Research on the Construction and Application of an Intelligent Work Order Management System

With the rapid development of information technology, intelligent work order management systems offer significant advantages in improving work efficiency and service quality. This paper designs and implements an intelligent work order management system based on big data analysis and machine learning techniques, aiming to optimize the work order processing workflow, enhance processing efficiency, and improve customer satisfaction. The system introduces technologies such as early warning models, real-time reminder mechanisms, and automated outbound calling systems, significantly reducing work order timeout rates, improving work order acceptance rates, and optimizing the response time for sensitive requests. After implementing the early warning mechanism, the timeout rate of work orders decreased from 11.67% to 4.00%, and the acceptance rate increased from 83.33% to 95%. Additionally, the system’s stability and scalability were effectively validated, ensuring efficient operation in future high-concurrency environments. Experimental results show that after the intelligent transformation, the system performs excellently on several key metrics, with work order processing time reduced from 12 hours to 8 hours, response time for sensitive requests shortened from 20 minutes to 5 minutes, and customer satisfaction increased from 70% to 90%. These improvements effectively reduced labor costs and significantly enhanced customer satisfaction, demonstrating strong practical application value.