Automated Maintenance Prioritization: Implementing Natural Language Processing and Sentiment Analysis in Industrial Workflows

Abstract

In the rapidly evolving landscape of industrial operations, the need for efficient maintenance prioritization is paramount. This research paper explores the integration of Natural Language Processing (NLP) and Sentiment Analysis (SA) to enhance maintenance decision-making processes in industrial workflows. By leveraging vast amounts of unstructured data generated in industrial environments such as equipment logs, maintenance requests, and operator feedback this approach seeks to optimize maintenance schedules, reduce downtime, and improve overall operational efficiency. The findings reveal that the combination of NLP and SA not only aids in identifying critical maintenance needs but also enriches communication between stakeholders, facilitating a more responsive and proactive maintenance strategy. Ultimately, this paper underscores the significance of integrating advanced technologies into traditional maintenance frameworks to drive innovation and productivity.