Optimising predictive maintenance in commercial buildings in Saudi Arabia

Abstract

This systematic review examines how predictive maintenance (PdM) can improve the reliability and energy performance of commercial buildings in Saudi Arabia. PdM uses sensors, the Internet of Things (IoT), data analytics, and artificial intelligence to monitor asset condition and anticipate failures before they occur. Although these technologies are increasingly applied internationally, adoption in Saudi commercial buildings remains constrained by harsh climatic conditions, high cooling demand, implementation costs, cybersecurity concerns, and shortages of specialised skills. The review synthesises evidence on maintenance challenges, PdM applications, and the transferability of international practices to the Saudi context. The findings indicate that effective implementation requires predictive models calibrated to local temperature, dust, equipment, and energy-use conditions; durable sensing infrastructure; workforce development; and cooperation among building owners, technology providers, policymakers, and academic institutions. These measures support the energy-efficiency, digital-transformation, and sustainability objectives of Saudi Vision 2030. Overall, context-specific PdM can reduce unplanned downtime, improve asset reliability, and strengthen the long-term sustainability of Saudi commercial buildings.