One of first of such models was developed in, wherein the authors sought to maximise the number of groups in order to consequently reduce inefficiencies related to availability. The developed tool is a step forward for grouping (or any scheduling scheme) strategies due to its flexibility to be implemented in almost any case, enabling comparison between different grouping algorithms. Reporting is focused on six maintenance sub-functions, enabling the decision maker to easily assess performance of any maintenance process, thereby improving the quality of decisions. Performance indicators were carefully developed considering a stochastic perspective when possible this enables decisions to be influenced by risk assessment under a costs view. The reportability tool is based on specially developed indicators based on current maintenance standards for better implementation and considers a formerly developed grouping strategy with poor embedded performance indicators as an implementation case for the tool. In this article, we report on the design and implementation of a reportability tool using Microsoft Power BI embedded with Python script to assess opportunistic grouping schemes under a preventive maintenance policy.
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