## 5 Conclusions and Managerial Recommendations  
The research proposes the framework formulation and optimization model for the opportunistic grouping strategy in the planning of preventive maintenance activities for a wastewater treatment plant. A review of the operating context, as well as the formulation of the framework and the respective optimization model, is addressed in detail through this research. The computational results from the implementation of the model reveal in turn a considerable decrease in the level of unavailability associated with the primary treatment line and sludge treatment, which implies the potential to greater cost-efficiency and an increase in productivity and performance levels through optimized strategies for maintenance management in the WWTP under analysis. However, it is necessary to consider the limitations and the challenges that the framework formulation pose, among which are the consideration of a fixed periodicity under perfect maintenance assumption, and the incapability to stochastically include the failure phenomenon and its impact on maintenance plans. Other challenges posed through this research include: the impact of the maintenance execution and opportunistic groups modeling with multiple failure modes per equipment; the incorporation of different maintenance policies and grouping strategies with a focus on repairable systems; and alternatively, the possibility of modeling degradation, along with stochastic and structural interaction of the components involved in the complex production systems. These limitations in turn represent research lines and suggestive challenges that will be addressed in future research, which are directly related to the capacity of the framework to improve its flexibility, adherence, and applicability of the grouping strategy in several industrial settings. Based on the computational implementation and the obtained results, several recommendations and insights focused on the maintenance management decision-making are presented below:  
* The maintenance grouping framework must be applied, restructured, and updated periodically to ensure effectiveness as an operational support tool. Said update process \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Number of Total** & **Number of Binary** & **Number of** & **Number of** \\  & **Variables** & **Variables** & **Continuous Variables** & **Constraints** \\ \hline Theoretical & \(5\hat{j}+\hat{n}+\hat{k}\) & \(\hat{j}+\hat{n}+\hat{k}\) & \(\hat{4j}\) & \(9\hat{j}+9\hat{n}+\hat{\varrho}\) \\ \(e=0\%\) & \(351\) & \(179\) & \(172\) & \(679\) \\ \(e=1\%\) & \(441\) & \(269\) & \(172\) & \(978\) \\ \(e=2\%\) & \(501\) & \(329\) & \(172\) & \(1196\) \\ \(e=3\%\) & \(531\) & \(359\) & \(172\) & \(1305\) \\ \(e=4\%\) & \(579\) & \(407\) & \(172\) & \(1487\) \\ \(e=5\%\) & \(624\) & \(452\) & \(172\) & \(1663\) \\ Max. Theoretical & \(2924\) & \(2752\) & \(172\) & \(13{,}287\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Number of variables and constraints under several tolerance factor values (\(e\)). must be managed both at an operational level, (through the requirements of parts and pieces, personnel qualification, and repair and logistic-delay times) as well as at a tactical level, (e.g., faced with the incorporation or redesign of tasks through maintenance plan design tools such as Reliability Centered Maintenance). * The framework assumes that components can necessarily share fixed setup costs through the clustering process. This information must be corroborated at a tactical level, by reviewing the procedures for the execution of PM activities, as well as the effect of said activities on the productivity level or on the treatment plant cost function. Otherwise, the framework formulated will not allow to optimally improve the cost-efficiency of the current maintenance plan. * It is also assumed that a certain work package can share a setup cost, regardless of the number of grouped components.