## 5 Conclusions  
Based on the emerging practices extracted in this study and discussions, it is clear that most of the emerging practices are related to CBM and PdM. Industry 4.0 technologies as one set of technologies have enabled the industry to use and operationalize advanced maintenance concepts like CBM, PdM, and PsM: (1) IoT revolutionized remote condition monitoring capabilities, (2) data analytics enabled automated diagnosis, (3) data analytics and simulation analytics enabled predictive maintenance, and (4) data analytics and simulation and optimization analytics opened the world of prescriptive maintenance. The shift toward CBM and PdM is value-driven, mainly to reduce scheduled maintenance work, as offshore assets are currently relying on heavy inspection, functional testing, and maintenance campaigns to maintain high availability. However, it is worth mentioning that risk-based maintenance will continue to be an effective generic maintenance program for complex and critical systems where you need to prioritize maintenance work based on consequences. It is expanding and merging the condition, predictive and prescriptive maintenance analytics into its program. The operation of offshore assets in a remote and automated manner is now mature and enables the unmanned mode. However, maintenance is still a barrier for unmanned assets, requiring more efforts to remotely monitor, inspect the assets, and remotely execute maintenance tasks to keep unmanned intervals as desired. Merging advanced maintenance concepts such as PdM and PsM into generic risk-based maintenance is promising in terms of enabling unmanned assets to be remotely operated and maintained. Industrial facilities involved different type of assets that needs different maintenance concept, and one maintenance concept like CBM or PdM might not be feasible for all assets due to technical or economical consideration. Risk-based maintenance is more than a maintenance concept; it is a generic maintenance program that encourages a combination of maintenance concepts, with each asset selecting its best-fit maintenance concept. It is worth highlighting that reliability-centered maintenance is also a generic maintenance program that encourages a combination of maintenance concepts. In fact, reliability-centered maintenance is based on risk analysis, in which both likelihood (reliability) and consequences are considered. It is clear that all maintenance management loop functions are affected by Industry 4.0 technologies. The impact is mainly due to the new data flows that have been introduced and the new methods for planning, reporting, and analysis that are enabled. The new data flows enable new information to be extracted in order to support decisions in the utilization phase and engineering analysis in the project phase. Regarding the utilization phase, the practices observed in this study indicate a future challenge of prioritizing preventive maintenance tasks triggered by different maintenance concepts (time-based, condition-based, predictive model-based and prescribed action-based). Maintenance prioritization will depend not only on the consequence, but also on the current level of severity, the expected future level of severity (the consequence of delay in maintenance action) and the useful useful lifetime remaining. The consequence of delaying maintenance action is not the same as the consequence of failure; for example, delaying the replacement of one component might lead to deterioration in other components, which means an increase in the level of damage and level of service. The determination of maintenance intervals is also becoming more and more complicated and needs to be optimized, as it can be estimated based on different maintenance concepts. Regarding decision support, the practices observed in this study indicate a high potential for diagnostic, predictive, and prescriptive analytics to be enhanced and reinforced over time to provide more accurate and valid insights that support final decisions.