Reportability Tool Design: Assessing Grouping Schemes for Strategic Decision Making in Maintenance Planning from a Stochastic Perspective

Abstract

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. 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. Performance indicators were carefully developed considering a stochastic perspective when possible; this enables decisions to be influenced by risk assessment under a costs view. 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. 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.