Integrating Predictive Maintenance Analytics into a Cloud-based CMMS for Smart Work Order Management and Resource Allocation

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There is a massive amount of data generated in the maintenance practices as related to components of buildings, facilities, and infrastructure. This data is mostly passively stored in databases as maintenance records. Data analytics technologies present an opportunity to turn this large volume of data into useful information and generate knowledge to enhance the performance, reliability, and efficiency of future maintenance practices. In this regard, predictive analytics can be utilized beyond failure prediction and diagnosis (ensuring reliability of physical systems) and towards an intelligent (smart and predictive) management of maintenance activities to enhance resource efficiency. The main objective of the proposed project is to establish R&D into the integration of AI technologies into a cloud-based maintenance administration process with a focus on maintenance work-order management and resource allocation. Using a select set of clustering methods, the set of differentiating attributes (features) and an optimal (minimum) number of clusters are identified to classify work-order management patterns. In addition, a smart resource allocation rule-based systems is designed to assign the staff to the jobs in an intelligent manner such that to reduce resource intensity and improve the cost efficiency. Using decision tree classifiers, the identified patterns from historical data are utilized to generate a set of decision rules for future maintenance projects, establishing a predictive approach toward resource allocation. Finally, a KPIs-based assessment system will be developed as a means of providing feedback to the above AI-based algorithms for validation and improvement purposes.