Planning and competitiveness in maintenance management

PurposeThis paper aims to present the results of an analysis of two hypotheses on maintenance in manufacturing industries: the first hypothesis refers to managers' perception of planning maintenance when undertaking certain practices, and the second hypothesis to the behavior of manufacturing companies that consider their maintenance sector a source of competitive advantage for their organization.Design/methodology/approachThe research study is carried out in medium‐sized and large manufacturing companies in a large metropolitan area of Northeastern Brazil. An exploratory analysis is made of the variables considered representative for this study. Data are collected from a probability sampling of companies selected randomly and in which interviews are later held. Analyses are performed in order to identify associations that validate the hypotheses constructed. The hypotheses are tested and can be explained by variables presented in the paper.FindingsIt is observed that some maintenance practices are currently correlated to the manager's perception of planning for maintenance and the treatment given to the area of maintenance displays a positive relationship with the perception of management in relation to maintenance as a source of competitive advantage for the organization.Practical implicationsThe results obtained from this paper will assist maintenance engineers with understanding how the maintenance manager perceives planning and competitiveness and also what maintenance actions should in fact be implemented. The findings have yielded more knowledge of the maintenance environment and using this knowledge will be able to improve maintenance performance.Originality/valueThe results of this study are relevant for the maintenance area and will enable it to pay more attention to issues relating to planning and to the competitiveness of organizations. The advantages of probability sampling are also demonstrated, as is the fact that the findings from the statistical analysis can be generalized to the population investigated.