An approach to evaluate tactical decision-making in industrial maintenance

Abstract

This work comprises a part of a global and modular framework for Maintenance Decision Support Systems, whose general objective is the proposal of a system that assists an expert in decision making for the design of maintenance program customized in a productive plant. This system starts from the alignment with the strategic objectives of the company, after with the tactical and operational maintenance. This paper deals the tactical goal, which defines a dynamic and graphic Balanced Scorecard to extracts knowledge and prediction of the indicators in the medium term. The proposed custom Balanced Scorecard design integrates the use of Pivot Tables and Pivot Charts in Microsoft Excel© with Machine Learning in Matlab©. We apply "Top-Down" strategies to address a very abundant data with Pivot Chart filters on a data. Artificial Neural Networks algorithms are used in order to recognize patterns and to establish a correspondence between input and outputs variables.