###### Abstract
Therefore, the industry has really begun to appreciate the benefits of applying modeling and simulation methodologies as a supportive function to enable assessing the behavior and predicting the future outcome of, e.g., maintenance management. For example, Shoreline AS provides a simulation model that helps to simulate possible maintenance alternatives for offshore wind farms and select the most cost-effective by considering operational aspects such as weather forecast, accessibility, and resources i.e., technicians and type of vessel. Moreover, Miriam RAM studio simulates the availability and productivity of O&G installations based on reliability analysis. This helps designers to redesign or design out items to enhance availability and overall equipment effectiveness. These industrial simulation tools shall be enhanced until they capture and are able to estimate all the lifetime benefits of an intelligent maintenance management system. For example, industrial managers are looking forward to estimating the lifetime benefits of PdM and the potential opportunistic maintenance intervals in (1) reducing the corrective maintenance and unintended maintenance events, (2) reducing the preventive maintenance workload and minimizing the planned maintenance campaigns, (3) reducing the level of damage and repair, and (4) extending the lifetime of industrial assets. The desired simulation tool shall support estimating the scheduled maintenance workload (maintenance campaigns), potential corrective maintenance workload, PdM capabilities (effectiveness and earliness), and the planned and potential opportunistic maintenance intervals to perform intelligent maintenance. These basic functions shall enable industrial managers to (1) redesign their maintenance campaigns and potential corrective maintenance (with the help of intelligent maintenance) to fit opportunistic maintenance intervals, (2) reschedule maintenance campaigns at the utilization phase, and (3) redefine their loading and operating profile (optimal profile to produce as high as possible at a deterioration rate as low as possible) either for short-term tactical decisions as toleration to utilize the next potential opportunistic maintenance (avoid unintended maintenance visit) or for long-term strategic decisions to extend their assets' lifetime. For instance, [PERSON] [2] illustrates how the change in loading profile (from stand-by redundancy to preschedule redundancy) extends the asset lifetime. In this case study, two out of three crude oil pumps were operating continuously, while one pump was in stand-by mode functioning as redundancy (triggered once one of the other two pumps fail). Following this, the company decided to change the operating policy and run each pump based on time, whereas each pump was operating for two months followed by one month in redundancy (monthly shift between the pumps to ensure that two pumps were running continuously). The state-of-the-art of simulation models for maintenance practices shows three schools of thinking: discrete event, system dynamics, and agent-based modeling. The discrete-event simulation models for maintenance services and failure events are nicely summarized by [PERSON], [PERSON] et al. [3]. These models have the objective of independently simulating preventive maintenance and corrective maintenance events (dueto probabilistic failures) and consider them as discrete events that return the asset to a state of \"as good as new\". First, maintenance practitioners and researchers [4; 5] have noticed that preventive maintenance has a long-term effect on corrective maintenance and maintenance resources, and they defined the \"shift the burden\" phenomenon. Second, they noticed that preventive maintenance activities fix the symptoms of failures but might not fix the fundamental problem or cause of the propagating deterioration.