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Alternative maintenance strategy selection using analytic hierarchy process: An application in the manufacturing industry

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2016
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Abstract (EN)

The relation between maintenance and product quality is big enough that cannot be ignored. In today's economy, in order to provide a competitive advantage for companies, keeping the quality goods cheap at any time and place has a critical importance. The main priority of this research is determine a maintenance strategy that allows companies delivering their products to the customers on time without disrupting the production process. In this context, it has been decided that using AHP method is appropriate to determine the most suitable one among the maintenance strategies for the companies are supposed to choose. First of all a literature review has been made and main criteria have been found out. Main criteria for the practice in this study are cost, safety, feasibility and competitive advantage. These alternatives are Repair (Corrective Maintenance), Time Based Maintenance, Condition Based Maintenance, Total Productive Maintenance and Reliability Centred Maintenance. These alternatives have been determined according to knowledge and experience of staff of the company which the practice has been made in. Gathered data by AHP method have been observed using Expert Choice software and results have been evaluated. According to all these calculations, maintenance strategies have been arranged in order to importance level. As the result of the study, Total Productive Maintenance has been found the most suitable maintenance strategy for the factory. However, it shouldn't be ignored that the most suitable maintenance strategy may vary depending on capacity of factory, production field, types of used machines and any other factors. Key Words Analytic Hierarchy Process, Multi-Criteria Decision Making, Maintenance Management, Maintenance Strategy Selection.

Author

Hasan Dinç

How to Cite

Hasan Dinç (Doctorate thesis). Alternative maintenance strategy selection using analytic hierarchy process: An application in the manufacturing industry, 2016, Dicle University.

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