## 2 Materials and Methods
### Step 1: System Analysis and Project Planning  
The first step in the six-step modeling and simulation process starts with a system analysis addressing the needed fundamentals to attain an understanding of the system's behavior, i.e., structure, interfaces, processes, interactions, etc. To do so, relevant stakeholders must be addressed, including their needs and requirements to the system under study. Then, the model constraints must be defined by studying, e.g., system context, hierarchy, interface architecture, and functional and physical architecture in greater detail. In addition, other features posing a significance to the purpose must be identified, e.g., politics, market, technology.  
The purpose of the system analysis step is to (1) identify the purpose and objective of the simulation, (2) analyze the case study data that is required to conceptualize the maintenance management practices (scheduled, corrective, condition monitoring, opportunistic intervals), especially, workflow, rules, conditions, and actions, (3) analyze the case study data that is required as inputs for the simulation model e.g., failure rates, maintenance service times, and (4) analyze the case study data that is required to validate the simulated behavior e.g., real availability and real corrective maintenance workload.  
The purpose of the proposed multi-method simulation model is to simulate and estimate the potential lifetime benefits of implementing an intelligent maintenance management system in terms of availability and corrective maintenance workload during a time period of 20 years. Thus, the simulated outputs of the computational model address: (1) operational behavior, (2) maintenance event: timeline and workload, and (3) the occurrence of failures allocated at the component level. It is evident that operational availability is essential for the case company, as the end-user consumption is traceable to industrial operation and human welfare in Europe. Therefore, the operational behaviors including availability and unavailability caused by failures and the need for corrective maintenance are analyzed. This is easiest illustrated through a time-plot diagram showing continuous availability and unavailability as a function of time during operation. The maintenance event timeline of both scheduled maintenance and corrective maintenance is analyzed. First, a scheduled maintenance event timeline is analyzed as it introduces opportunistic maintenance intervals whereas future predicted failures can be allocated. Second, a corrective maintenance event timeline that demonstrates the corrective maintenance events required by the different use case scenarios is analyzed. This is especially interesting when it comes to comparing the corrective use case scenarios with the intelligent maintenance scenario. The maintenance workload is analyzed to demonstrate the allocation of maintenance management. The number of component failures occurring during operation is analyzed to compare different use case scenarios, which supports highlighting the number of corrective maintenance events that can potentially be replaced with intelligent maintenance. In addition, it addresses possible differences in input data originating from the empiric case study and the OREDA database.  
To simulate the possible lifetime benefits of incorporating an intelligent maintenance system into the specific case study, data concerning failure rates and MTTR values of the specific case of interest is needed. In addition, data that enables determining the capabilities of detection, diagnosis, and prognosis of such a system is also needed. To do so, an analysis tool that has been developed by the authors on a previous occasion can be adapted [16]. A more detailed system analysis has already been performed and presented by the authors in [22].