International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064




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Research Paper | Software Engineering | United States of America | Volume 13 Issue 6, June 2024 | Rating: 3.8 / 10


Intelligent Service Order Management with AI Integration

Naveen Koka [6]


Abstract: This paper outlines the implementation of an AI - driven system designed to enhance order management and service efficiency, particularly within the contexts of electronics manufacturing and manufacturing plants. The system leverages machine learning to analyze various data points from past work orders, including issue descriptions, parts used, labor involved, expenses incurred, time taken for resolution, and final resolution status. By predicting necessary parts and resources for future orders, the system ensures they are pre - ordered and ready, minimizing downtime and improving service readiness. Technicians benefit from this system by receiving comprehensive information upon arriving at a customer's location, including detailed problem descriptions, ordered parts, required labor, and relevant historical data. This immediate access to pertinent information allows technicians to begin work promptly and resolve issues more efficiently, significantly enhancing customer satisfaction. For electronics manufacturing, where large units and numerous service requests are common, the system optimizes customer satisfaction by ensuring quick and accurate responses to service needs. In manufacturing plants, the system aids in the regular servicing of areas, even when parts are not involved, by optimizing labor and expense management and scheduling maintenance efficiently. Technical implementation steps include data collection and preparation, model development, AI system integration, user interface design, real - time data processing, continuous learning, testing, deployment, and ongoing monitoring and maintenance. The system employs regression models for predicting service costs and completion times, and recommendation algorithms for suggesting necessary parts or solutions based on historical work orders. Ultimately, this AI - driven system streamlines the service process, boosts technician productivity, and enhances customer satisfaction through quicker, more reliable service, leading to smoother operations and better overall outcomes for both technicians and customers.


Keywords: LLM, AI, Technician, Machine learning


Edition: Volume 13 Issue 6, June 2024,


Pages: 1888 - 1891



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