## 4 Conclusions  
Aligned with the main objective of this article and its scope, a reporting tool was successfully developed to address timely decisions and easy interpretation, enabling decision makers to quickly decide between strategies and accurately analyse different scenarios. Furthermore, the designed tool is able to adapt to different algorithms, as it requires little information from the process, enabling future work or even implementation in a real system or case study. The latter is a major improvement for maintenance management and grouping strategies in particular, as the same tool is able to measure different scheduling approaches with little information needed, improving decision quality and the capacity to compare different strategies from varied sources. The developed indicators obtained through a rigorous analysis of the algorithm and the maintenance standards enable a transversal and disaggregated view of the different areas directly or indirectly related to the maintenance function, leading to the possibility of deciding not only which tolerance is best-suited for a given system but to manage different aspects of maintenance before and after a tolerance has been chosen. In other words, is possible to decide which strategy to implement in parallel with other management decisions, such as how to manage the workforce, resources, and purchases. To develop the indicators, it was necessary to perform a comprehensive analysis of the strength and weaknesses of the algorithm; based on this analysis, we determined that little and inaccurate information was available, resulting in a timeline perspective enabling precise measurement of all developed indicators. This suggests that further improvements can be achieved by considering this perspective in relation to future algorithms or modifications of the original algorithm. The precision achieved in this study improves the information available for decision makers, also improving the quality of decisions. Moreover, the developed perspective may even be used as a baseline to develop new grouping algorithms addressing more precise decisions not only based on time but also risk and resource allocation. Despite the limitations of Power BI, it was possible to design and implement a simple and efficient report that precisely measures each of the designed indicators, presenting them in an easy-to-use format. Moreover, it was possible to grant autonomy to the report,  
Figure 9: Administration and supply section. enabling the decision maker to adjust, modify, or even completely change the studied instance and parameters. Whenever changes are made to the parameters of the algorithm, important changes can be observed in the algorithm, accounting for the precision of the same and the variability associated with implementing different scenarios. Considering that only one scenario was presented, there is an opportunity to test more instances for the same algorithm to define and analyse tendencies and behaviour. Additionally, it is even possible to test different algorithms under the same report, enabling comparisons of possible decisions in order to determine which algorithm is best-suited for each case. Regarding risk analysis of the strategies, it is necessary to integrate a risk variability analysis, mainly because whenever tolerance is available, there is a range of possible risks to assume related to how much of the tolerance is being used for each execution and activity. In other words, a specific tolerance hides a wide range of possibilities that are not measured in this article. The analysed algorithm tends to disregard risk but is possibly compensated by the advancement of other tasks and the savings achieved due to lower setup costs in contexts wherein the setup cost is high.