Development of intelligent systems for labor conflict management: An approach from industrial engineering and organizational sociology

Abstract

The development of the intelligent system for managing workplace conflicts was carried out with the goal of enhancing the identification, prevention, and resolution of conflicts within organizational settings by leveraging advanced tools in artificial intelligence and social network analysis. The system was designed to detect conflict patterns, generate automated solutions, and dynamically adapt its responses using machine learning techniques, natural language processing, and graph theory analysis. The methodology included a detailed analysis of the organizational structure, the implementation of a survey to assess perceptions and workplace dynamics, and the deployment of a continuous feedback system to improve the system’s performance. Network analysis made it possible to identify key employees with high social influence and detect critical points in the organizational structure, while sentiment analysis and automated responses strengthened the system’s ability to resolve conflicts efficiently. The results showed a significant improvement in conflict detection accuracy (from 60% to 90%), a 30% reduction in the number of reported conflicts, and an increase in employee satisfaction (from 60% to 88%). The improvement in the work environment and the system’s ability to adapt to different organizational contexts confirmed the effectiveness and scalability of the implemented solution.