Detecting critical building maintenance health status through automatic performance-based assessment

Purpose Large organizations receive numerous daily maintenance requests that must be assessed efficiently. Facility managers need rapid support to detect critical conditions and reduce functionality losses. A key performance indicators (KPIs)-based approach is introduced to quantify the relevance of intervention requests from a Computerized Maintenance Management System (CMMS). This work aims to develop quick metrics to help facility managers monitor relative impacts across different locations and activity types. Design/methodology/approach A data set of about 17,000 maintenance requests from an Italian university was analyzed. Each request was categorized by activity type (based on Omniclass classification), priority and programmability. This information is combined through the analytical hierarchy process to define a Relevance Index (RI) measuring the impact of different building clusters on the overall health status of facilities. Findings RI provides a rapid overview of corrective maintenance needs and performance levels. Critical conditions are identified and compared, quantifying that the larger and most crowded buildings cluster (i.e. engineering faculty buildings) has the highest impact. Practical implications RI can be integrated within CMMS (and customized for other large organizations) to provide facility managers with a data-driven tool for evaluating requests relevance and facilities health status, ultimately contributing to safer and more functional workplaces. Originality/value The proposed methodology fills the current gap in rapid metric for automatically assessing the relevance of maintenance issues across building stocks. By translating unstructured maintenance requests into a quantifiable RI, the framework strengthens facility managers decision-making and benchmarking within and between assets.