of view. Therefore, their spare parts are still manufactured in the traditional manner with a heavy certification involved. However, suppose the additive-manufactured spare parts are more regulated and certified in the future; it can \begin{table}
\begin{tabular}{l l} \hline \hline
**No.** & **Emerging Practice** \\ \hline
1 & New worksheet to determine maintenance concept and best available technology \\
2 & New worksheet to determine diagnostic coverage of turbine monitoring system \\
3 & New model to estimate the effectiveness of the applied Industry 4.0 technologies \\
4 & New guideline to handle inspection image data \\
5 & New guideline to design AR-assisted inspection \\
6 & New worksheet to explore the technology big picture \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Summary of emerging practices in case study 7.  
be expected that more emerging practices will be observed to engineer these spare parts, evaluate their reliability, and manage their digital warehouse and supply. Regarding digital twin technology, some emerging practices related to some case studies (Cases 4 and 5) are observed to transform documents into models using model-based systems engineering practices. The capability level of these digital twins is at the \"descriptivelevel \" and aims at the \"diagnostic\" level according to the DNV guidelines [57]. The use of model-based systems engineering is not limited to building digital twins but also to gain value or save the expenses of rework at the handover phase (between the project and utilization phases). It can be observed that the level of using Industry 4.0 technologies is driven by the value that stakeholders might gain. It is clear that \"IoT and Cloud Services\", \"Machine Learning and AI\", and \"Simulation modeling\" technologies provide higher value for operators (in terms of safety, production, and maintenance expenses) compared to \"additive manufactured spare parts\", \"VR and AR\" and \"autonomous vehicles\". Moreover, \"IoT and Cloud Services\", \"Machine Learning and AI\", and \"Simulation modeling\" technologies are needed to enable advanced maintenance concepts like condition-based, predictive, and prescriptive. In Table 10, condition-based maintenance is the most emerging maintenance concept enabled by Industry 4.0 technologies. Predictive maintenance is also emerging due to machine learning and cloud computing technologies. The prescriptive maintenance concept is also emerging to offer an alternative to predictive maintenance, especially when the maintenance interval for unmanned assets is very long and the predictive horizon does not cover the entire maintenance interval.