AI-Driven Work Order and Asset Management Systems in Facility Operations Using Natural Language Processing

Abstract

Manual work orders and asset management systems often result in delays, inefficiencies, and communication errors in facility operations. This study proposes an AI-driven framework that employs Natural Language Processing (NLP) to automate the classification, prioritisation, and processing of maintenance requests, as well as the tracking of asset lifecycles. A fine-tuned BERT-based NLP model was developed to extract critical information, such as fault type, urgency level, and asset identifiers, from unstructured maintenance text logs. Integrated into a decision support module, the system automatically generates structured work orders and recommends technician assignments based on asset history and task severity. Evaluation using over 10,000 real-world maintenance logs showed that the model achieved 91% classification accuracy and reduced work order processing time by 45%. The findings underscore the potential of NLP to enhance the responsiveness, efficiency, and intelligence of Computerised Maintenance Management Systems (CMMS). This research contributes to the digital transformation of facility management by demonstrating the value of AI in enabling proactive and data-driven maintenance operations.