AI-Enabled Predictive Maintenance and Inventory Management for Smart Buildings: A GISBIM Data Fusion Approach to Supply Chain Optimization

Smart buildings generate massive spatial and operational data, but existing facility management systems are often not fully utilized due to the separation of geographic information system (GIS) and building information modeling (BIM) platforms. This paper proposes a data integration framework integrating GIS and BIM, providing support for AI-driven predictive maintenance and dynamic inventory management of smart buildings. By aligning the Internet of Things sensor data with the fine-grained BIM asset model and GIS spatial background, this system constructs rich feature vectors and introduces the gradient hoist stacked model and long short-term memory network to achieve accurate fault time prediction. Meanwhile, the prediction results stimulate the real-time dynamic replenishment strategy and adjust the spare parts inventory threshold. For large commercial facilities, this method reduced the mean absolute error of defect prediction at 12 hours (a 30% increase over the baseline model), reduced the inventory shortage rate by 40%, and reduced the number of inventory days on hand by 25%. Overall, this integration solution accelerated maintenance response by 20% and saved an average of US$50,000 in annual holding costs. These achievements demonstrate the value of GIS-BIM integration in promoting the shift from passive operations and maintenance to active and efficient supply chain strategies. Subsequent work will focus on real-time flow processing and the multi-facility collaboration mechanism.