Investigation and Leveling of Factors Affecting Preventive Maintenance and Repairs Using ISM (Case study: Roga Gostar Company)

The main purpose of this study was to identify and level the most important factors affecting preventive maintenance and repairs using interpretive structural modeling (ISM).

The present study employed descriptive survey method. The data were collected through a variety of methods, including library resources and field studies (interviews and questionnaires) and the use of websites (the Internet). To identify the relationships between variables, the expert judgment system (Delphi method) was used and a questionnaire was distributed to the experts to examine the pair-wise interaction of indicators in terms of their effectiveness and impact on each other. Questionnaires that included the information for the research model were randomly distributed among 10 elites. The qualitative data were analyzed through grounded theory method and the data collected through the questionnaire were subjected to factor analysis. In this model, the initial access matrix was obtained by converting the structural self-interaction matrix into a dual value matrix (zero and one). In the next step, the relationships and leveling of the factors were determined and then the final model was drawn.

MICMAC software was used to analyze the data obtained through the questionnaires. Based on the MICMAC matrix, it was observed that factors 1 to 7 were in the dependent region, which means that they have low driving power but a high degree of dependence on other factors. Factors 8 to 14 were in the driver power zone. These factors have high driving power with minimal dependence.

According to the model obtained from the ISM technique, the required budget index was identified as the most effective factor. Therefore, we must first identify the necessary budget factors for maintenance and repair. The ISM graph shows a clear picture of causal relationships between the factors. Also, the appropriate checklist index was identified as the most highly influenced index.