Industrial maintenance decision-making: A systematic literature review |
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| Affiliation: | 1. Department of Industrial Engineering, University of Jeddah, Jeddah, Saudi Arabia;2. Manufacturing Department, Cranfield University, Cranfield, United Kingdom;3. Propulsion Engineering Centre, Cranfield University, Cranfield, United Kingdom;1. Division of Operation and Maintenance Engineering, Luleå University of Technology, Sweden;2. Department of Information Engineering, University of Florence, via S. Marta 3, 50139 Florence, Italy;1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China;2. Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada M5S 3G8;1. Department of Industrial Engineering, Texas Tech University, Lubbock, TX 79409, USA;2. Department of Industrial Engineering, Western New England University, Springfield, MA 01119, USA;3. Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA;1. Construction Management and Engineering, University of Twente, Enschede, The Netherlands;2. Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta, Canada |
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| Abstract: | The increasing competition among industries has leveraged the emergence of various tools and methods for maintenance decision-making support. This paper identifies in literature the application areas of industrial maintenance decision-making, the relationships between these areas and the ways in which authors integrate tools and methods. This information makes it possible to identify trends and deficiencies in this context, helping to centralize the efforts required for future work. This work follows a series of structured steps for a systematic literature review of papers related to the main topic available in online databases. The selected papers are subject to a content assessment and grouped according to the application areas. The direct comparison between these areas and the construction of a relational matrix provide a quantitative interpretation of the results and well-structured information. Additionally, this paper proposes a framework based on information from the literature, which summarizes the origin and flow of information used in the development of models, showing the relationship among application areas of decision making. The research undertaken identifies trends focused on joint production systems optimization and increasing the deployment of methods for autonomous equipment predictions. |
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| Keywords: | Industrial maintenance Decision-making Systematic literature review |
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