## 1 Introduction  
It is a deeply internalised fact that equipment for any industrial process is subject to degradation produced either by the regular use of the asset or the unavoidable passage of time. Maintenance is a fundamental piece within asset management, as its main concern is to minimise the direct and indirect consequences of failure arising as a result of degradation [1]. Most of the time, maintenance interventions should address the availability and reliability needs of the organisation, which is the reason such executions involve positive consequences, such as an increase in reliability levels, prevention of early degradation of assets or even the early identification of damaged subsystems or parts that may lead into fault state. Several studies have been conducted to address more accurate and robust modelling, accounting for the interactions, criticality of components [2], and the downstream effect of components that, after failure, allow for normal performance for a limited time [3]. Nevertheless, during the execution of maintenance actions, the asset or a whole subsystem (according to dependencies) may become unavailable, which can develop into a major issue when the cost associated with lack of production is high. Therefore, \"organisations increasingly realise that they can improve their efficiency and reliability by planning maintenance interventions more effectively. This results in more preventure maintenance actions that are also better aligned with other business functions, such as production scheduling and spare parts control\" [1]. Although there are currently several efforts to solve maintenance scheduling problems, such as the model proposed by [PERSON] et al. [4] or any of the models presented in [5], these efforts rest on the fact that their effectiveness and impact on a real situation depend almost exclusively on the capability of management to implement, adapt and control the models, proving the difficulty of adapting such models to the specific context and conditions of an organisation [6]. One of first of such models was developed in [7], wherein the authors sought to maximise the number of groups in order to consequently reduce inefficiencies related to availability. They further developed the model in [8] with the objective of minimizing the number of stoppages instead of maximizing the groups, achieving higher levels of availability and precision in comparison with the original proposed model. More recently, the same authors presented a model available for implementation, also addressing opportunistic grouping strategy for maintenance scheduling in [9], aiming to minimise downtime but considering the use of resources in the modelling. These frameworks address a methodology to determine the optimal grouping strategy under certain conditions using Gurobi solver within a Python script. Consequently, these conditions the restrain framework, and therefore, the optimal solutions found by the algorithm may be suboptimal, depending on the maintenance management focus to assess maintenance scheduling. According to the latter, in this paper, we addresses the need to report performance to maintenance management, enabling observation and analysis in a clear and simple manner with appropriate information and the most influential indicators for the life cycle cost of the assets benefiting planning and maintenance scheduling. Therefore, in this article, a reporting tool and its design are developed to assess maintenance scheduling strategies and, in particular, the grouping strategy proposed in [9], where the authors only presented simple and inaccurate metrics to compare performance between strategies, resulting in low-quality decisions regarding the strategy to pursue. This will be achieved by using Microsoft Power BI Desktop (PBI) and appropriate performance indicators (KPI) to benefit the decision-making process related to maintenance planning for a multi-component system.