Improve predictive maintenance through the application of artificial intelligence: A systematic review

Abstract

Facility operations and maintenance are defined as the functions, duties, and labor required daily to operate and preserve a facility asset to ensure its original function is available for its primary use and its functions are maintained throughout the facility's life. Organizations, facility management professionals, and their stakeholders expend billions of dollars annually to perform this function in the United States. Much of the cost is on inadequate facility operations that may be avoided. Utilizing the theoretical lens of the adaptive structuration theory, this rapid evidence assessment shall review the current body of scholarly literature to identify how artificial intelligence can be used with predictive maintenance to reduce a facility operations program's operations and maintenance costs. Through an organized systematic review process, this research shall utilize peer-reviewed scholarly articles published within the last 5 years to perform a rapid evidence assessment of predictive maintenance and artificial intelligence in facility operations. Through this rapid evidence assessment, the research finds three common themes that respond to the research question. The most significant theme is artificial intelligence, once implemented in the process, provides unbiased investment and repair recommendations from the analyzed data. An unanticipated discovery of interest is that the current body of literature identifies insufficient data as the number one barrier to the full implementation of artificial intelligence within a facility operations program.